Arrhythmia & Electrophysiology Review Volume 8 • Issue 3 • Autumn 2019
Volume 8 • Issue 3 • Autumn 2019
Physicians Writing Fiction Douglas P Zipes
British Heart Rhythm Society Clinical Practice Guidelines on the Management of Patients Developing QT Prolongation on Antipsychotic Medication Pier D Lambiase, Joseph Paul de Bono, Richard J Schilling, Martin Lowe, Andrew Turley, Alistair Slade, Jason Collinson, Kim Rajappan, Stuart Harris, Jason Collison, Viki Carpenter, Holly Daw, Angela Hall, Eleri Roberts, Shona Holding, John Paisey, Mark Sopher, Ian Wright, Benedict Wiles, Francis Murgatroyd and David Taylor
Relationship Between Obstructive Sleep Apnoea and AF Ghanshyam Shantha, Frank Pelosi and Fred Morady
Preventive Ventricular Tachycardia Ablation in Patients with Ischaemic Cardiomyopathy: Meta-analysis of Randomised Trials Roland R Tilz, Charlotte Eitel, Evgeny Lyan, Kivanc Yalin, Spyridon Liosis, Julia Vogler, Ben Brueggemann, Ingo Eitel, Christian Heeger, Ahmed AlTurki and Riccardo Proietti
Left ventricular scar map
8.0e+02 750 700 650 600 550 500 450 400 350 300 250 200 150 100
Leadless pacemaker in the right ventricle
3D multiplanar reconstruction
Severe septal-lateral wall dyssynchrony and impaired longitudinal strain
ISSN – 2050-3369
Lifelong Learning for Cardiovascular Professionals
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Volume 8 • Issue 3 • Autumn 2019
www.AERjournal.com Official journal of
Editor-in-Chief Demosthenes G Katritsis Hygeia Hospital, Athens
Section Editor – Arrhythmia Mechanisms / Basic Science
Section Editor – Clinical Electrophysiology and Ablation
Section Editor – Implantable Devices
Lankenau Institute for Medical Research, Pennsylvania
Heart Rhythm Institute, University of Oklahoma Health Sciences Center, Oklahoma City
St Thomas’ Hospital and King’s College London, London
Yale University School of Medicine, New Haven
Mayo Clinic, St Mary’s Campus, Rochester, Minnesota
University of Bordeaux, CHU Bordeaux
Uppsala University, Uppsala
Northshore University Hospital, New York
Klinikum Coburg, II Med Klinik, Coburg
Imperial College Healthcare NHS Trust, London
Heart Rhythm Institute, University of Oklahoma Health Sciences Center, Oklahoma City
University of Cambridge, Cambridge
Johns Hopkins Medicine, Baltimore
Fondazione Cardiocentro Ticino, Lugano
Editorial Board Joseph G Akar
Hospital Sant Joan de Déu, University of Barcelona, Barcelona
Pedro Brugada University of Brussels, UZ-Brussel-VUB
Alfred Buxton Beth Israel Deaconess Medical Center, Boston
David J Callans University of Pennsylvania, Philadelphia
A John Camm St George’s University of London, London
Riccardo Cappato IRCCS Humanitas Research Hospital, Rozzano, Milan
Shih-Ann Chen National Yang Ming University School of Medicine and Taipei Veterans General Hospital, Taipei
Carlo Pappone IRCCS Policlinico San Donato, Milan
Institute for Clinical and Experimental Medicine, Prague
ALFA – Alliance to Fight Atrial Fibrillation, Venice-Mestre
Hospital Privado del Sur, Bahia Blanca, Argentina
Barts Heart Centre, St Bartholomew’s Hospital, London
Karl-Heinz Kuck Asklepios Klinik St Georg, Hamburg
Pier Lambiase Institute of Cardiovascular Science, University College London, and Barts Heart Centre, London
Samuel Lévy Aix-Marseille University, Marseille
Bordeaux University Hospital, Electrophysiology and Heart Modelling Institute, Bordeaux
Richard Schilling Barts Health NHS Trust, London
Afzal Sohaib Imperial College London, London
William Stevenson Vanderbilt School of Medicine, Nashville, Tennessee
Karolinska University, Stockholm
Gregory YH Lip University of Liverpool, Liverpool
National Heart and Lung Institute, Imperial College London, London
Maastricht University Medical Center, Maastricht
University of Pennsylvania Health System, Philadelphia
Virginia Commonwealth University, Richmond
Indiana University School of Medicine, Indiana
Royal Brompton & Harefield NHS Foundation Trust, London
Cardiovascular Center, University of Michigan
Antwerp University and University Hospital, Antwerp
Sanjiv M Narayan
Gerhard Hindricks University of Leipzig, Leipzig
Carsten W Israel JW Goethe University, Frankfurt
Cover image © AdobeStock
Stanford University Medical Center, California
Heraklion University Hospital, Heraklion
Marc A Vos University Medical Center Utrecht, Utrecht
Hein Wellens University of Maastricht, Maastricht
Katja Zeppenfeld Leiden University Medical Center, Leiden
Douglas P Zipes
Texas Cardiac Arrhythmia Institute, St David’s Medical Center, Austin, Texas
Krannert Institute of Cardiology, Indiana University School of Medicine, Indianapolis
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Published by Radcliffe Cardiology. All information obtained by Radcliffe Cardiology and each of the contributors from various sources is as current and accurate as possible. However, due to human or mechanical errors, Radcliffe Cardiology and the contributors cannot guarantee the accuracy, adequacy or completeness of any information, and cannot be held responsible for any errors or omissions, or for the results obtained from the use thereof. Published content is for information purposes only and is not a substitute for professional medical advice. Where views and opinions are expressed, they are those of the author(s) and do not necessarily reflect or represent the views and opinions of Radcliffe Cardiology. Radcliffe Cardiology, Unit F, First Floor, Bourne End Business Park, Cores End Road, Bourne End, Buckinghamshire SL8 5AS, UK © 2019 All rights reserved ISSN: 2050-3369 • eISSN: 2050–3377
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Established: October 2012 | Frequency: Quarterly | Current issue: Autumn 2019
Aims and Scope
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• Arrhythmia & Electrophysiology Review aims to assist time-pressured physicians to stay abreast of key advances and opinion in heart failure. • Arrhythmia & Electrophysiology Review comprises balanced and comprehensive articles written by leading authorities, addressing the most pertinent developments in the field. • Arrhythmia & Electrophysiology Review provides comprehensive updates on a range of salient issues to support physicians in continuously developing their knowledge and effectiveness in day-to-day clinical practice.
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Lifelong Learning for Cardiovascular Professionals
© RADCLIFFE CARDIOLOGY 2019
Foreword Journal Impact Factor: Widely Used, Misused and Abused
Demosthenes G Katritsis DOI: https://doi.org/10.15420/aer.2019.8.3.FO1
Guest Editorial Physicians Writing Fiction
Douglas P Zipes DOI: https://doi.org/10.15420/aer.2019.8.3.ED1
Guidelines British Heart Rhythm Society Clinical Practice Guidelines on the Management of Patients Developing QT Prolongation on Antipsychotic Medication
Pier D Lambiase, Joseph Paul de Bono, Richard J Schilling, Martin Lowe, Andrew Turley, Alistair Slade, Jason Collinson, Kim Rajappan, Stuart Harris, Jason Collison, Viki Carpenter, Holly Daw, Angela Hall, Eleri Roberts, Shona Holding, John Paisey, Mark Sopher, Ian Wright, Benedict Wiles, Francis Murgatroyd and David Taylor DOI: https://doi.org/10.15420/aer.2019.8.3.G1
Clinical Arrhythmias The Increment of Short-term Variability of Repolarisation Determines the Severity of the Imminent Arrhythmic Outcome
Agnieszka Smoczynska, Henriëtte DM Beekman and Marc A Vos DOI: https://doi.org/10.15420/aer.2019.16.2
Electrophysiology and Ablation Preventive Ventricular Tachycardia Ablation in Patients with Ischaemic Cardiomyopathy: Meta-analysis of Randomised Trials
Roland R Tilz, Charlotte Eitel, Evgeny Lyan, Kivanc Yalin, Spyridon Liosis, Julia Vogler, Ben Brueggemann, Ingo Eitel, Christian Heeger, Ahmed AlTurki and Riccardo Proietti DOI: https://doi.org/10.15420/aer.2019.31.3
Relationship Between Obstructive Sleep Apnoea and AF
Ghanshyam Shantha, Frank Pelosi and Fred Morady DOI: https://doi.org/10.15420/aer.2019.35.2
Individualised Approaches for Catheter Ablation of AF: Patient Selection and Procedural Endpoints
Nicolas Johner Mehdi Namdar and Dipen C Shah DOI: https://doi.org/10.15420/aer.2019.33.2
The Role of Cardiac MRI in the Management of Ventricular Arrhythmias in Ischaemic and Non-ischaemic Dilated Cardiomyopathy
Tom Nelson, Pankaj Garg, Richard H Clayton and Justin Lee DOI: https://doi.org/10.15420/aer.2019.5.1
Mapping and Imaging in Non-paroxysmal AF
Michael Ghannam and Hakan Oral DOI: https://doi.org/10.15420/aer.2019.18.1
Understanding AF Mechanisms Through Computational Modelling and Simulations
Konstantinos N Aronis, Rheeda L Ali, Jialiu A Liang, Shijie Zhou and Natalia A Trayanova DOI: https://doi.org/10.15420/aer.2019.28.2
Drugs and Devices Improving Cardiac Resynchronisation Therapy
George Thomas, Jiwon Kim and Bruce B Lerman DOI: https://doi.org/10.15420/aer.2018.62.3
Update in Cardiac Pacing
Nishant Verma and Bradley P Knight DOI: https://doi.org/10.15420/aer.2019.15.3
© RADCLIFFE CARDIOLOGY 2019
The British Heart Rhythm Society is proud to partner AER in its effort to inform, educate and support clinicians with an interest in heart rhythm management.
We would encourage readers to become members. Membership is only £60 a year (£40 for nurse or trainee members). By joining the BHRS you are both supporting and being a member of the British heart rhythm community. You also have the following benefits:
Access to BHRS members areas on the website. This contains:
• Educational material like our monthly ECG and electrograms cases • Business cases, job descriptions and standard operating procedures – why do the work yourself when another member has already done it for you? • Slide presentations
Representation at the top level of UK and European health care.
The BHRS has advises the UK government on health care issues, training and policy related to heart rhythm care. We have a seat on the European Heart Rhythm Association national society working groups and the British Cardiovascular Society • Cardiac physiologist influence and representation at the Academy of Healthcare Science (AHCS), National School of Healthcare Science (NSHCS) and Improving Quality in Physiological Services (IQIPS) professional boards • Gaining and maintaining BHRS certification in Devices, Electrophysiology and Nursing/Clinical certification • Discounted rates for Heart Rhythm Congress (Membership fee is taken off the registration fee to attend) • A chance to influence BHRS by voting in council elections, and standing for office (council minutes are published openly on our website) • Access and support from a multidisciplinary council with the ability to raise concerns and voice opinion regardless of profession. We also regularly offer advice to our members who have professional concerns or challenges
To become a member go to our website http://www.bhrs.com/how-to-join
Journal Impact Factor: Widely Used, Misused and Abused
ince the inauguration of Arrhythmia & Electrophysiology Review in 2012, one of the most consistent, and persistent, concerns expressed both by authors and reviewers has been the issue of our impact factor and when should we get one. This is a legitimate question: publishing in a citable journal of considerable impact factor justifies the efforts of the authors and secures credible publicity of their scientific work. Journal impact factor (JIF) is a citation metric designed in 1955 by Eugene Garfield, the founder of the Institute for Scientific Information, to help librarians prioritise their purchases of the most important journals.1 It was inspired by Shepard’s Citations,
an American citation index of legal resources that began in 1873, and which allows lawyers to locate the publications citing a particular case and legal decisions influenced by a case. The idea of quantifying impact by counting citations led to the creation of the prestigious journal rankings, which have been recorded annually in the Science Citation Index since 1961. JIFs are calculated by Clarivate Analytics and published annually in Journal Citation Reports and measure the average impact of articles published in a journal with a citation window of 1 year. The formula for calculating JIF is the total number of citations in a year, divided by the total number of ‘citable’ articles published by the journal during the 2 preceding years. To obtain a JIF, a journal must be accepted by Clarivate Analytics’ citation databases, such as the Science Citation Index Expanded, and remain in the system for at least 3 years.2 JIF has several shortcomings and limitations. Editorials and letters are non-citable items and are excluded from the JIF denominator, but these items, particularly in modern biomedicine, contain long lists of references, affecting the JIF calculations in many ways. In addition, it restricts citations of recent articles, because JIF only considers the first 2 years after a study is published. That is, if a journal has articles cited later, they will not affect the impact. Citations of journals not indexed in the Web of Science are not considered, regardless of their potential importance. Last, but not least, there has been a lack of transparency in the JIF calculations, partly due to the lack of open access to the citations tracked by the databases used by Thomson Reuters (the previous owner).3 All these have damaged the reputation of the JIF as a reliable and reproducible scientometric tool. There has been a growing unease within the scientific community, among journal publishers and within funding agencies that the widespread use of JIFs to measure the quality of research is detrimental for science itself.3–5 The San Francisco Declaration on Research Assessment, initiated by the American Society for Cell Biology together with editors and publishers, calls for moving away from using JIFs to evaluate individual scientists or research groups and developing more reliable ways to measure the quality and impact of research. One such method is the new Relative Citation Ratio that is now being used by the US National Institutes of Health.4 In a recent, devastating review, Ioannidis and Thombs pronounced JIF as “without a doubt the most widely used, misused and abused bibliometric index in academic science”, adding that “JIF is a highly flawed, easily gameable metric.”5 They recommended its replacement with Median Citations per Item indicators calculated separately for articles, reviews and other types of papers (Tables 1–3). However, one should keep in mind that all bibliometric numbers are only a proxy of research quality, which measure one part of quality, namely impact or resonance. Despite its many limitations, JIF is still a credible marker and no serious scientific journal can thrive without it. Approximately 11,000 academic journals are currently listed in Journal Citation Reports and JIF.
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Foreword Table 1. Information Readily Available in Journal Citation Reports that Can Help Obtain Insights into Journal Impact Factor Inflation Journal
Papers among top 10 cited
4 (1 review, 3 original)
No such papers
New England Journal of Medicine
3 (3 industry trials)
1 (1 sepsis definition)
British Medical Journal
1 (1 reporting guideline)
Journal of Clinical Epidemiology
1 (1 method)
No such papers
European Heart Journal
6 (6 expert-based guidelines)
Revista Espanola de Cardiologia
No such papers
European Journal of Heart Failure
1 (1 expert-based guideline)
2 (2 expert-based guidelines)
JIF is given in Journal Citation Reports with three decimals, but this excessive accuracy is inappropriate, given the numbers used in the calculation, therefore only one decimal is given in this table. Note that in the theoretical situation where there are no journal self-citations, a journal only publishes articles (no reviews, and no items such as editorials and letters that do not contribute to the denominator of the JIF calculation), and the distribution of citations per article is normal (there are no papers with skewed extremely high citations), then JIF, JIF without self-citations and MCA would be identical. EJCI = European Journal of Clinical Investigation; JIF = journal impact factor 2017 (based on citations received in 2017 for articles published in 2015â€“2016); MCA = median citations per article. Source: Ioannidis and Thombs.5 Reproduced with permission from Wiley.
Table 2. Key Measures that Capture Mechanisms of Journal Impact Factor 2017 Inflations Journal
Skewness and Nonarticle Inflation
New England Journal of Medicine
British Medical Journal
Journal of Clinical Epidemiology
European Heart Journal
Revista Espanola de Cardiologia
European Journal of Heart Failure
Self-citing boost: percentage increase in JIF due to journal self-citations; skewness and nonarticle inflation: percentage inflation of JIF over the median citations per article; expert-based blockbuster: clinical guidelines and position statements and disease definition papers among the papers that received >10 JIF citations. JIF = journal impact factor 2017. Source: Ioannidis and Thombs.5 Reproduced with permission from Wiley.
ARRHYTHMIA & ELECTROPHYSIOLOGY REVIEW
Impact Factor: Widely Used, Misused and Abused Table 3. Main Points Made by Ioannidis and Thombs JIF is widely misused and abused, and editors are under pressure to game this metric. Journals may inflate their JIF by publishing papers that get cited without counting in the denominator of the JIF calculation; increasing journal self‐citations; publishing more reviews rather than regular articles; and publishing papers with questionable value which are perceived as standard, massively used references across large communities, for example expert‐based guidelines. Routinely available information from JCR can be used to spot the potential for spuriously inflated JIFs. One can calculate three measures of JIF inflation: self‐citing boost, skewness and nonarticle inflation and expert‐based blockbusters. Examples are provided for 12 journals. Evidence of major JIF inflation based on any of these three measures should lead to scrutiny of editorial practices. Better sensitisation to JIF inflation practices and the use of penalties (e.g. suppression of JIF of journals with extreme inflation tricks), where appropriate, may help curtail JIF misinterpretations and manipulations. Given that JIF is so well‐documented to be flawed, JCR should stop reporting it and replace it by the more appropriate median citations per article, median citations per review and median citations per other type of article, also excluding journal self‐citations. JCR = Journal Citation Reports; JIF = Journal Impact Factor 2017 (based on citations received in 2017 for articles published in 2015–2016). Source: Ioannidis and Thombs.5 Reproduced with permission from Wiley.
A 2003 survey of physicians specialising in internal medicine in the US provided evidence that despite its shortcomings, impact factor may be a valid indicator of quality for general medical journals, as judged by both practitioners and researchers in internal medicine.6 Until it dies or is killed, therefore, JIF is here to stay. Arrhythmia & Electrophysiology Review is taking its final steps towards this goal, and we hope for a respectable IF in 2020. Having announced that, we do know that our reputation is better served by the quality of the articles published and the high esteem in which senior and junior members of the electrophysiology community hold the journal. Demosthenes G Katritsis Editor-in-chief, Arrhythmia & Electrophysiology Review Hygeia Hospital, Athens, Greece
Garfield E. The history and meaning of the journal impact factor. JAMA 2006;295:90–3. https://doi. org/10.1001/jama.295.1.90; PMID: 16391221. Gasparyan AY, Nurmashev B, Yessirkepov M, et al. The Journal Impact Factor: moving toward an alternative and combined scientometric approach. J Korean Med Sci 2017;32:173–9. https://doi.org/10.3346/ jkms.2017.32.2.173; PMid:28049225. Mugnaini R. The impact factor: its popularity and
impacts, and the need to preserve the scientific knowledge generation process. Rev Esc Enferm USP. 2016;50:722–3. https://doi.org/10.1590/s0080623420160000600002; PMID: 27982388. Bornmann L, Marx W. The journal impact factor and alternative metrics: A variety of bibliometric measures has been developed to supplant the impact factor to better assess the impact of individual research papers. EMBO Rep 2016;17:1094–7.
https://doi.org/10.15252/embr.201642823; PMID: 27354417. Ioannidis JPA, Thombs BD. A user’s guide to inflated and manipulated impact factors. Eur J Clin Invest 2019:e13151. https://doi.org/10.1111/eci.13151; PMID: 31206647. Saha S, Saint S, Christakis DA. Impact factor: a valid measure of journal quality? J Med Libr Assoc 2003;91:42–6. PMID: 12572533.
ARRHYTHMIA & ELECTROPHYSIOLOGY REVIEW
Physicians Writing Fiction Douglas P Zipes Indiana University School of Medicine Indianapolis, IN, US
Disclosure: The author has no conflicts of interest to declare. Received: 21 March 2019 Accepted: 21 March 2019 Citation: Arrhythmia & Electrophysiology Review 2019;8(3):156–60. DOI: https://doi.org/10.15420/aer.2019.8.3.ED1 Correspondence: Douglas P Zipes, Indiana University School of Medicine, 340W 10th St #6200, Indianapolis, IN, 46202, US. E: firstname.lastname@example.org Open Access: This work is open access under the CC-BY-NC 4.0 License which allows users to copy, redistribute and make derivative works for non-commercial purposes, provided the original work is cited correctly.
riting fiction is simply telling a story. Humans have been storytelling for millennia, from recounting tales while sitting around a fire to modern day blogging and stories told on social media. The history of medicine is rife with outstanding storytellers, including W Somerset Maugham, Arthur Conan Doyle, Oliver Wendell Holmes, Anton Chekhov and William Carlos Williams, along with more contemporary authors such as Robin Cook, Michael Crichton, Abraham Verghese, Khaled Hosseini and others. Chekhov captured the appeal of writing when he wrote: “Medicine is my lawful wife and literature my mistress; when I get tired of one, I spend the night with the other.” Physicians are trained to be professional storytellers from our first days as medical students when we present a patient to a staff physician on rounds or write a patient summary for the chart or for a referral. We are privileged to be present when people are literally or figuratively undressed during momentous occasions such as childbirth, illness and death. We are taught empathy, while retaining objectivity when observing life’s great dramas. These storytelling skills serve physicians who write fiction well.
Science Writing Versus Fiction Accuracy and clarity are two virtues of medical writing that are drilled into us, whether it is describing a patient’s illness or reporting the results of a study. Such reporting contains elements of novel writing. For example, charting a patient’s history begins with a chief complaint, which is like the opening paragraphs; history, physical exam and lab data are like the middle of a novel; and diagnosis and treatment are the final chapters. A scientific article is generally more rigidly divided into an introduction, methods, results and discussion in which the author describes what he is about to present, states the tools used, relates the results and recapitulates the entire experience. The physician/science writer remains totally impersonal, while the fiction writer can become intensely involved in the story through his past experiences. Fiction writing, in contrast to science writing, involves using an eyedropper to dispense a fact here, another there, and a third 10 pages later, taking the reader through unexpected twists and turns to reach
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the final outcome. It is an elaborate lie used to tell the truth to explain and explore life. Fiction allows for the creative freedom to invent your own universe, to imagine a world without boundaries, to conceive characters you love and make into heroes, or characters you hate that you can kill if you so choose. It is exhilarating and a world apart from writing tightly regulated science. Every novel has a purpose or premise, such as ‘ruthless ambition destroys itself’, and is comprised of three important elements that I call the 3Cs: character, conflict and conclusion. Each C must be fully developed and instead of scientific clarity, fiction authors strive for suspense; instead of medical accuracy, we seek drama; and instead of resolution, we pursue conflict until the end. Another, more practical, distinction is that the fiction author retains copyright ownership, in contrast to publishing in a medical journal or textbook where the author relinquishes the copyright to the publisher. A novel can be any length, from hundreds of thousands of words such as Tolstoy’s War and Peace to as short as six: ‘For sale; baby shoes; never worn,’ which has been attributed to Ernest Hemingway. Ninety per cent of fiction writing is revision. When Oscar Wilde was asked how he spent his morning, he answered: “I spent it revising a poem.” “What changes did you make?” “I took out a comma.” “What did you do in the afternoon?” “I put it back.” Good fiction emphasises ‘showing’ rather than ‘telling’; adverbs such as quickly, angrily and briefly are banished, replaced by active descriptions depicting each state or condition. The active voice supplants the passive, and point of view must be consistent. However, all rules are made to be broken as long as the transgression propels the story forward.
First Attempt at Writing a Novel My first attempt at novel writing began long before computers were commonplace and I had not yet learned to type. At work I had a secretary and dictated everything I needed to be written. After reading a bestselling medical thriller, I decided to try and write one.
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Physicians Writing Fiction Figure 1: The Black Widows
Figure 2: El Deir, Petra, Jordan
Figure 3: 900 Steps to El Deir, Petra, Jordan
I began dictating my novel, bringing the tapes home to my wife for transcription. As she typed, she rearranged sentences and substituted words and gradually became my coauthor. Therein lay the danger. We viewed scenes and characters differently. She would advise: “No woman would say that while making love,” and I would retort: “No man is going to act like that in a fight.” We held countless conversations over dinner. My wife called them discussions; I called them arguments. She liked them; I didn’t. The only way we could agree on a scene or a character was to compromise, a fatal tactic that blunted the sharp edges of the story. Scenes and characters had to be negotiated to please us both and we lost the vibrancy of the tale. In the end, we relegated this first team effort to a drawer where 110,000 words slumber peacefully, perhaps awaiting resuscitation during the throes of a long, cold Indiana winter while we are sitting alongside a blazing, toasty fire.
The Black Widows The premise of my first completed novel The Black Widows, published in 2011, is based on the concept that evil begets its own downfall (Figure 1).1 Two elderly widows control a worldwide terrorist operation that seeks to overthrow Western democracy. Foreword Reviews chose it as a Book of the Year finalist. The prologue of the novel sets forth several precepts emphasised above. “Wahad, code name for ‘the first’, slipped into the building, past the guard… The first kill was always the toughest… Wahad slid a finger through the trigger guard, aimed and slowly squeezed… and began the surgical procedure. Number one was completed. Only 999 to go.” The reader doesn’t know who Wahad is, and I carefully avoided pronouns to identify Wahad’s sex. ‘The first kill’ – of whom and why? Followed by ‘a surgical procedure’ – what? Why? And ‘999 to go’ – oh my God! A rewarding experience for an author is to weave one’s own personal experiences into the novel. I had toured Petra and Jordan around the
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time I was writing The Black Widows, so I ended the novel with the hero chasing the leader of the terrorists to Petra. The terrorists have used El Deir to create a hidden laboratory in which they fashioned weapons of mass destruction (Figure 2). The leader of the Black Widows has captured the heroine and is planning to stone her to death on the plateau in front of the building. Petra has 900 steps (Figure 3), leading to El Deir. Here is an excerpt from one of the final chapters: “A nine-hundred-step run is agonizing… the stairs were uneven and twisted in, out, and around the mountainside, and the sun was hot… my calves bunched into knots, my breath was raspy and rapid, and my heart rate too fast to count. My body told me to stop and rest while my head said, Keep going—she may still be alive. “I won’t let her die today! “Finally I reached the last step, wheezing like an asthmatic. It opened onto the plateau in front of El Deir. A circle of people stood around a white robed statue, buried to the shoulders in a hole. The sheet was tied with a string at the top like a sack of laundry… “He hurled the rock, hitting its target. A red stain spread across the top of the sheet… she… lost consciousness.”
Guest Editorial Figure 4: Ripples in Opperman’s Pond
Figure 5: Not Just a Game
Ripples in Opperman’s Pond
Figure 6 Dock to ‘Hitler’s House’, Estancia Inalco
I used my second novel Ripples in Opperman’s Pond to explore genotype/phenotype mismatch by having identical twin brothers – both cardiologists – exhibit totally different personalities (Figure 4).2 I also tried to create an opening sentence that readers would remember, as they remember “It was the best of times, it was the worst of times” from A Tale of Two Cities by Charles Dickens, or “Call me Ishmael” from Moby Dick by Herman Melville. In Ripples, one twin says of the other, “We were identical, Dorian and I, but not at all alike.” The premise, that ruthless ambition breeds self-destruction, stems from two trials at which I testified as an expert witness. Reggie Lewis (b. 1965, d. 1993), a Boston Celtics basketball star, suffered sudden cardiac death while being evaluated for syncope by a cardiologist at a Boston hospital. His wife sued the cardiologist for malpractice and I was asked to testify (successfully) in his defence. In the second trial, I was a plaintiff expert witness testifying against Merck Pharmaceuticals in 2006, alleging their drug Vioxx (rofecoxib) caused a heart attack in a patient. The jury awarded $51 million to the plaintiff. Here is an excerpt from the first chapter of Ripples: ”Randy hit his patented fall-away jumper as the game-ending buzzer sounded… guaranteeing the Pacers a play-off berth. Not planned was Randy’s mid-air collision with the Boston guard… Unbalanced, Randy landed on a bowed-out ankle, fragile ligaments suddenly supporting 225 crashing pounds. Randy’s scream drowned the papery whisper of the ball’s swish as he fell.” One brother’s newly discovered anti-inflammatory drug, Redex, is used to treat Randy. Predictably, Randy suffers sudden cardiac death as the novel unfolds and his twin is sued for malpractice.
Not Just a Game The premise of my third novel Not Just a Game is that good overcomes evil (Figure 5).3 I wrote it to focus attention on the growing menace of far-right extremism, the rise in anti-Semitism, along with the resurgence of Nazism.
I created three generations of a single family from which the father, son and granddaughter each participate in an important summer Olympics: the father in the 1936 Olympic Games in Berlin hosted by Adolf Hitler; the son in the 1972 Olympic Games in Munich during which the Black September Palestinian terrorist group murdered 11 Israeli athletes; and the granddaughter in the 2016 Olympic Games in Rio de Janeiro when, in this fictional account, the Nazi Fourth Reich raises its virulent head to stage a re-enactment of Kristallnacht. The original Kristallnacht took place in Nazi Germany, 9–10 November 1938, and was the beginning of the Holocaust. The story draws from the conspiracy theory that Hitler survived World War II and fled to South America where he is said to have lived in a house in La Angostura, Argentina, which I visited last year (Figures 6 and 7). I have imagined him there, sowing the seeds for the Fourth Reich. The granddaughter in my novel, Kirsten, becomes the last hope to subdue and transform the uprising through a series of interconnected events. She speaks to the citizens of Rio on national television after the first
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Physicians Writing Fiction Figure 7: The Author at ‘Hitler’s House’, Estancia Inalco
Figure 8: Damn the Naysayers
night of riots to try and prevent an even more violent second night. The teleprompter has just quit and she is winging it. “She panicked. What should I do? Words spoken more than two thousand years ago came to her, reassured and calmed her. “A famous rabbi named Hillel once said, ‘If I am not for myself, who is for me? And being for my own self, what am I? And if not now, when?’ “So, I have to help, now, in any way I can, and try to make you realize what you are doing and the horrible consequences of your actions. “All of this is as new to me as it is to you. I didn’t ask for the role… I must try to prevent a second night of Kristallnacht.”’
Figure 9: Author Lecturing to Refuseniks in a Moscow Apartment, 1982
A Failure to Warn In A Failure to Warn (tentative title), I used for inspiration my 9-year battle as a plaintiff expert witness against TASER International.4 I argued in multiple sudden death lawsuits that their weapon could cause cardiac arrest. For the novel, I fabricated the story of a family wiped out by an electronic control weapon manufactured by the fictitious Electric Gun Company. The last survivor beseeches her lawyer brother: “Just promise me you’ll go after the people responsible for killing my men and bring them to justice. If I know you’ll do that, I’ll die and can rest in peace.”
Damn the Naysayers I wrote my memoir Damn the Naysayers to capture for myself, my family, and my friends, personal highlights over the past 80 years that have illuminated and affected my life (Figure 8).5 I am indebted to my friend and colleague Eugene Braunwald for writing the foreword. Transitioning from writing fiction to writing a memoir is an interesting challenge because of the tricks one’s memory plays, particularly recalling events that happened a long time ago (though sometimes incidents 50 years old remain more vivid than last night’s dinner). Just as my personal memories influenced my fiction, all memoirs inevitably contain some fiction. But a true memoir must be told from the author’s memory, however flawed, and from the subjective recollection of
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Guest Editorial Figure 10: First Chapter of Damn the Naysayers Published in Nov/Dec 2017 Issue of The Saturday Evening Post
of the Cold War (Figure 9). The Saturday Evening Post published that first chapter in its entirety (Figure 10). “Are you Douglas Zipes, the heart specialist from Indiana?” the deep voice over the phone asked. “I am a refusenik. We are in a jail without bars,” he said. “I’m sorry for that,” I said. “But why are you calling me?” “How brave are you?” he asked. “We need someone with courage. Two years ago, we started the Sunday Seminars. When a major scientific meeting was going to be held in Moscow, one of us would invite a visiting scientist to give us a private lecture – on Sundays. One evening during the lecture, the KGB burst in. The owner of the apartment was arrested and exiled for 3 years to Kazakhstan. That ended the Sunday Seminars… you could be trusted.” “Trusted? To do what?” I asked, my voice tremulous. “To be our first scientist to restart our Sunday Seminars.”
Conclusion I emphasised recently that life’s journey is more important than the finish.6 This is particularly applicable to my late-blooming career in fiction. My transition from cardiologist to novelist has been like travelling from Who’s Who to Who’s He? events, shaded by the author’s personal point of view, by who he is and the facts as he remembers them. I began the memoir with one of the most terrifying yet rewarding events in my entire life: lecturing to refuseniks in Moscow at the height
1. 2. 3. 4. 5. 6.
Zipes Zipes Zipes Zipes Zipes Zipes
I admire the successes of the writers featured above, much as an intern would admire the skills of staff physicians. But I’m still young, and if I work hard at my writing, who knows what I can accomplish in the next 80 years. With a bit of luck, maybe I will join that list.
D. The Black Widows. Bloomington IN: iUniverse, 2011. D. Ripples in Opperman’s Pond. Bloomington IN: iUniverse, 2013. D. Not Just a Game. Bloomington IN: iUniverse, 2016. D. A Failure to Warn. Bloomington IN: iUniverse, in press. D. Damn the Naysayers: A Doctor’s Memoir.Bloomington IN: iUniverse, 2018. DP. HRS 40th anniversary viewpoints: The journey is more important than the finish. Heart Rhythm 2019;16:320–2. https://doi.org/10.1016/j.hrthm.2018.10.019; PMID: 30712647.
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British Heart Rhythm Society Clinical Practice Guidelines on the Management of Patients Developing QT Prolongation on Antipsychotic Medication Pier D Lambiase, 1 Joseph Paul de Bono, 2 Richard J Schilling, 3 Martin Lowe, 3 Andrew Turley, 4 Alistair Slade, 5 Jason Collinson, 6 Kim Rajappan, 7 Stuart Harris, 8 Jason Collison, 9 Viki Carpenter, 10 Holly Daw, 3 Angela Hall, 11 Eleri Roberts, 12 Shona Holding, 13 John Paisey, 14 Mark Sopher, 15 Ian Wright, 16 Benedict Wiles, 17 Francis Murgatroyd 18 and David Taylor 19 1. UCL and Barts Heart Centre, London, UK; 2. University Hospitals Birmingham, UK; 3. Barts Heart Centre, London, UK; 4. James Cook University Hospital, Middlesbrough, UK; 4. Royal Cornwall Hospital, Truro, UK; 6. Basildon Hospital, Essex, UK; 7. John Radcliffe Hospital, Oxford, UK; 8. Essex Heart Centre, UK; 9. Basildon Hospital, Essex; 10. Addenbrooke’s Hospital, Cambridge, UK; 11. Jersey General Hospital, Jersey; 12. Manchester University NHS Foundation Trust, Manchester, UK; 13. Westcliffe Medical Group Practice/Community Cardiology Service, Shipley, UK; 14. University Hospital Southampton, UK; 15. Royal Bournemouth Hospital, UK; 16. Hammersmith Hospital, London, UK; 17. Southampton General Hospital, Southampton, UK; 18. King’s College Hospital, London, UK; 19. King’s Health Partners, Guy’s and St Thomas’ NHS Foundation Trust, London, UK
Abstract The British Heart Rhythm Society’s Clinical Practice Guidelines on the Management of Patients Developing QT Prolongation on Antipsychotic Medication are written for heart rhythm consultants, primary care physicians, specialist registrars, nurses and physiologists who may be requested to review ECGs or advise on cases where antipsychotic-induced QT prolongation is suspected or proven. The guidance is adapted from the latest Maudsley Prescribing Guidelines in Psychiatry, published in 2018.
Keywords Antipsychotic medication, schizophrenia, QT prolongation, guidelines, psychiatry Disclosure: PDL is supported by UCLH Biomedicine NIHR. All other authors have no conflicts of interest to declare. Received: 13 March 2019 Accepted: 3 April 2019 Citation: Arrhythmia & Electrophysiology Review 2019;8(3):161–5. DOI: https://doi.org/10.15420/aer.2019.8.3.G1 Correspondence: Pier Lambiase, UCL Institute of Cardiovascular Science & Barts Heart Centre, Room 3.20, Rayne Institute, 5 University St, London WC1E 6JF, UK. E: email@example.com Open Access: This work is open access under the CC-BY-NC 4.0 License which allows users to copy, redistribute and make derivative works for non-commercial purposes, provided the original work is cited correctly.
Heart rhythm consultants, primary care physicians, specialist registrars, nurses and physiologists may be requested to review ECGs or advise on cases where antipsychotic-induced QT prolongation is suspected or evident. The British Heart Rhythm Society has issued the Clinical Practice Guidelines on the Management of Patients Developing QT Prolongation on Antipsychotic Medication to support them. The guidance is adapted from the most recent Maudsley Prescribing Guidelines in Psychiatry, issued in 2018.1
avoided.6 Indeed, mortality is 40% lower in patients with schizophrenia who use antipsychotics than those who do not.
When considering the risk of death with schizophrenia, it is important to recognise that this condition carries a mortality from its psychiatric effects. Nearly one-third of the excess mortality in schizophrenia is attributable to a significantly higher risk of suicide – 5% risk over the patient’s lifetime – with an additional 12% due to accidental death. Antipsychotics reduce adverse outcomes on mortality in the schizophrenia population.2–5
The mortality data also indicate that people with schizophrenia have higher fatality rates due to natural causes than the general population in the US. Cardiovascular, respiratory and metabolic disorders are 2–3 times more prevalent in people with schizophrenia.3,4 Some antipsychotics cause increased appetite and weight gain, and can also directly cause metabolic syndrome, dyslipidaemia, hypertension and insulin resistance, which probably leads to greater cardiac mortality rates. Unhealthy lifestyles, polypharmacy and suboptimal healthcare are all regarded as contributing factors to the risk of higher mortality.
The overall and cardiovascular mortality distributions follow a U-shaped curve in relation to antipsychotic dose with patients taking no medication and those taking the highest doses having the greatest mortality. This indicates that antipsychotics can protect patients against the consequences of schizophrenia, including suicide, at low and medium cumulative doses; compliance is critical and high doses should be
© RADCLIFFE CARDIOLOGY 2019
Long-acting injection (LAI) use is associated with an approximately 30% lower risk of death than oral use of the same medication, most probably because this ensures adherence and sustained drug action. Second-generation LAI antipsychotics and oral aripiprazole have the lowest mortality.7
Case-control studies have suggested that the use of most antipsychotics is associated with rate of sudden cardiac death that is two- to threetimes higher than in the general population –15 per 10,000 years of drug exposure.8–12 This is substantially lower than the mortality associated with uncontrolled psychosis.
Access at: www.AERjournal.com
Guidelines Figure 1. Tangent Method of QTc Measurement
Table 1. The Effect of Psychotropics on QTc
No effect Brexpiprazole (not available in UK) Cariprazine Lurasidone Low effect (only in overdose or <10 ms increases in QTc) Aripiprazole, amisulpride, asenapine, clozapine, flupentixol, fluphenazine perphenazine, prochlorperazine, olanzapine*, risperidone, sulpiride, loxapine, paliperidone, sulpiride Moderate effect (>10 ms QT prolongation at clinical doses) Amisulpride, chlorpromazine, levomepromazine, iloperidone, melperone, quetiapine, ziprasidone
High effect (>20 ms QTc prolongation at average clinical doses)
RR = 0.68 s
Pimozide, sertindole Any drug or combination of drugs used in doses exceeding recommended maximum
QT = 0.48 s
*Isolated cases of QTc prolongation demonstrated effect on IKr,other data suggest no effect on QT.19,22–24,34,40–42 Data on paliperidone are confusing (some suggest no effect) and recent data suggest aripiprazole prolongs QT to a small extent and is probably torsadogenic. Source: The Maudsley Prescribing Guidelines in Psychiatry.1 Reproduced with permission from Wiley Blackwell.
QTc = 0.48 s/√0.68 s = s0.58 s = 580 ms
Leads II or V5 may be measured. The tangent of the downslope of the T wave is taken to the baseline of the ECG and the QT interval measured in seconds between the Q wave and point where the tangent hits the baseline. It is important to use seconds as the measurement rather than milliseconds in the calculation if using a calculator or the QTc value will be incorrect. An average of 3–5 beats should be measured. A number of online or mobile apps can be employed to simplify the calculation of QTc, including https://www.mdcalc.com/corrected-qt-interval-qtc and https://www.omnicalculator.com/health/qtc.
Most antipsychotics (which are prescribed for schizophrenia and other psychoses, agitation, severe anxiety, mania and violent or dangerously impulsive behaviour) prolong the QT interval primarily through K channel blocking effects. QT prolongation increases the risk of torsades de pointes and sudden cardiac death, although the evidence base to suggest this is exponential is limited; there are exceptions in that some drugs prolong the QT interval but do not increase dispersion of repolarisation. However, more robust data indicate that, once the QTc interval is >500 ms, the risk of torsades de pointes in significantly increased.11
In summary, currently the only antipsychotics not associated with QT prolongation are: • lurasidone; • cariprazine; and • brexpiprazole. As a general rule, if the QTc is significantly prolonged (>500 ms) with no other reversible causes (see below) or any another antipsychotic, the patient should be switched to one of the three drugs above, but clinicians should be aware that the risk of relapse is increased to a small extent. The sole exception is clozapine – do not switch the patient from clozapine – the patient will relapse dramatically and quickly. Changing antipsychotic medications should be undertaken in consultation with the patient’s psychiatrist.
Accurate QT measurement may be challenging because the presence of U waves makes it difficult to determine the end of the T wave, but the QT interval can be easily measured using the ‘tangent method’ if automated measurement is not available or appears incorrect. Correct QT measurement is key to minimise the risks of over- or underestimating the QT interval and wrongly stratifying patient risk.
Action to be Taken According to QTc
To minimise inconsistencies, it is best to measure the tangent of the descending T wave to baseline in leads II or V5.13,14 This technique has been found to be the most reproducible among experts and non-experts alike (Figure 1).
QTc >440 ms (Men) or >470 ms (Women), but <500 ms
A summary of management according to QTc is shown in Figure 2.
QTc <440 ms (Men) or <470 ms (Women) No action required unless abnormal T-wave morphology – consider cardiac review if in doubt.
Consider reducing dose or switching to drug of lower effect; repeat ECG and consider cardiology review.
QTc >500 ms Many patients will have a resting tachycardia (>100/min) on medication so a correction for heart rate using the Fredericia formula (which uses the cube root of the RR interval) is recommended rather than Bazett’s as the latter will overestimate the QTc at higher heart rates. A simple online calculator to work out the QTc can be found at https://www. mdcalc.com/corrected-qt-interval-qtc. Regardless of which of these forumulae is used, detection of QTc >500 ms should prompt review.
Stop suspected causative drug(s) and switch to drug with a lower effect: immediate cardiology review is needed. If the patient has syncope or pre-syncope, immediate ECG monitoring for ventricular arrhythmias should be performed.
Abnormal T-wave Morphology Review treatment. Consider reducing dose or switching the patient to a lower risk antipsychotic, i.e. lurasidone, cariprazine or brexpiprazole.
Quantifying Risk Drugs are categorised here according to data available on their effects on QTc (Table 1).
Clozapine has a small effect on QTc. An implantable loop or closer 24-hour Holter recording may need to be considered if the QTc is
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BHRS Clinical Practice Guidelines Figure 2. Summary of Management According to QTc
Measure QTc using tangent method in leads II or V5 (correct with Fredericia formula if HR <60/>100 bpm)
Table 3. Non-psychotropics Associated with QT Prolongation Antibiotics Erythromycin
QTc <440 ms (man) <470 ms (woman) No action
QTc QTc>440 ms (man) or >470 ms (woman) but <500 ms • Consider reducing dose/switching to drug of lower effect • Repeat ECG and consider cardiology review
QTc>500 ms • Stop drug and switch to alternative agent • Cardiology review
Abnormal T wave morphology • Consider reducing dose/switch to lower risk antipsychotic
Clarithromycin Ampicillin Co-trimoxazole Pentamidine Some 4-quinolones affect QTc – see manufacturers’ literature Antimalarials
Review and correct cardiovascular risk factors
Chloroquine Mefloquine Quinine Antiarrhythmics Quinidine
Low-risk antipsychotics: lurasidone, cariprazine, brexpiprazole
Table 2. Physiological Risk Factors for QTc Prolongation and Arrhythmia
Long QT syndrome
Ischaemic heart disease
Left ventricular hypertrophy
Note: Beta 2 agonists and sympathomimetics may provoke torsades de pointes in patients with prolonged QTc.
Extreme physical exertion
Measure QTc in all patients prescribed antipsychotics: • on admission • before discharge and at yearly check-up
Extreme physical exertion
The effect of drugs on the QTc interval is usually plasma level-dependent. Drug interactions are therefore important, especially when metabolic inhibition results in increased plasma levels of the drug affecting QTc. Commonly used metabolic inhibitors include fluvoxamine, fluoxetine, paroxetine and valproate.
Extremes of age – children and elderly may be more susceptible to QT changes Stress or shock Female sex Note: Hypokalemia-related QTc prolongation is more commonly observed in acute psychotic admissions. Also, there are a number of physical and genetic factors which may not be discovered on routine examination, but which probably predispose patients to arrhythmia. Source: The Maudsley Prescribing Guidelines in Psychiatry.1 Reproduced with permission from Wiley Blackwell.
persistently prolonged over 500 ms to check that the patient is not developing ventricular arrhythmias. Cardiology review is necessary to specifically assess if the QTc measurement is accurate and there are no other factors leading to QT prolongation, including the cardiovascular risk factors or structural heart disease highlighted in Table 2.
Recommended Cardiology Assessment A cardiology/electrophysiology expert review should consist of an ECG, echocardiography, 24-hour Holter and electrolyte monitoring, and liver function tests. If there are features in the history or investigations suggestive of coronary artery disease, it is prudent to undertake a
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Source: Adapted from the Maudsley Prescribing Guidelines in Psychiatry.1 Used with permission from Wiley Blackwell.
CT coronary angiogram, as per National Institute for Health and Care Excellence guidance.15 Reversible causes of QT prolongation independent of the psychotropic drug effect should be assessed. These include other QT-prolonging drugs, agents that alter the metabolism of the antipsychotics to prolong half-life and electrolyte abnormalities (Table 3). If no reversible cause is identified apart form the antipsychotic drug, an alternative agent should be employed. Consideration should be given to inherited causes of QT prolongation; a significant proportion of drug-induced cases with a large degree of QT prolongation are associated with ion channel mutations.16 This can be evaluated by assessing persistent features of repolarisation abnormalities after stopping the drug for five half lives and examining
Guidelines the family history to see if there are any indications to suggest an inherited channelopathy, e.g. family history of sudden death at a young age, cot death, epilepsy, congenital deafness as well as ion channel mutation testing when clinically indicated (see the section in the Heart Rhythm Society guidelines on gene testing for inherited arrhythmias).17
therefore requires careful consideration. Prophylactic ICD implantation may be considered if the patient is developing non-sustained torsades de pointes or the QTcs are consistently very prolonged (e.g. >550 ms), or if a reversible cause such as stopping the antipsychotic is contraindicated due to the severity of the psychiatric condition.
Other cardiovascular risk factors such as smoking, obesity and impaired glucose tolerance should be considered, because they may present a much greater risk to patient morbidity and mortality than the uncertain outcome of QT changes. These should be managed accordingly, e.g. with statin treatment as per current guidelines.
Secondary prevention for out-of-hospital cardiac arrest or sustained haemodynamically compromising ventricular arrhythmias is appropriate if a reversible cause cannot be corrected and the patient is able to accept an ICD and comply with follow-up, which may be easier in the current era of home monitoring.
A dual chamber device to enable atrial pacing at 70–80 BPM and minimise QT prolongation or short-long-short pauses to trigger to torsades de pointes is advisable, although emerging data describe subcutaneous ICDs being employed in people with long QT syndrome.
There are no systematic data on the use of prophylactic pacing or ICDs in this population and therefore it is not addressed in the current Heart Rhythm Society and European Society of Cardiology guidelines, which focus on minimising QT prolongation. Device implantation in such patients may be difficult because their mental state may affect their ability to tolerate an ICD, which may exacerbate their psychiatric condition. The decision to implant an ICD
In cases where symptoms of pre-syncope or syncope are suspected due to ventricular arrhythmias, an implantable loop recorder may be considered (if the patient is amenable) to correlate symptoms with arrhythmia and hence guide management.
General Principles • • • •
1. 2. 3.
Assume all antipsychotics carry an increased risk of sudden cardiac death. Prescribe the lowest antipsychotic dose possible and avoid polypharmacy/metabolic interactions. Perform ECG on admission, before discharge and at yearly check-up. Consider measuring QTc within a week of reaching therapeutic doses of moderate-/high-risk antipsychotics.
Taylor DM, Barnes TRE, Young AH. Maudsley Prescribing Guidelines in Psychiatry. 13th edition. Chichester: Wiley Blackwell; 2018. Brown S. Excess mortality of schizophrenia. A meta-analysis. Brit J Psychiatry 1997;171:502–8. PMID: 9519087. Casey DE. Metabolic issues and cardiovascular disease in patients with psychiatric disorders. Am J Med 2005;118(Suppl 2):12S–22S. https://doi.org/10.1016/j.amjmed.2005.01.046; PMID: 15903291. Enger C, Weatherby L, Reynolds RF, et al. Serious cardiovascular events and mortality among patients with schizophrenia. J Nerv Ment Dis 2004;192:19–27. https://doi. org/10.1097/01.nmd.0000105996.62105.07; PMID: 14718772. Torniainen M, Mittendorfer-Rutz E, Tanskanen A, et al. Antipsychotic treatment and mortality in schizophrenia. Schizophr Bull 2015;41:656–63. https://doi.org/10.1093/schbul/ sbu164; PMID: 25422511. Taipale H, Mittendorfer-Rutz E, Alexanderson K, et al. Antipsychotics and mortality in a nationwide cohort of 29,823 patients with schizophrenia. Schizophr Res 2018;197;274–80. https://doi.org/10.1016/j.schres.2017.12.010; PMID: 29274734. Reilly JG , Ayis SA, Ferrier IN, et al. Thioridazine and sudden unexplained death in psychiatric in-patients. Br J Psychiatry 2002;180:515–22. https://doi.org/10.1192/bjp.180.6.515; PMID:12042230. Hennessy S, Bilker WB, Knauss JS, et al. Cardiac arrest and ventricular arrhythmia in patients taking antipsychotic drugs: cohort study using administrative data. BMJ 2002;325:1070. https://doi.org/10.1136/bmj.325.7372.1070; PMID: 12424166. Straus SM, Bleumink GS, Dieleman JP, et al. Antipsychotics and the risk of sudden cardiac death. Arch Intern Med 2004;164:1293–7. https://doi.org/10.1001/ archinte.164.12.1293; PMID:15226162. Liperoti R, Gambassi G, Lapane KL, et al. Conventional and atypical antipsychotics and the risk of hospitalization for ventricular arrhythmias or cardiac arrest. Arch Intern Med 2005;165:696–701. https://doi.org/10.1001/archinte.165.6.696; PMID: 15795349. Ray WA, Chung CP, Murray KT, et al. Atypical antipsychotic drugs and the risk of sudden cardiac death. N Engl J Med 2009;360:225–35. https://doi.org/10.1056/NEJMoa0806994; PMID:19144938. Malik M, Camm AJ. Evaluation of drug-induced QT interval prolongation: implications for drug approval and labelling. Drug Saf 2001;24:323–51. https://doi.org/10.2165/00002018200124050-00001; PMID:11419561. Botstein P. Is QT interval prolongation harmful? A regulatory perspective. Am J Cardiol 1993;72:B50–2. https://doi.org/ 10.1016/0002-9149(93)90041-A; PMID: 8256756.
14. Postema PG, De Jong JS, Van der Bilt IA, Wilde AA. Accurate electrocardiographic assessment of the QT interval: teach the tangent. Heart Rhythm 2008;5:1015–8. https://doi.org/10.1016/j. hrthm.2008.03.037; PMID: 18598957. 15. National Institute for Health and Care Excellence. Chest pain of recent onset: assessment and diagnosis. London: NICE; 2010. Available at: www.nice.org.uk/cg95 (accessed 12 June 2019). 16. Itoh H, Sakaguchi T, Ding WG et al. Latent genetic backgrounds and molecular pathogenesis in drug-induced long-QT syndrome. Circ Arrhythm Electrophysiol 2009;2:511–23. https://doi.org/10.1161/CIRCEP.109.862649; PMID: 19843919. 17. Priori SG, Wilde AA, Horie M, et al. HRS/EHRA/APHRS expert consensus statement on the diagnosis and management of patients with inherited primary arrhythmia syndromes: document endorsed by HRS, EHRA, and APHRS in May 2013 and by ACCF, AHA, PACES, and AEPC in June 2013. Heart Rhythm 2013;10:1932–63. https://doi.org/10.1016/j. hrthm.2013.05.014; PMID: 24011539. 18. Haddad PM, Anderson IM. Antipsychotic-related QTc prolongation, torsade de pointes and sudden death. Drugs 2002;62:1649–71. https://doi.org/10.2165/00003495200262110-00006; PMID:12109926. 19. Taylor DM. Antipsychotics and QT prolongation. Acta Psychiatr Scand 2003;107:85–95. https://doi.org/10.1034/j.16000447.2003.02078.x; PMID:12534433. 20. Glassman AH, Bigger JT Jr. Antipsychotic drugs: prolonged QTc interval, torsade de pointes, and sudden death. Am J Psychiatry 2001;158:1774–82. https://doi.org/10.1176/appi. ajp.158.11.1774; PMID: 11691681. 21. Warner B, Hoffmann P. Investigation of the potential of clozapine to cause torsade de pointes. Adverse Drug React Toxicol Rev 2002;21:189–203. https://doi.org/10.1007/BF03256196; PMID:12503253. 22. Harrigan EP, Miceli JJ, Anziano R, et al. A randomized evaluation of the effects of six antipsychotic agents on QTc, in the absence and presence of metabolic inhibition. J Clin Psychopharmacol 2004;24:62–9. https://doi.org/10.1109/ MM.2004.1269000; PMID: 14709949. 23. Lindborg SR, Beasley CM, Alaka K, Taylor CC. Effects of intramuscular olanzapine vs haloperidol and placebo on QTc intervals in acutely agitated patients. Psychiatry Res 2003;119:113–23. https://doi.org/10.1016/S01651781(03)00107-0; PMID: 12860365. 24. Dineen S, Withrow K, Voronovitch L, et al. QTc prolongation and high-dose olanzapine. Psychosomatics 2003;44:174–5. https://doi.org/10.1176/appi.psy.44.2.174; PMID:12618539. 25. Gupta S, Nienhaus K, Shah SA. Quetiapine and QTc issues: a case report. J Clin Psychiatry 2003;64:612–3. https://doi.
org/10.4088/JCP.v64n0518e; PMID:12755671. 26. Su KP, Shen WW, Chuang CL, et al. A pilot cross-over design study on QTc interval prolongation associated with sulpiride and haloperidol. Schizophr Res 2003;59:93–4. https://doi. org/10.1016/S0920-9964(01)00336-X; PMID: 12413648. 27. Lin CH, Chen MC, Wang SY, Lin CY. Predictive factors for QTc prolongation in schizophrenic patients taking antipsychotics. J Formos Med Assoc 2004;103:437–41. PMID: 15278188. 28. Chong SA, Mythily, Lum A, et al. Prolonged QTc intervals in medicated patients with schizophrenia. Hum Psychopharmacol 2003;18:647–9. https://doi.org/10.1002/hup.540; PMID:14696025. 29. Krantz MJ, Kutinsky IB, Robertson AD, Mehler PS. Doserelated effects of methadone on QT prolongation in a series of patients with torsade de pointes. Pharmacotherapy 2003;23:802–5. https://doi.org/10.1592/phco.23.6.802.32186; PMID: 12820821. 30. Gil M, Sala M, Anguera I, et al. QT prolongation and torsades de pointes in patients infected with human immunodeficiency virus and treated with methadone. Am J Cardiol 2003; 92:995–7. https://doi.org/10.1016/S0002-9149(03)00906-8; PMID: 14556883. 31. Piguet V, Desmeules J, Ehret G, et al. QT interval prolongation in patients on methadone with concomitant drugs. J Clin Psychopharmacol 2004;24:446–8. https://doi.org/10.1097/01. jcp.0000132347.81455.57; PMID:15232338. 32. Stollberger C, Huber JO, Finsterer J. Antipsychotic drugs and QT prolongation. Int Clin Psychopharmacol 2005; 20:243–51. https://doi.org/10.1097/01.yic.0000166405.49473.70; PMID:16096514. 33. Isbister GK, Murray L, John S, et al. Amisulpride deliberate self-poisoning causing severe cardiac toxicity including QT prolongation and torsades de pointes. Med J Aust 2006; 184:354–6. https://doi.org/10.5694/j.1326-5377.2006. tb00272.x; PMID: 16584372. 34. Ward DI. Two cases of amisulpride overdose: a cause for prolonged QT syndrome. Emerg Med Australas 2005; 17:274–6. https://doi.org/10.1111/j.1742-6723.2005.00734.x; PMID:15953230. 35. Vieweg WV, Schneider RK, Wood MA. Torsade de pointes in a patient with complex medical and psychiatric conditions receiving low-dose quetiapine. Acta Psychiatr Scand 2005;112:318–22. https://doi.org/10.1111/j.16000447.2005.00592.x; PMID: 16156840. 36. Huang BH, Hsia CP, Chen CY. Sulpiride induced torsade de pointes. Int J Cardiol 2007;18:e100–2. https://doi.org/10.1016/j. ijcard.2007.01.060; PMID: 17408770. 37. Kane JM, Lauriello J, Laska E, et al. Long-term efficacy
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BHRS Clinical Practice Guidelines and safety of iloperidone: results from 3 clinical trials for the treatment of schizophrenia. J Clin Psychopharmacol 2008;28(Suppl 1):S29–35. https://doi.org/10.1097/ JCP.0b013e318169cca7; PMID: 18334910. 38. Kim MD, Eun SY, Jo SH. Blockade of HERG human K+ channel and IKr of guinea pig cardiomyocytes by prochlorperazine. Eur J Pharmacol 2006;544:82–90. https://doi.org/10.1016/j. ejphar.2006.06.009; PMID: 16860311. 39. Meltzer H, Bobo WV, Nuamah IF, et al. Efficacy and
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tolerability of oral paliperidone extended-release tablets in the treatment of acute schizophrenia: pooled data from three 6-week placebo-controlled studies. J Clin Psychiatry 2006;69:817–29. PMID: 18466043. 40. Su KP, Lane HY, Chuang CL, et al. Olanzapine-induced QTc prolongation in a patient with Wolff-ParkinsonWhite syndrome. Schizophr Res 2004;66:191–2. https://doi. org/10.1016/S0920-9964(03)00182-8; PMID: 15061254. 41. Morissette P, Hreiche R, Mallet L, et al. Olanzapine prolongs
cardiac repolarization by blocking the rapid component of the delayed rectifier potassium current. J Psychopharmacol 2007;21:735–41. https://doi.org/10.1080/02680930600969308; PMID: 17092964. 42. Bär KJ1, Koschke M, Berger S, et al. Influence of olanzapine on QT variability and complexity measures of heart rate in patients with schizophrenia. J Clin Psychopharmacol 2008;28:694–8. https://doi.org/10.1097/ JCP.0b013e31818a6d25; PMID: 19011440.
The Increment of Short-term Variability of Repolarisation Determines the Severity of the Imminent Arrhythmic Outcome Agnieszka Smoczynska, Henriëtte DM Beekman and Marc A Vos Department of Medical Physiology, University Medical Center Utrecht, Utrecht, the Netherlands
Abstract Ventricular remodelling can make the heart more susceptible to ventricular arrhythmias like torsades de pointes. Understanding the underlying mechanisms of initiation of ventricular arrhythmias and the determining factors for its severity has the potential to uncover new interventions. Beat-to-beat variation of repolarisation, quantified as short-term variability of repolarisation (STV), has been identified as an important factor contributing to arrhythmogenesis. This article provides an overview of experimental data about STV in relation to the initiation of torsades de pointes in a canine model of complete chronic atrioventricular block susceptible to torsades de pointes arrhythmias. Furthermore, it explores STV in relation to the severity of the arrhythmic outcome.
Keywords Short-term variability of repolarisation, beat-to-beat variation of repolarisation, ventricular arrhythmias, torsades de pointes Disclosure: The authors have no conflicts of interest to declare. Received: 15 January 2019 Accepted: 17 June 2019 Citation: Arrhythmia & Electrophysiology Review 2019;8(3):166–72. DOI: https://doi.org/10.15420/aer.2019.16.2 Correspondence: Marc Vos, Department of Medical Physiology, University Medical Center Utrecht, Utrecht, the Netherlands. E: firstname.lastname@example.org Open Access: This work is open access under the CC-BY-NC 4.0 License which allows users to copy, redistribute and make derivative works for non-commercial purposes, provided the original work is cited correctly.
The plasticity of the heart enables it to adapt to certain pathological insults and to maintain the cardiac output necessary to satisfy the metabolic requirements of the body.1 Although beneficial at first, this process of ventricular remodelling can have detrimental effects on cardiac function and contribute to arrhythmogenesis.2 Sudden cardiac death due to ventricular tachyarrhythmias accounts for up to 60% of cardiovascular deaths.3 Despite improvements in cardiovascular risk management and therapeutic strategies for heart failure, sudden cardiac death remains an important healthcare issue.4 A considerable number of patients rely on the ICD for the prevention of sudden cardiac death.5 While ICD therapy is highly effective in ending ventricular arrhythmias, it does not prevent malignant arrhythmias from occurring.6–8 Experiencing ICD-shocks – either appropriate or inappropriate – can result in psychosocial distress for the patient and a reduced quality of life.9,10 Additional treatment, such as antiarrhythmic drugs or radio frequency ablation, are often used as adjunctive therapy to reduce the number of ICD-shocks, but both therapeutic modalities expose the patient to potentially adverse effects.11,12 This underscores the importance of finding alternative therapies that intervene to prevent the arrhythmia (and concomitant shock therapy). Beat-to-beat variation of repolarisation (BVR) quantified as short-term variability of repolarisation (STV), has been studied extensively in relation to arrhythmogenesis in the chronic complete atrioventricular block (CAVB) dog model.13 STV has been proposed as a novel electrophysiological parameter for the monitoring of imminent ventricular arrhythmias.14,15 This review explains the influence of ventricular remodelling on BVR and how it can lead to a higher susceptibility for arrhythmias. The severity of arrhythmias is diverse;
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therefore, this review also explores the relation of STV to the severity of the arrhythmic outcome. It will also discuss the clinical implications of STV.
Ventricular Remodelling in the Chronic Complete Atrioventricular Block Dog Model The CAVB dog model is a widely used animal model to study ventricular remodelling and its relation with ventricular arrhythmias.16–18 It is a model of compensated biventricular hypertrophy with a long QT phenotype and is reproducibly inducible for torsades de pointes (TdP) arrhythmias when challenged with a delayed rectifier outward potassium current (IKr)-blocker such as dofetilide.19–21 Creation of complete atrioventricular block, by means of His bundle ablation, forces the ventricles to activate from an infranodal focus of the conduction system resulting in a slow idioventricular rhythm. As a result of this bradycardia, the cardiac output drops abruptly and there is volume overload. The decrease in cardiac output and the altered asynchronous activation form insults to the heart and trigger electrical, contractile and structural ventricular remodelling as compensatory mechanisms. Whereas structural remodelling forms slowly and reaches a stable condition between 12 and 16 weeks of CAVB, electrical and contractile remodelling are present from 2 weeks of CAVB and coincide with the inducibility of TdP arrhythmias in the CAVB dog model.13,22–28 The most striking feature of electrical remodelling is the prolongation of repolarisation time.15,18,28 On a cellular level, this is explained by the downregulation of the slow and rapid components of the delayed rectifier outward potassium currents (IKs and IKr).29 The prolonged action potential duration (APD) provides more time for the contractile process
© RADCLIFFE CARDIOLOGY 2019
Short-term Variability of Repolarisation and Arrhythmic Outcome of the cardiomyocyte. In combination with altered Ca2+ handling this leads to Ca2+ overload in the CAVB dog model.30–32 In an electrically remodelled heart, the Ca2+ overload can lead to early after depolarisations (EADs) that can trigger TdP arrhythmias in this animal model.
Figure 1: Poincaré Plot Depicting Left Ventricular Monophasic Action Potential Durations 600
Repolarisation Reserve is Reflected by Beat-to-beat Variation of Repolarisation
In a Poincaré plot, the repolarisation duration of a predetermined number of consecutive beats (n=31) is plotted against the duration of each previous beat (Figure 1). The difference in repolarisation between two subsequent beats is reported by each deviation from the line of identity. The average deviation of repolarisation from the line of identity for a number of beats reports STV, expressed in milliseconds (ms). STV is calculated according to the formula ∑ Dn+1 − Dn / ( N × 2) , where D represents repolarisation duration and N is the total number of beats.36
Increased Temporal Dispersion of Repolarisation as a Substrate and Trigger for Torsades de Pointes There are two fundamental requirements for ventricular arrhythmias such as TdP. The first being the need for a substrate that renders the heart susceptible to the development of arrhythmias. Electrical remodelling in the CAVB dog model diminishes the repolarisation reserve, thereby forming part of the substrate. During sinus rhythm and acute AV-block in the absence of remodelling, TdP arrhythmias cannot be induced when the animals are challenged with dofetilide.13,26 It is also evident that STV is higher at baseline in CAVB dogs where TdPs can be induced repetitively compared with resistant CAVB dogs.37,38 Sudden cardiac death can occur in the canine CAVB model and is associated with a higher STV at baseline.39 This indicates that proarrhythmic ventricular remodelling is associated with an increased STVbaseline which reflects the decreased repolarisation reserve. The second fundamental for ventricular arrhythmias is the presence of a trigger. Temporal dispersion of repolarisation also fulfils this second role. Alongside ventricular remodelling, anaesthesia and bradycardia further challenge the repolarisation reserve in the canine CAVB model.14,21,40 When the ‘final hit’ in the form of an IKr-blocker is infused, the absolute value of STV increases abruptly in the minutes preceding a storm of TdPs (STVarrhythmic).13–15,36,41,42 Moreover, STV has been shown to be a superior repolarisation parameter to predict the imminent proarrhythmic outcome, compared with repolarisation prolongation and interventricular dispersion of repolarisation.25,40
Relation of Short-term Variability of Repolarisation to Severity of Arrhythmic Outcome The arrhythmic outcome in the inducible CAVB dogs is diverse and can range from self-terminating TdPs to TdPs requiring defibrillation
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LV MAPD (n), ms
The normal cardiomyocyte possesses a redundancy in repolarising currents, enabling it to withstand internal and external challenging factors on the repolarisation, which is also called the repolarisation reserve.33 The duration of repolarisation is within normal limits and fluctuates slightly between subsequent beats. Repolarisation reserve diminishes due to electrical remodelling, causing the heart to become more susceptible to repolarisation-related ventricular arrhythmia. This phenomenon is reflected by the increased temporal dispersion of repolarisation, or BVR, which can be quantified as STV. This should not be confused with heart rate variability (HRV), which reflects the balance of the activity of the autonomic nervous system and can serve as an indicator of cardiovascular integrity and prognosis.34,35
Dofetilide STV = 5.9 ms
350 Baseline STV = 1.5 ms 300 300
450 500 LV MAPD (n-1), ms
Poincaré plot depicting 31 consecutive left ventricular monophasic action potential durations in milliseconds at baseline and after infusion of dofetilide but prior to the first ectopic beat. LV = left ventricular; h517151-3 = dog number; MAPD = monophasic action potential duration; STV = short-term variability of repolarisation.
for termination. Therefore, we investigated the relation of STV to the severity of the arrhythmic outcome. Articles studying STV in relation to TdP arrhythmias upon dofetilide challenge in the CAVB dog model at our lab were considered for pooled analysis (n=28). Only original articles that reported STV of the left ventricular monophasic action potential duration (STVLV, MAPD) in idioventricular rhythm-remodelled CAVB dogs and that used a standard anaesthetic regimen were considered. The QT-interval was measured on lead II of the ECG and corrected for heart rate using the van de Water formula or the Bazett formula.43,44 Original data from the remaining 11 articles was obtained. After removing duplicates, data from 64 inducible dogs was pooled.
Definition of Inducibility and Quantification of Arrhythmia Severity A decreased repolarisation reserve enables the occurrence of single ectopic beats (sEB), multiple ectopic beats (mEB), and TdPs. A TdP has been defined as ≥5 consecutive EBs with a twisting QRS vector around the isoelectric line. A dog is considered to be inducible when ≥3 TdPs occur within 10 minutes after the start of infusion with an IKr-blocker such as dofetilide.41 When a TdP lasts for 10–12 seconds, defibrillation with thoracic patches is performed to restore normal heart rhythm. The arrhythmia score (AS) has been developed in an effort to quantify the severity of the arrhythmias.45 The AS is calculated by averaging the three most severe arrhythmias that occur during 10 minutes from the start of dofetilide infusion. Figures 2A and B show how the individual arrhythmic event is scored: 1 point is given by default for each regular beat in the absence of arrhythmic events. An ectopic beat or run of ectopic beats is scored with 1 additional point per ectopic beat to a maximum of 50. Hence, 2 points for sEB, 3–5 points for mEB, and 6–50 points for TdP. A TdP requiring defibrillation is perceived as the most severe individual arrhythmic episode in the CAVB dog model and is applied for TdPs that last ≥10 seconds and contain ≥50 beats.
Clinical Arrhythmias Figure 2: Quantification of Arrhythmia Severity During 10 Minutes Using the Arrhythmia Score A
B Arrhythmic Event
Single ectopic beat
Run of 2–4 ectopic beats
TdP of 5+x beats
6 +x (maximum 50)
TdP/VF requiring 1, 2, ≥3 defibrillations
50 II MAP LV
Arrhythmia score: the average of the three most severe arrhythmic events during 10 minutes
10+12+50 = 24 3
C Severity of TdP storm in inducible animals with ≥3 TdPs
No combination defibrillation and no defibrillation
Arrhythmia score 20.7 Low AS <20.67
50 Intermediate AS 20.67–49.99
High AS ≥50
A. Scoring of the arrhythmic events using the AS. B. Example of calculation of the AS using ECG lead II and the monophasic action potential of the left ventricle. C. Categories of severity of a torsades de pointes storm and the corresponding AS in inducible animals based on the necessity of defibrillation for termination. AS = Arrhythmia score; LV = left ventricle; MAP = monophasic action potential; TdP = torsades de pointes. Source: Stams et al. 2013.45
Therefore, defibrillation is scored with 50, 75 or 100 points depending on the number of consecutive defibrillations necessary to terminate one TdP episode (1, 2, ≥3, respectively).40,46 Figure 2C illustrates how the AS corresponds with the severity of a storm of TdPs in inducible animals with ≥3 TdPs. We consider a storm of self-terminating TdPs less severe than a storm of TdPs requiring at least one defibrillation. In an inducible animal with one defibrillated TdP (50 points), the lowest possible AS is 20.67 when the other two TdPs have the minimal duration corresponding to 6 points each (AS = 50 + 6 + 6 = 20.67). 3 Therefore, all inducible animals with an AS ≤20.67 only have selfterminating TdPs and are part of the low AS group. An example of ECG tracings of an inducible animal with no defibrillations is shown in Figure 3A, where the AS is 16.3. The most severe storm of TdPs consists of TdPs requiring defibrillation (in the high AS group). The AS is 50, when all three most severe arrhythmic events require defibrillation (Figure 3B). However, an average of 50 points can also be obtained with three long-lasting TdPs of ≥49 beats that terminate spontaneously before 10 seconds. The AS can reach a maximum of 100 points when the three most severe arrhythmic events require ≥3 consecutive defibrillations for termination. A combination of self-terminating and defibrillated arrhythmias corresponds to an AS of ≥20.67 and <50. An example in Figure 2B shows the intermediate AS group.
STV and QTc Increase Before Torsades de Pointes It has been described previously that STVarrhythmic is higher compared with STVbaseline in inducible CAVB dogs. As shown in Figure 4A, STVarrhythmic is significantly increased compared with STVbaseline in the three different
AS groups. STV increases from 0.98±0.72 to 1.71 ± 1.18 (p=0.0020) in the low AS group, from 1.34 ± 0.61 to 3.78 ± 2.02 (p<0.0001) in the intermediate AS group, and from 1.37 ± 0.63 to 3.55 ± 1.40 (p<0.0001) in the high AS group (Table 1). STVbaseline values do not differ between the three AS groups. Whereas previous research reports that STVbaseline is significantly higher in inducible dogs compared with non-inducible dogs, STVbaseline does not seem to contribute to the severity of the arrhythmias in inducible dogs.37,38 STVarrhythmic shows a trend for higher values in the group with an intermediate and high AS compared with the low AS group, but fails to reach statistical significance. Arrhythmic QTc is also significantly increased compared with QTc baseline in the three AS groups (Figure 4B and Table 1). Neither the values of QTc baseline nor the values of arrhythmic QTc differ between AS groups.
The Rise in Short-term Variability of Repolarisation is Associated with the Severity of the Arrhythmic Outcome Figure 4A indicates that the change from STVbaseline to STVarrhythmic (∆STVLV, MAPD) is greater in the intermediate and high AS groups compared with the low AS group. Further quantification of this observation shows a significant correlation between ∆STVLV, MAPD and the AS (Spearman r 0.308, p=0.006; Figure 4C), indicating an association between the increment of STV and the severity of the arrhythmic outcome. This correlation is absent for the change in QTc (∆QTc), whereby QTc is equally prolonged in dogs with a low AS and a high AS (Pearson r 0.006, R2 3.65-0.005, p=0.481; Figure 4D). The AS has a heterogeneous nature, in the intermediate AS group the same AS score can be the result of a diverse combination in severity of arrhythmias. The group with an intermediate AS shows a big variation in ∆STVLV, MAPD with some outliers (Figure 2C and Figure 4C). Therefore,
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Short-term Variability of Repolarisation and Arrhythmic Outcome Figure 3: Examples of the Three Most Severe Arrhythmic Episodes in Two Inducible Chronic Atrioventricular Block Dogs A
10 mm/mV 10 mm/s
h631914-2 Arrhythmia score
19+17+13 = 16.3 3
15 mm/mV 10 mm/s
h628263-2 Arrhythmia score
50+50+50 = 50 3
Examples of the three most severe arrhythmic episodes within 10 minutes in two inducible chronic atrioventricular block dogs, one representative of a low arrhythmia score (A) and another of a high arrhythmia score (B). h631914-2 and h628263-2 both refer to the dog numbers.
the relative ∆STVLV, MAPD and ∆QTc have been compared in dogs with a low AS and high AS (Figure 4E). The dogs discussed in this review with an AS score of 50 all had ≥3TdPs requiring defibrillation. Relative ∆STVLV, MAPD increases with 90.83 ± 90.28% in the low AS group and with 197.50 ± 143.60% in the high AS group (p=0.0414). This discriminative capacity is not found in the relative ∆QTc, which prolongs almost equally with 42.03 ± 21.95% in the low AS group and 41.31 ± 20.14% in the high AS group (p>0.9999). In the high AS group, the relative ∆STVLV, MAPD is significantly higher than the relative ∆QTc (p<0.0001) (Table 1, Figure 4E).
Severity of Torsades de Pointes A higher ∆STV contributes to the severity of a storm of TdPs, but is unlikely to be determinant for the duration of an individual TdP. The mechanism of initiation of an individual TdP episode has been attributed to EAD-dependent focal activity due to reduced repolarisation reserve.47–54 The significant increase in STV before a storm of TdPs can therefore be expected. However, the mechanism underlying the perpetuation of a TdP episode is still under debate. Extensive mapping experiments have been performed in animal hearts, whereby on the one hand focal activity has been proposed as the dominant underlying mechanism.48,52–56 On the other hand, it has been suggested that nonstationary re-entry perpetuates TdPs.50,51,57 Others have observed both mechanisms to be present when TdP is perpetuated.47,49 These different observations may be due to the moment of measurement during a TdP. A TdP can deteriorate into ventricular fibrillation, which is most likely to be driven by re-entry. Therefore Boulaksil et al. clearly defined the moment of measurement,
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whereupon they based the conclusion that focal activity is the dominant mechanism in the perpetuation of self-terminating TdPs and during the early phase of a TdP episode, indicating triggered activity.58 Vandersickel et al. confirmed that all observed TdPs were perpetuated by focal activity in the early phase and in the case of short-lasting TdPs, but added that the longer-lasting TdP episodes were re-entry driven.59 It is important to note that the STV measured by the studies incorporated in Figure 4 preceded the first arrhythmic episode of a storm of sEB, mEB, or TdP and was correlated to the AS that was observed for 10 minutes. A higher ∆STV is therefore not associated with a longer individual episode of a TdP, but rather with the severity of the complete electrical storm of TdPs in a short period of time. The positive correlation between ∆STV and the AS can be explained in several ways. First of all, a more severely decreased repolarisation reserve reflected by a higher ∆STV, may give rise to more EADs and therefore more focal activity. The bigger amount of focal activity can in turn simply improve the odds that a proportion of the focal activity is perpetuated into a long-lasting TdP. Second, the circumstances during the early phase of a TdP may influence the perpetuation and/or degeneration into ventricular fibrillation. The increased repolarisation lability and propensity for competing foci may create a more chaotic activation pattern, and the consequences on the initiation of re-entry have not yet been explored. One of the factors that is believed to promote the initiation of re-entry is spatial dispersion of repolarisation. However, Dunnink et al. demonstrated that spatial dispersion of repolarisation contributes to
Clinical Arrhythmias Figure 4: Short-term Variability of Repolarisation of the Left Ventricular Monophasic Action Potential Duration in Relation to the Arrhythmic Outcome in the Inducible Chronic Atrioventricular Block Dog Model B
STVLV MAPD (ms)
QTc baseline QTc arrhythmic
0 Low AS (n=12)
Intermediate AS (n=25)
High AS (n=27)
Low AS (n=12)
Spearman r 0.308 p=0.006
Intermediate AS (n=25)
Pearson r 0.006 R2 = 3.65-0.005 p=0.481
6 4 2
High AS (n=27)
DSTVLV MAPD (ms)
Din % compared with baseline values
*** Low AS (n=12)
High AS (n=27) 200 100 0
A. STVbaseline and STVarrhythmic categorised by AS. B. Baseline QTc and arrhythmic QTc categorised by AS. C. Correlation between ∆STV and AS. D. Correlation between ∆QTc and AS. E) Relative ∆STV and ∆QTc grouped by AS. Low AS <20.67 and high AS ≥50. *p<0.05, **p<0.01, ***p<0.0001. AS = arrhythmia score; LV = left ventricular; MAPD = monophasic action potential duration; LV = left ventricular; MAPD = monophasic action potential duration; STV = short-term variability of repolarisation. Source: Oosterhoff et al. 2010;14 Sprenkeler et al. 2018;26 Bossu et al. 201740; Van de Water et al. 1989;43 Sandhu et al. 2008;67 Bossu et al. 2018;68 Ji et al. 2017;69 Varkevisser et al. 2013;70 Bourgonje et al. 2012;71 Stams et al. 2011;72 Oros et al. 2010.73
Table 1: Pooled Experimental Data of Short-term Variability (STV) of Repolarisation of the Left Ventricular Monophasic Action Potential Duration and QTc Prior to Torsades de Pointes STVarrhythmic and Arrhythmic QTc, Compared With Baseline in the Chronic Atrioventricular Block Dog Model STVLV, MAPD (ms)
Absolute (ms) Relative (%)
1.28 ± 0.65
3.30 ± 1.79**
2.01 ± 1.59
182.8 ± 145.3
398.60 ± 59.80 557.6 ± 69.43**
159.1 ± 72.22
42.04 ± 22.87
0.98 ± 0.72
1.71 ± 1.18*
0.73 ± 0.70
90.83 ± 90.28
397.20 ± 68.89 555.1 ± 68.52**
157.9 ± 67.60
42.03 ± 21.95
Intermediate (n=25) 1.34 ± 0.61
3.78 ± 2.02**
2.44 ± 1.87
211.10 ± 155.10*** 400.60 ± 57.82 562 ± 70.20**
161.60 ± 82.22
42.83 ± 26.67****
2.18 ± 1.32
197.50 ± 143.60
157.2 ± 66.73
41.31 ± 20.14****
1.37 ± 0.63
397.40 ± 59.70 554.6 ±71.49
Absolute (ms) Relative (%)
Data are expressed as mean ± standard deviation. *p<0.01, **p<0.0001 compared with the same electrophysiological parameter in baseline, ***p<0.05 compared with the same electrophysiological parameter in the low arrhythmia score group, ****p<0.0001 compared to relative ∆STV of the same arrhythmia score group. All comparisons between the same electrophysiological parameter were not significant between the intermediate and high arrhythmia score group. Statistics between STV and QTc have not been calculated for absolute ∆, because these are not comparable. CAVB = chronic atrioventricular block; LV = left ventricular; MAPD = monophasic action potential duration; STV = short-term variability of repolarisation.
the initiation and early phase continuation of a TdP, whereby focal activity was the underlying mechanism. In mapping experiments in the CAVB dog, sEBs and the first beat of the TdP arose at the site of maximal dispersion of repolarisation and demonstrated a focal origin. The TdPs continued with a second focal beat that arose from a different location in a region of maximal repolarisation heterogeneity.56 The complex interplay between temporal and spatial dispersion of repolarisation in relation to arrhythmogenesis needs further investigation.
Clinical Implications The observed change in STV before arrhythmia, and especially the correlation between ∆STV and severity of the imminent arrhythmic outcome, confirm the potential of STV as a possible electrophysiological marker for the monitoring of imminent ventricular arrhythmias.
Data from the canine CAVB model cannot readily be extrapolated to humans, therefore clinical studies need to further investigate STVarrhythmic. In this review STV was measured with an intracardiac MAP-catheter in the left ventricle. There are also other modalities to measure STV that can be applied in humans more easily, namely the QT-interval on the non-invasive ECG and the activation recovery interval on intracardiac electrogram (EGM) in devices. The ECG has been used in descriptive studies in humans to study if STVbaseline of the QT-interval can identify the individual at risk for repolarisationdependent ventricular arrhythmias.60–64 However, longer registrations are necessary to capture sustained ventricular arrhythmias in patients to investigate STVarrhythmic. The EGM is a promising candidate for the continuous monitoring of STV and to study STV before arrhythmia. In the CAVB dog, the STV can be reliably derived from the activation recovery interval of the EGM in the right ventricle (RV), and
ARRHYTHMIA & ELECTROPHYSIOLOGY REVIEW
Short-term Variability of Repolarisation and Arrhythmic Outcome it increases before TdP.15 Other repolarisation parameters have been studied in patients on the RV EGM, which indicates that it is a feasible method to further investigate STVarrhythmic in patients.65–67
Conclusion Temporal dispersion of repolarisation, quantified as STV, plays an important role in the initiation of ventricular arrhythmias like TdP. A sudden increase in STV precedes imminent TdP in the canine CAVB model. A higher increment of STV compared with baseline is associated with a more severe arrhythmic outcome, in contrast to QTc. These findings confirm the potential of STV for the monitoring of imminent pro-arrhythmic risk of the ventricles and create new options for interventions.
Hill JA, Olson EN. Cardiac plasticity. N Engl J Med 2008;358:1370–80. https://doi.org/10.1056/NEJMra072139; PMID: 18367740. Konstam MA, Kramer DG, Patel AR, et al. Left ventricular remodeling in heart failure current concepts in clinical significance and assessment. J Am Coll Cardiol Cardiovasc Imaging 2011;4:98–108. https://doi.org/10.1016/j.jcmg.2010.10.008; PMID: 21232712. Adabag AS, Luepker RV, Roger VL, et al. Sudden cardiac death: epidemiology and risk factors. Nat Rev Cardiol 2010;7:216–25. https://doi.org/10.1038/nrcardio.2010.3; PMID: 20142817. Deo R, Albert CM. Epidemiology and genetics of sudden cardiac death. Circulation 2013;125:620–37. https://doi. org/10.1161/CIRCULATIONAHA.111.023838; PMID: 22294707. Ponikowski P, Voors AA, Anker SD, et al. 2016 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J 2016;37:2129–2200. https://doi.org/10.1093/ eurheartj/ehw128; PMID: 27206819. Connolly SJ, Hallstrom AP, Cappato R, et al. Meta-analysis of the implantable cardioverter defibrillator secondary prevention trials. Eur Heart J 2000;21:2071–8. https://doi. org/10.1053/euhj.2000.2476; PMID: 11102258. Moss AJ, Zareba W, Hall WJ, et al. Prophylactic implantation of a defibrillator in patients with myocardial infarction and reduced ejection fraction. N Engl J Med 2002;346:877–83. https://doi.org/10.1056/NEJMoa013474; PMID: 11907286 Bardy GH, Lee KL, Mark DB, et al. Amiodarone or an implantable cardioverter-defibrillator for congestive heart failure. N Engl J Med 2005;352:225–37. https://doi.org/10.1056/ NEJMoa043399; PMID: 15659722. Sola CL, Bostwick JM. Implantable cardioverter-defibrillators, induced anxiety, and quality of life. Mayo Clin Proc 2005;80:232– 7. https://doi.org/10.4065/80.2.232; PMID: 15704778. Tomzik J, Koltermann KC, Zabel M, et al. Quality of life in patients with an implantable cardioverter defibrillator: a systematic review. Front Cardiovasc Med 2015;2–34. https://doi. org/10.3389/fcvm.2015.00034; PMID: 26664905. van Herendael H, Pinter A, Ahmad K, et al. Role of antiarrhythmic drugs in patients with implantable cardioverter defibrillators. Europace 2010;12:618–25. https://doi. org/10.1093/europace/euq073; PMID: 20304841. Reddy VY, Reynolds MR, Neuzil P, et al. Prophylactic catheter ablation for the prevention of defibrillator therapy. N Engl J Med 2007;2657–65. https://doi.org/10.1056/NEJMoa065457; PMID: 18160685. Thomsen MB, Oros A, Schoenmakers M, et al. Pro-arrhythmic electrical remodelling is associated with increased beat-tobeat variability of repolarisation. Cardiovasc Res 2007;73:521–30. https://doi.org/10.1016/j.cardiores.2006.11.025; PMID: 17196569. Oosterhoff P, Thomsen MB, Maas JN, et al. High-rate pacing reduces variability of repolarization and prevents repolarization-dependent arrhythmias in dogs with chronic AV block. J Cardiovasc Electrophysiol 2010;21:1384–91. https://doi. org/10.1111/j.1540-8167.2010.01824.x; PMID: 20561108. Wijers SC, Sprenkeler DJ, Bossu A, et al. Beat-to-beat variations in activation-recovery interval derived from the right ventricular electrogram can monitor arrhythmic risk under anesthetic and awake conditions in the canine chronic atrioventricular block model. Heart Rhythm 2018;15:442–8. https://doi.org/10.1016/j.hrthm.2017.11.011; PMID: 29146275. Oros A, Beekman JD, Vos MA. The canine model with chronic, complete atrio-ventricular block. Pharmacol Ther 2008;119:168– 78. https://doi.org/10.1016/j.pharmthera.2008.03.006; PMID: 18514320. Zhou S, Jung B, Tan AY, et al. Spontaneous stellate ganglion nerve activity and ventricular arrhythmia in a canine model of sudden death. Heart Rhythm 2008;5:131–9. https://doi. org/10.1016/j.hrthm.2007.09.007; PMID: 18055272. Wada T, Ohara H, Nakamura Y, et al. Impacts of surgically performed renal denervation on the cardiovascular and electrophysiological variables in the chronic atrioventricular block dogs. Circ J 2016;80:1556–63. https://doi.org/10.1253/ circj.CJ-16-0198; PMID: 27250918. Volders PG, Sipido K, Vos MA, et al. Cellular basis of
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Clinical Perspective • Beat-to-beat variation of repolarisation quantified as shortterm variability (STV) of repolarisation reflects the diminished repolarisation reserve of the heart. • The extent of the increase in STV before ventricular arrhythmia is correlated to the severity of the arrhythmia in the complete chronic AV-block dog model, in contrast to the change in QTc. • STV is a promising electrophysiological parameter for the monitoring of imminent proarrhythmic risk, further clinical studies are needed to confirm this potential.
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ARRHYTHMIA & ELECTROPHYSIOLOGY REVIEW
Electrophysiology and Ablation
Preventive Ventricular Tachycardia Ablation in Patients with Ischaemic Cardiomyopathy: Meta-analysis of Randomised Trials Roland R Tilz, 1 Charlotte Eitel, 1 Evgeny Lyan, 1 Kivanc Yalin, 1,2 Spyridon Liosis, 1 Julia Vogler, 1 Ben Brueggemann, 1 Ingo Eitel, 1 Christian Heeger, 1 Ahmed AlTurki 3 and Riccardo Proietti 4 1. University Heart Centre Lübeck, Lübeck, Germany; 2. Usak University, Faculty of Medicine, Department of Cardiology, Usak, Turkey; 3. Division of Cardiology, McGill University Health Centre, Montreal, Canada; 4. Department of Cardiac, Thoracic and Vascular Sciences, University of Padua, Padua, Italy
Abstract Catheter ablation of ventricular tachycardia (VT) aims to treat the underlying arrhythmia substrate to prevent ICD therapies. The aim of this meta-analysis was to assess the safety and efficacy of VT ablation prior to or at the time of secondary prevention ICD implantation in patients with coronary artery disease, as compared with deferred VT ablation. Based on a systematic literature search, three randomised trials were considered eligible for inclusion in this analysis, and data on the number of patients with appropriate ICD shocks, appropriate ICD therapy, arrhythmic storm, death and major complications were extracted from each study. On pooled analysis, there was a significant reduction of appropriate ICD shocks (OR 2.58; 95% CI [1.54–4.34]; p<0.001) and appropriate ICD therapies (OR 2.04; 95% CI [1.15–3.61]; p=0.015) in patients undergoing VT ablation at the time of ICD implantation without significant differences with respect to complications (OR 1.39; 95% CI [0.43–4.51]; p=0.581). Mortality did not differ between both groups (OR 1.30; 95% CI [0.60–2.45]; p=0.422). Preventive catheter ablation of VT in patients with coronary heart disease at the time of secondary prevention ICD implantation results in a significant reduction of appropriate ICD shocks and any appropriate ICD therapy compared with patients without or with deferred VT ablation. No significant difference with respect to complications or mortality was observed between both treatment strategies.
Keywords Ventricular arrhythmias, catheter ablation, meta-analysis Disclosure: RT received research grants from Hansen, Abbot, Medtronic and Biotronik; travel grants from Biosense Webster, Medtronic, Abbot, Sentrheart and Daiichi Sankyo; speakers’ bureau honoraria/proctor for Biosense Webster, Medtronic, Abbot, Biotronik, Boston Scientific, Pfizer, Bristol-Myers Squibb, Bayer, Sanofi Aventis and AstraZeneca; and worked as a consultant for Biosense Webster and Biotronik. CE received presentation fees from Bayer, Biosense Webster, Impulse Dynamic, St Jude Medical/Abbott, Pfizer, Liva Nova, Zoll, Boston Scientific, Novartis, Daiichi Sankyo and AstraZeneca; and travel grants from St Jude Medical, Biotronik and Medtronic. EL received travel grants from Biosense Webster, Abbott, Medtronic and Boston Scientific; and speakers’ bureau honoraria from Biosense Webster and Abbott. KY received an educational and research grant from the Turkish Society of Cardiology. All other authors have no conflicts of interest to declare. Received: 19 March 2019 Accepted: 17 June 2019 Citation: Arrhythmia & Electrophysiology Review 2019;8(3):173–9. DOI: https://doi.org/10.15420/aer.2019.31.3 Correspondence: Riccardo Proietti, Department of Cardiac, Thoracic and Vascular Sciences, University of Padua, Via Giustiniani 2, 35121 Padua, Italy. E: Riccardoproietti6@gmail.com Open Access: This work is open access under the CC-BY-NC 4.0 License which allows users to copy, redistribute and make derivative works for non-commercial purposes, provided the original work is cited correctly.
Ventricular tachycardia (VT) is associated with increased mortality in patients with a history of MI. ICD implantation is currently the standard of care for the prevention of sudden cardiac death, and contributes to a reduction of total mortality. 1 Despite effective treatment of ventricular arrhythmias with the use of anti-tachycardia pacing (ATP) or shocks, ICDs do not prevent VT. Furthermore, sudden cardiac death still occurs in approximately 5% of ICD patients, and ICD therapies are associated with an increase in mortality and a reduction in quality of life.2–5 According to a recent meta-analysis, mortality after appropriate shock is higher than after inappropriate shock, suggesting an increased risk related to the underlying arrhythmia substrate.3 The concept of VT ablation aims at effective treatment of the underlying arrhythmia substrate with consecutive prevention of shocks instead of treatment of VT with the use of shocks. Early VT ablation, defined as ablation within 30 days after the first documented VT, has been associated with
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a lower VT recurrence rate, as compared with VT ablation performed later.6 Preventive catheter ablation before the occurrence of any ICD therapy might contribute to improved prognosis, compared with deferred VT ablation. Recent guidelines recommend urgent catheter ablation in patients with scar-related heart disease presenting with incessant VT or electrical storm, and in patients with ischaemic heart disease and recurrent ICD shocks due to sustained VT.1 Furthermore, catheter ablation should be considered after a first episode of sustained VT in patients with ischaemic heart disease and an ICD.1 Data on preventive catheter ablation at the time of ICD implantation are limited, but potential benefits might relate to a reduction of ICD therapies and mortality. The aim of this meta-analysis was to assess the safety and efficacy of VT ablation prior to or at the time of secondary prevention ICD implantation in patients with coronary artery disease, as compared with deferred VT ablation.
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Electrophysiology and Ablation Figure 1: Individual and Pooled OR for Appropriate ICD Shock Events, Study
OR (95% CI)
ICD only Ablation plus ICD Weight
Overall (I-squared=0.0%, p=0.445)
NOTE: Weights are from random effects analysis .1
1 Ablation plus ICD
10 ICD only
Individual and pooled OR for appropriate ICD shock in the two groups, catheter ablation plus ICD and ICD only (p<0.001).
Methods Search Strategy Studies were identified by searching electronic databases (Medline via OvidSP, Embase via OvidSP, Medline via PubMed, BIOSIS Previews via OvidSP, Web of Science and Scopus) from inception to 21 December 2017. The literature search used text words and relevant indexing to capture data on catheter ablation of VT/VF or arrhythmogenic substrate modification in patients with ischaemic cardiomyopathy undergoing ICD implantation. The following search structure was used in Medline and translated into the other databases as appropriate: (catheter ablation[title/abstract]) AND (implantable defibrillator[title/abstract]) OR (implantable cardioverter defibrillator[title/abstract]) AND (ventricular fibrillation[title/abstract]) OR (ventricular tachycardia[title/abstract]). Handsearching, with crossreferences of retrieved publications, review articles and guidelines, was also performed to ensure that all relevant studies were included. No restriction on study type or language was applied.
Study Selection Studies selected for inclusion were randomised trials that tested the safety and effectiveness of catheter ablation for VT before ICD implantation versus ICD implantation alone in patients with ischaemic cardiomyopathy. Cohort studies, case reports, case series, review articles and conference abstracts were excluded. Selection was limited to English-language articles. The first screening was performed independently by two authors (RP and CE), based on title and abstract. Next, the full text of the eligible articles was examined to ensure that they met the study criteria: randomisation of patients to catheter ablation plus ICD implantation versus ICD implantation alone, patients included had ischaemic cardiomyopathy, the catheter ablation strategy applied was described in detail, and data were provided regarding the safety and efficacy of both therapeutic approaches. From the selected studies, the same authors (RP and CE) then independently extracted data. Disagreements were resolved by discussion; if no accord was reached, a third author (RRT) made the final decision. Data on the type of study, year of publication, country where the study was performed, population included, presence of spontaneous or inducible VT/VF, type
of ablation performed, duration of long-term follow up, the occurrence of appropriate ICD shock, appropriate ICD therapy (ATP and/or shock), arrhythmic storm, death and complications were extracted. The primary outcome was the number of patients with an appropriate ICD shock in both groups at long-term follow up. Secondary outcomes were the number of patients with appropriate ICD therapy (shock and/ or ATP), arrhythmic storm, death and major complications in the two groups.
Statistical Analysis From the number of events extracted in the two groups (ablation plus ICD versus ICD alone) and their respective population, the number of non-cases in both groups was calculated. The OR was the primary measure of treatment effect or side-effect; 95% CIs for OR were calculated. The ORs were then pooled using a DerSimonian and Laird random effect model.7 Heterogeneity was assessed with I-squared statistics (I2). The I2 statistic indicated the percentage of variability due to between-study (or inter-study) variability, as opposed to within-study (or intra-study variability). An I2 >50% was classified as substantial presence of heterogeneity. The analysis was performed using commercially available software STATA 14 (StataCorp).
Quality Assessment The risk of bias was assessed based on the tool from the Cochrane Collaboration.8 Specifically, the risk of bias was assessed in the following domains: selection (random sequence generation and allocation concealment), performance (blinding of participants and personnel), detection (blinding of outcome assessment), attrition, reporting and other bias. Every domain could be classified as high or low risk of bias. If the information reported in the article was insufficient, the domain was defined as unclear.
Results Our literature search identified 340 citations after exclusion of duplicates. After review of the abstracts, nine of these references were considered potentially eligible for inclusion, and their full text
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Preventive Ventricular Tachycardia Ablation in Patients with Ischaemic Cardiomyopathy Table 1: Key Features of Trials Included in the Meta-analysis Study
Patients (n) Inclusion criteria
Prior MI, secondary prevention ICD, primary prevention ICD with appropriate ICD therapy included later on
Survival free from any appropriate ICD therapy
Prior MI, secondary prevention ICD for stable ICD only VT, LVEF ≤50%
Time from ICD implantation to 22.5 recurrence of sustained VT/VF
CAD, secondary prevention ICD for unstable VT, LVEF ≤40%
Time to first recurrence of VT/VF
CAD = coronary artery disease; LVEF = left ventricular ejection fraction; VT = ventricular tachycardia.
was analysed in more detail. Three references were excluded because patients with non-ischaemic cardiomyopathy were included, one was excluded because of a lack of a comparison group and two were excluded because only patients with a prior ICD shock were included.9–14 Finally, a total of three randomised trials were included in the analysis (Supplementary Material Figure 1).15–17
Study Characteristics The key features of the three trials are summarised in Table 1.15–17 All studies had a multicentre, prospective, randomised controlled study design.15–17 Randomisation was performed using computer-based 1:1 allocation (Ventricular Tachycardia Ablation in Coronary Heart disease; VTACH, Substrate Modification Study; SMS) or with the use of sealed numbered envelopes (Substrate Mapping and Ablation in Sinus rhythm to Halt Ventricular Tachycardia; SMASH-VT).15–17 Regarding drug therapy, patients taking class I or III antiarrhythmic medications were excluded from SMASH-VT; VTACH allowed antiarrhythmics in the control arm at the discretion of the treating physician, and SMS stratified the randomisation of patients according to treatment with amiodarone (30% of patients) or a beta-blocker.15–17 VT ablation was performed with the use of an electroanatomical mapping system (SMASH-VT, VTACH, SMS), a non-contact-mapping system (VTACH, SMS) and conventional ablation (SMS) with the use of irrigated and non-irrigated (SMASH-VT, VTACH, SMS) ablation catheters. In the SMASH-VT study, a non-irrigated ablation catheter was used throughout the study in the USA, as irrigated catheters had not been approved for clinical use until after the end of enrolment. Mapping and ablation was performed in sinus rhythm or, if AV conduction was not present, with ventricular pacing in SMASHT-VT, whereas patients in VTACH and SMS underwent mapping and ablation of stable VT or substrate modification in case of noninducible or unstable VT. In case of inducible VT at the beginning of the procedure, the ablation endpoint was non-inducibility of the clinical VT (SMS) or any VT (VTACH). In case of non-inducibility, the absence of all channels/late potentials inside the area of interest or ablation with linear lesions based on pace mapping along the infarct scar target sites was aimed at (VTACH) or the ablation strategy was based on the anatomy of the substrate, and the procedural endpoint was the implementation of the projected ablation lines (SMS). In the SMASH-VT study, the VT was induced, the arrhythmogenic portions were identified by pace mapping, and entrainment mapping and targeted for ablation with linear ablation lines or with ablation of the late and/or fractionated potentials deeper within the scar. If only VF or polymorphic VT was inducible, stimulation was repeated after
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Table 2: Baseline Characteristics of Studies Included in the Meta-analysis Study
67 ± 9
30.7 ± 9.5
66 ± 10
32.9 ± 8.5
67.7 ± 8.3 50 (96)
34.0 ± 9.6
64.4 ± 8.2 50 (91)
34.1 ± 8.8
68.4 ± 7.7 47 (87)
32.0 ± 6.9
65.9 ± 8.4 46 (81)
30.4 ± 7.3
Values are mean ± standard deviation or n (%). LVEF = left ventricular ejection fraction.
intravenous infusion of a class I antiarrhythmic drug (procainamide or ajmaline). The ICD manufacturer and programming differed between studies. While SMASH-VT did not standardise ICD manufacturers and programming, except the definition of at least one VT zone with ATP, VTACH only used St Jude Medical devices and SMS Medtronic devices with predefined programming (VF zone with a cut-off rate of 200–220 BPM, and a VT zone with a cut-off cycle length of 60 ms above the slowest documented VT and anti-tachycardia pacing followed by shock therapy). Baseline characteristics of patients included in the three studies were quite similar and are shown in Table 2. Differences relate to the use of antiarrhythmic drugs at inclusion, with none of the patients in SMASHVT receiving amiodarone, whereas about 30% of patients in the other studies were taking amiodarone.
Appropriate ICD Shocks All three randomised trials reported data on the number of patients with an appropriate shock in the two groups (ablation plus ICD and ICD only). Overall, 346 patients were included in the analysis. The random effect pooled analysis showed an OR of 2.58 (95% CI [1.54–4.34]; p<0.001), favouring the former. The heterogeneity was 0.0%; p=0.445 (Figure 1).
Secondary Endpoints The pooled analysis on the endpoint of any appropriate ICD therapy included 346 from all three randomised trials. The cumulative OR was 2.04 (95% CI [1.15–3.61]; p=0.015), favouring ablation plus ICD. The I2 was 32.2%; p=0.229 (Figure 2).
Electrophysiology and Ablation Figure 2: Individual and Pooled OR for Appropriate ICD Therapy Events, Study
OR (95% CI)
ICD only Ablation plus ICD Weight
Overall (I-squared=32.2%, p=0.229)
NOTE: Weights are from random effects analysis .1
1 Events Ablation plus ICD
10 Events ICD only
Individual and pooled OR for appropriate ICD therapy in the two groups, catheter ablation plus ICD and ICD only (p=0.015).
The number of deaths for both groups was reported in all three randomised trials for an overall number of 346 patients with a pooled OR of 1.30 (95% CI [0.60–2.45]; p=0.422; Figure 3). The number of arrhythmic storms was reported in two studies, whereas one study reported the percentage of patients free from arrhythmic storms at follow-up; from this, the number of patients with arrhythmic storms was calculated, the pooled OR of 346 patients was 1.82 (95% CI [0.99–3.36]; p=0.053; Figure 4). Complications were reported in all three studies with a pooled OR of 1.39 (95% CI [0.43–4.51]; p=0.581; Figure 5).
Quality Assessment All three studies were judged by the investigators to be at high risk of bias in several items. Specifically, although all three studies were registered randomised trials, only VTACH and SMS used a computer processing sequence for randomisation. Patients in the SMS study were block randomised and subrandomised using the SAS software according to the following clinical characteristics: ejection fraction (EF) >30% versus EF <30%, amiodarone versus no amiodarone treatment and beta-blocker versus no beta-blocker treatment. Indeed, the generation of the randomisation sequence in the SMASH-VT trial was evaluated as suboptimal due to the use of envelopes. Also, performance and detection bias have been judged at high risk in all three studies; considering the endpoints and the type of intervention, it may have been difficult to achieve blinding of the participants. However, none of the studies discussed or adopted measures to overcome this issue. Moreover, all of them had the same physicians performing the procedure and assessing long-term outcomes. Both investigators agreed that the data collection and results were accurately described. Consequently, reporting and attrition bias were considered at low risk. Finally, different techniques and technologies of VT ablation were utilised in the respective studies. In addition, differing procedural endpoints might have influenced the outcome and success in the three trials; these intrinsic technical aspects were judged to be at high risk of bias by both investigators. The risk of bias evaluation for all three studies is outlined in Supplementary Material Figure 2.
Discussion The primary finding of our meta-analysis can be summarised as follows. Catheter ablation of VT after a first episode of VT in patients with ischaemic cardiomyopathy undergoing ICD implantation is effective in achieving a significant reduction in the occurrence of an appropriate ICD shock. Considering the mean rate of appropriate shock in the overall study population, an OR of 2.58 corresponds to an 84% increase in the relative risk of shock in the group that underwent ICD implantation without preventive VT ablation. Furthermore, patients without VT catheter ablation experienced a significantly higher risk of any appropriate ICD therapy compared with patients with VT ablation (OR 2.04; 95% CI [1.15–3.61]). With respect to the incidence of arrhythmic storm, a trend towards an increased risk was observed in the group without VT ablation without reaching statistical significance. Of note, there was no difference between the two approaches with respect to mortality. Regarding complications during short- and long-term follow up, no differences could be observed between the two groups. Myocardial scarring associated with ischaemic heart disease acts as the underlying substrate for ventricular arrhythmias. The semi-vital and stunned myocytes scattered in between fibrosis or myocardial scarring generate an arrhythmic milieu for the re-entrant mechanism, enhanced automaticity and triggered activity, which can give rise to ventricular arrhythmias.18–20 It is well known that ICDs do not modify the underlying arrhythmic substrate, although effectively treating ventricular arrhythmias and preventing sudden cardiac death; in other words, it does not itself prevent the occurrence of arrhythmias. Of note, to date, there is no diagnostic test available that can reliably predict the occurrence of arrhythmias according to the characterisation of the underlying arrhythmogenic substrate. In fact, the onset of ventricular arrhythmias is unpredictable and not related to the size of the scar. For more than two decades, the ICD indication in patients with ischaemic heart disease has been based on the Multicentre Automatic Defibrillator Implantation Trial (MADIT) and Antiarrhythmics Versus Implantable Defibrillators (AVID) trial.21,22 As a result, a relevant number of patients with ischaemic heart disease worldwide are implanted with an ICD following the detection of an EF <30%. However, the long-term prognosis of these patients is
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Preventive Ventricular Tachycardia Ablation in Patients with Ischaemic Cardiomyopathy Figure 3: Individual and Pooled OR for Death
OR (95% CI)
Ablation plus ICD Weight
Overall (I-squared=0.0%, p=0.516)
NOTE: Weights are from random effects analysis .1
Ablation plus ICD
Individual and pooled OR for death in the two groups, catheter ablation plus ICD and ICD only (p=0.422).
Figure 4: Individual and Pooled OR for Arrhythmic Storm Events, Study
OR (95% CI)
Ablation plus ICD Weight
Overall (I-squared=0.0%, p=0.445)
NOTE: Weights are from random effects analysis .1
1 Events Ablation plus ICD
10 Events ICD only
Individual and pooled OR for arrhythmic storm in the two groups, catheter ablation plus ICD and ICD only (p=0.053).
afflicted by the sequence of ventricular arrhythmia recurrences, ICD shocks and increased mortality. In this scenario, the VTACH, SMASH-VT and SMS trials represent a major breakthrough. All three trials tested the effectiveness and safety of an ablation strategy aiming to modify the underlying arrhythmic substrate in patients undergoing ICD implantation. Of note, the reduction of arrhythmic events achieved with this preventive ablation approach is relevant, as outlined by our analysis. In addition, preventive ablation did not result in a higher complication rate. Periprocedural complications may occur with VT ablation, particularly acute haemodynamic decompensation, given the multiple comorbidities usually found in patients with ischaemic cardiomyopathy and the haemodynamic effects of VT.23 Muser et al. developed a score to predict the risk of complications
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in patients undergoing ventricular tachycardia ablation. The main components of this score were: age, ischaemic cardiomyopathy, pulmonary disease, diabetes, New York Heart Association class, left ventricular EF and VT storm. Patients with VT have a higher burden of comorbidities, which increases procedural complications and mortality. The authors demonstrated that such risk may be mitigated by the use of mechanical support in selected cases.23 Recently, Atti et al. published another meta-analysis comparing the use of VT ablation before ICD implantation versus ICD implantation only, pooling data from the same studies.24 Despite showing similar results, we believe that our analytical approach in treating the OR and the direction of the comparison could be more helpful in streamlining the risks associated with the use of ICD implantation without the use of
Electrophysiology and Ablation Figure 5: Individual and Pooled OR for Complications Events, Study
OR (95% CI)
ICD only Ablation plus ICD
Overall (I-squared=53.3%, p=0.118)
NOTE: Weights are from random effects analysis .1 ICD only
10 Ablation plus ICD
Individual and pooled OR for complications in the two groups, catheter ablation plus ICD and ICD only.
prophylactic VT ablation, and ultimately provide more clinically useful information in the decision-making process for the current daily practice. Although this meta-analysis could show a significant reduction of ICD shocks and appropriate ICD therapies in patients undergoing preventive VT ablation, this did not result in a significantly lower mortality rate. Although there was a trend towards a lower mortality rate in the preventive ablation group, it did not reach statistical significance. Higher patient numbers and/or a longer follow-up duration are needed to evaluate the impact of VT ablation on mortality. In addition, novel ablation strategies, such as LAVA elimination, dechannelling, substrate isolation or scar homogenisation, might have resulted in a better outcome and a lower mortality rate.25–31 Indeed, there are unsolved questions (patient selection, timing of ablation, endpoints of the procedure), as noted by Mukherjee et al., for which current trial data does not provide enough evidence to advocate for prophylactic VT ablation.32 Future trials, such as the ongoing preventive aBlation of vEntricular tachycaRdia in patients with MyocardiaL INfarction (BERLIN VT; NCT02501005) trial, will evaluate whether preventive catheter ablation is superior to deferred catheter ablation.
Limitations The main limitation of this analysis relates to the small number of patients randomised in these studies, which do not allow performing subanalyses of the groups at higher risk, such as patients with EF <30%. Another limitation is the short duration of follow up that may be inadequate to correctly assess hard endpoints, such as mortality. In addition, there is heterogeneity among the studies regarding the patients included, the type of intervention performed and the assessment of outcomes. Last, but not least, the low enrolment rates (e.g. <2 patients per year per institution in the SMS study) resulted in very long study duration and technological improvements with respect to ablation technologies and techniques over time that might influence outcome.
Conclusion In patients with ischaemic heart disease and secondary prevention ICD implantation, preventive catheter ablation results in a significant reduction of appropriate ICD shocks and any appropriate ICD therapy compared with patients without or with deferred VT ablation. Furthermore, there was a trend towards an increased risk for VT storm in patients without VT ablation. No significant difference with respect to complications or mortality was observed between both treatment strategies.
Clinical Perspective • • • •
Preventive VT ablation significantly reduces ICD shocks. Arrhythmic storm may be reduced by preventive VT ablation. Preventive VT ablation reduces all ICD therapy. Prophylactive VT ablation is not associated with significant procedural complications.
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Preventive Ventricular Tachycardia Ablation in Patients with Ischaemic Cardiomyopathy 1.
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j.joa.2014.09.007; PMID: 26336545. 12. K omatsu Y, Maury P, Sacher F, et al. Impact of substratebased ablation of ventricular tachycardia on cardiac mortality in patients with implantable cardioverter-defibrillators. J Cardiovasc Electrophysiol 2015;26:1230–8. https://doi. org/10.1111/jce.12825; PMID: 26332030. 13. Al-Khatib SM, Daubert JP, Anstrom KJ, et al. Catheter ablation for ventricular tachycardia in patients with an implantable cardioverter defibrillator (CALYPSO) pilot trial. J Cardiovasc Electrophysiol 2015;26:151–7. https://doi.org/10.1111/jce.12567; PMID: 25332150. 14. Bunch TJ, Weiss JP, Crandall BG, et al. Patients treated with catheter ablation for ventricular tachycardia after an ICD shock have lower long-term rates of death and heart failure hospitalization than do patients treated with medical management only. Heart Rhythm 2014;11:533–40. https://doi. org/10.1016/j.hrthm.2013.12.014; PMID: 24333283. 15. Kuck KH, Schaumann A, Eckardt L, et al. Catheter ablation of stable ventricular tachycardia before defibrillator implantation in patients with coronary heart disease (VTACH): a multicentre randomised controlled trial. Lancet 2010;375:31– 40. https://doi.org/10.1016/S0140-6736(09)61755-4; PMID: 20109864. 16. Kuck KH, Tilz RR, Deneke T, et al. Impact of substrate modification by catheter ablation on implantable cardioverterdefibrillator interventions in patients with unstable ventricular arrhythmias and coronary artery disease: results From the Multicenter Randomized Controlled SMS (Substrate Modification Study). Circ Arrhythm Electrophysiol 2017;10:e004422. https://doi.org/10.1161/CIRCEP.116.004422; PMID: 28292751. 17. Reddy VY, Reynolds MR, Neuzil P, et al. Prophylactic catheter ablation for the prevention of defibrillator therapy. N Engl J Med 2007;357;2657–65. https://doi.org/10.1056/NEJMoa065457; PMID: 18160685. 18. de Bakker JM, van Capelle FJ, Janse MJ, et al. Slow conduction in the infarcted human heart. ‘Zigzag’ course of activation. Circulation 1993;88:915–26. https://doi.org/10.1161/01. CIR.88.3.915; PMID: 8353918. 19. Proietti R, Roux JF, Verma A, et al. A historical perspective on the role of functional lines of block in the re-entrant circuit of ventricular tachycardia. Pacing Clin Electrophysiol 2016;39:490–6. https://doi.org/10.1111/pace.12827; PMID: 26852719. 20. El-Sherif N. Electrophysiologic mechanisms of ventricular arrhythmias. Int J Card Imaging 1991;7;141–50. https://doi. org/10.1007/BF01797747; PMID: 1726470. 21. Antiarrhythmics versus Implantable Defibrillators (AVID) Investigators. A comparison of antiarrhythmic-drug therapy with implantable defibrillators in patients resuscitated from near-fatal ventricular arrhythmias. N Engl J Med 1997;337:1576– 83. https://doi.org/10.1056/NEJM199711273372202; PMID: 9411221.
22. M oss AJ, Zareba W, Hall WJ, et al. Prophylactic implantation of a defibrillator in patients with myocardial infarction and reduced ejection fraction. N Engl J Med 2002;346:877–83. https://doi.org/10.1056/NEJMoa013474; PMID: 11907286. 23. Muser D, Castro SA, Liang JJ, Santangeli P. Identifying risk and management of acute haemodynamic decompensation during catheter ablation of ventricular tachycardia. Arrhythm Electrophysiol Rev 2018;7:282–7. https://doi.org/10.15420/ aer.2018.36.3; PMID: 30588317. 24. Atti V, Vuddanda V, Turagam MK, et al. Prophylactic catheter ablation of ventricular tachycardia in ischemic cardiomyopathy: a systematic review and meta-analysis of randomized controlled trials. J Interv Card Electrophysiol 2018;53:207–15. https://doi.org/10.1007/s10840-018-0376-5; PMID: 29680972. 25. Di Biase L, Santangeli P, Burkhardt DJ, et al. Endo-epicardial homogenization of the scar versus limited substrate ablation for the treatment of electrical storms in patients with ischemic cardiomyopathy. J Am Coll Cardiol 2012;60:132–41. https://doi.org/10.1016/j.jacc.2012.03.044; PMID: 22766340. 26. Jais P, Maury P, Khairy P, et al. Elimination of local abnormal ventricular activities: a new end point for substrate modification in patients with scar-related ventricular tachycardia. Circulation 2012;125:2184–96. https://doi. org/10.1161/CIRCULATIONAHA.111.043216; PMID: 22492578. 27. Tilz RR, Makimoto H, Lin T, et al. Electrical isolation of a substrate after myocardial infarction: a novel ablation strategy for unmappable ventricular tachycardias – feasibility and clinical outcome. Europace 2014;16:1040–52. https://doi. org/10.1093/europace/eut419; PMID: 24574495. 28. Vergara P, Trevisi N, Ricco A, et al. Late potentials abolition as an additional technique for reduction of arrhythmia recurrence in scar related ventricular tachycardia ablation. J Cardiovasc Electrophysiol 2012;23:621–27. https://doi. org/10.1111/j.1540-8167.2011.02246.x; PMID: 22486970. 29. Proietti R, Joza J, Essebag V. Therapy for ventricular arrhythmias in structural heart disease: a multifaceted challenge. J Physiol 2016;594:2431–43. https://doi.org/10.1113/ JP270534; PMID: 26621333. 30. Proietti R, Essebag V, Beardsall J, et al. Substrate-guided ablation of haemodynamically tolerated and untolerated ventricular tachycardia in patients with structural heart disease: effect of cardiomyopathy type and acute success on long-term outcome. Europace 2015;17:461–7. https://doi. org/10.1093/europace/euu326; PMID: 25488957. 31. Proietti R, Roux JF, Essebag V. Recent advances in ablation of ventricular tachycardia associated with structural heart disease: overcoming the challenges of functional and fixed barriers. Curr Opin Cardiol 2016;31:64–71. https://doi. org/10.1097/HCO.0000000000000242; PMID: 26627313.
Electrophysiology and Ablation
Relationship Between Obstructive Sleep Apnoea and AF Ghanshyam Shantha, Frank Pelosi and Fred Morady Clinical Cardiac Electrophysiology, University of Michigan, Ann Arbor, MI, US
Abstract With the growing obesity epidemic, the global burden of AF and obstructive sleep apnoea (OSA) is increasing at an alarming rate. Obesity, age, male gender, alcohol consumption, smoking and heart failure are common risk factors for both AF and OSA and they are independently associated with adverse cardiovascular outcomes. Weak evidence from observational studies link OSA to the development of AF. Hypoxia/ hypercapnia, systemic inflammation and autonomic nervous system modulation are biological mechanisms that link OSA to AF. Patients with OSA have a poor response to catheter ablation of AF and often suffer recurrences. Observational data shows that continuous positive airway pressure is associated with a reduction in AF burden and a better response to catheter ablation of AF. However, prospective randomised studies are needed to confirm the usefulness of continuous positive airway pressure in the treatment of AF in patients with OSA.
Keywords AF, catheter ablation, inflammation, continuous positive airway pressure, obstructive sleep apnoea, outcomes, sleep-disordered breathing Disclosure: The authors have no conflicts of interest to declare. Received: 24 April 2019 Accepted: 17 June 2019 Citation: Arrhythmia & Electrophysiology Review 2019;8(3):180–3. DOI: https://doi.org/10.15420/aer.2019.35.2 Correspondence: Fred Morady, Clinical Cardiac Electrophysiology, University of Michigan, 1500 E Medical Center Dr, Ann Arbor, MI 48109, US. E: email@example.com Open Access: This work is open access under the CC-BY-NC 4.0 License which allows users to copy, redistribute and make derivative works for non-commercial purposes, provided the original work is cited correctly.
Prevalence of AF and Obstructive Sleep Apnoea Nonvalvular AF is the most common sustained arrhythmia, affecting nearly 3 million adult Americans.1–4 By 2050, nearly 12–15 million adults in the US will have AF.1 The global prevalence of obstructive sleep apnoea (OSA) is also increasing. OSA is the most common form of sleep-disordered breathing, affecting 10–15% of the general population (6–9% in women; 17–31% in men).5,6 Nearly 5 million adults in the US will have OSA by 2020.5,6 Hence, both AF and OSA are global public health problems.
Risk Factors Common to AF and Obstructive Sleep Apnoea OSA and AF share multiple risk factors. Obesity seems to be the metabolic disease that strongly links OSA to AF. With nearly 35% of adult Americans being obese, it is estimated that both OSA and AF will increase exponentially in its prevalence.5–7 In addition, other risk factors, including increasing age, male gender, alcohol consumption, cigarette smoking, hypertension and heart failure, increase the risk of developing AF and OSA.8–10 Hence, OSA and AF have a lot in common and it is possible there may be pathophysiological mechanisms that operate to cause both AF and OSA.
Cardiovascular Outcomes Common to AF and Obstructive Sleep Apnoea AF confers a three- to five-fold increase in the risk of stroke, MI and heart failure (HF).11–13 The association between OSA and cardiovascular (CV) outcomes have also been clearly established. OSA is independently associated with an increased risk of stroke.7 In the Wisconsin Sleep Cohort, among 1,522 patients followed for 18 years, patients with untreated OSA had five times the risk of
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CV events when compared with those without OSA.14 In the Sleep Heart Health Study (SHHS), a cross-sectional analysis involving 6,424 patients with sleep studies, OSA was identified as an independent risk factor for CV disease.15 Furthermore, OSA is associated with a higher risk of hypertension, left ventricular hypertrophy and early atherosclerosis.16–18 Among patients with heart failure, OSA and AF are associated with a poor response to cardiac resynchronisation therapy and increased mortality.19,20
Is Obstructive Sleep Apnoea a Risk Factor for AF in the General Population? OSA and AF share common risk factors and are independently associated with similar adverse outcomes, but it has not been proved that OSA causes AF. Several prospective analyses have assessed the association between OSA and AF. In the SHHS, it was observed that OSA/sleep-disordered breathing increased AF risk fourfold, after adjusting for confounders.15 These findings were confirmed by a recent meta-analysis of patients who had coronary artery bypass surgery.21 In another analysis of 3,542 patients who received a sleep study, Gami et al. demonstrated a relationship between OSA severity and AF.22 OSA was associated with a 1.3-fold increase in the risk of AF for every 1-point increase in apnoea-hypopnea index (AHI). This association remained robust in people over the age of 65 years.22 Previous studies have also reported an association between OSA severity and AF frequency, AF persistence and treatment efficacy.23–25
Is Obstructive Sleep Apnoea a Risk Factor for AF in Patients with Heart Failure? Although multiple studies have assessed the association between OSA and AF in patients with HF, most patients in these studies had central
© RADCLIFFE CARDIOLOGY 2019
Obstructive Sleep Apnoea and AF sleep apnoea (CSA) and only some had OSA. Two studies, one involving 81 patients with HF (40% CSA and 11% OSA) and the other with 450 HF patients, reported a robust association between sleep apnoea and AF, but most patients had CSA and the link between OSA and AF was weak.26,27 Similar associations have been reported in patients with systolic HF and in patients with compensated and ambulatory CHF.28,29 However, there also was an over-representation of CSA in these studies, making conclusions about the relationship between OSA and AF non-definitive. Several methodological considerations need to be taken into account when assessing the association between OSA and AF. The diagnosis of OSA in a majority of studies was based on patient reports and screening questionnaires. Hence under- or over-diagnosis of OSA could have been possible. Most studies did not use polysomnography to confirm OSA. Also, inferring a causal association should be avoided because of the possibility of confounding variables that were not taken into account in these observational studies.
Mechanism of AF in Patients with Obstructive Sleep Apnoea There are several possible explanations for the triggers and substrate of AF: the multiple wavelet theory where multiple wavelets of activation coalesce and spread across the atrium; spontaneous pulmonary vein ectopy; atrial sources of repetitive spiralling activity referred to as rotors; and focal sources of rapid activation.30–34 Intramural micro re-entry enabled by discontinuous atrial myocardial fibres and anatomic or functional conduction barriers help to explain the substrate that maintains AF.35–37 Several other factors such as hypoxia, hypercapnia, fluctuations in autonomic tone, atrial stretch and inflammatory factors may contribute to the pathogenesis of AF alongside OSA.
Hypoxia/Hypercapnia as Triggers to AF in Patients with Obstructive Sleep Apnoea Patients with poorly controlled OSA have repetitive episodes of hypoxia/hypercapnia while experiencing apnoea, and then normal oxygenation when woken up by the episode. These repetitive episodes form an electrophysiological milieu for AF generation. Animal studies have shown that hypoxic periods enable slow atrial conduction and increased atrial refractoriness.38,39 Hypercapnia that accompanies hypoxic episodes accentuates these atrial conduction changes.38 Clinical studies have shown that the magnitude of nocturnal desaturation is proportional to the risk of AF, and there is a relationship between the severity of OSA and the risk of AF (AHI 5–15 is associated with a twofold higher risk of AF, whereas AHI >15 is associated with an almost sixfold higher risk of AF).40,41 These data add strength to the hypoxia/hypercapnia theory of AF in patients with OSA.
In our previous work, in support of this hypothesis, we showed that renal sympathetic denervation, a therapeutic modality that severs sympathetic nerves around the kidney, had the potential to reduce the AHI and hence improve OSA.42 The autonomic nervous system perturbations also increase blood pressure and cardiac afterload. These changes lead to diastolic dysfunction and significant atrial stretch.43 Atrial stretch leads to slow conduction, areas of low voltage denoting atrial scar, decreased sinus node reserve, and pockets of excitability, predisposing to both AF initiation and maintenance.44–47 In addition, the negative tracheal pressure that occurs during episodes of apnoea is known to modify the atrial effective refractory period and increase AF inducibility.48
Role of Inflammation in AF and Obstructive Sleep Apnoea Patients with OSA are known to have enhanced systemic inflammation. Inflammatory markers including C-reactive protein, tumour necrosis factor-alpha and interleukin-6 are elevated in patients with OSA and are known to correlate with OSA severity.49–51 Likewise, there are clinical and histological data to support the role of inflammation in promoting AF.52–54 Inflammation seems to correlate with the quantity of atrial scar and remodelling.
Obesity and Adiposity in AF and Obstructive Sleep Apnoea As mentioned above, an obesity epidemic runs in parallel to the increasing prevalence of AF and OSA. Obesity is a state of sympathetic excess and significant systemic inflammation and several mechanisms have been identified.55 Hence, obesity plays a central link between OSA and AF. Sustained weight loss abates these pathogenic pathways. Weight loss consistently reduces OSA severity and improves AF burden, AF symptom scores and potentially reverses cardiac remodelling.56,57
Negative Thoracic Pressure, Obstructive Sleep Apnoea and AF Pathogenesis Observations from studies using animal models show that negative intrathoracic pressure (NTP) created by an obstructed upper airway, has the potential to increase AF susceptibility.58,59 NTP is associated with a profound shortening of atrial effective refractory period and increases AF susceptibility. Further, recurrent and chronic NTP may lead to atrial remodelling by altering connexin junctions in atrial myocytes leading to conduction abnormalities and AF susceptibility.
Obstructive Sleep Apnoea Treatment and AF Burden Continuous positive airway pressure (CPAP) is the most common treatment modality for OSA. Multiple small observational studies have assessed the utility of CPAP in reducing AF burden.60–63 Although limited by methodology issues and small sample sizes, these studies largely support the view that CPAP therapy improves AF burden. This is independent of the modality used for rhythm control, including antiarrhythmic drug therapy, direct current cardioversion or catheter ablation.64–66
Autonomic Nervous System Modulation in AF Repetitive episodes of apnoea which wake up patients with OSA stimulates the sympathetic nervous system and can cause catecholamine surges.42 This stimulates the renin–angiotensin–aldosterone system which in turn leads to fluid retention. Parapharyngeal fluid collection seems to accentuate OSA.42 Hence, patients with OSA go through a vicious circle of autonomic nervous system stimulation, fluid retention and worsening OSA.
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In the Outcomes Registry for Better Informed Treatment for Atrial Fibrillation (ORBIT-AF) registry, 1,841 of the 10,132 AF patients had OSA.63 These patients had worse symptoms and required more hospitalisations. CPAP therapy reduced the risk of transition from paroxysmal to persistent AF (HR 0.66). However, large randomised studies are needed before firm conclusions can be made regarding the effects of CPAP on AF.
Electrophysiology and Ablation Catheter Ablation for AF in Patients with Obstructive Sleep Apnoea OSA is a risk factor for AF recurrence after catheter ablation (CA) of AF independent of comorbid conditions including obesity and left atrial size.60,61 In a study by Anter et al., 43 patients with paroxysmal AF and recently diagnosed, previously untreated, moderate-to-severe OSA underwent pulmonary vein isolation and ablation of residual triggers.67 A similar ablation approach was used in a group of 43 patients with paroxysmal AF who had a recent normal sleep study. These two groups were compared with control groups of patients with and without OSA in whom only pulmonary vein isolation was performed without any attempt to eliminate residual AF triggers. The authors found a high prevalence of low voltage areas and abnormal electrograms in both atria in patients with OSA. An increased amount of atrial fibrosis in patients with OSA has been reported in other clinical and experimental studies.68,69 CPAP therapy is noted to improve outcomes after CA of AF. A prospective evaluation of 62 patients with OSA undergoing CA of AF showed that CPAP therapy showed superior single (66% versus 33%) and multiple (72% versus 37%) procedure success rates.70 Furthermore, the higher the severity of OSA, the higher the rate of AF recurrence at 1 year (mild OSA 66% versus moderate OSA 58%, versus severe OSA 82%).23 CPAP appears to improve outcomes in patients with all grades of OSA severity. Pooled analyses shows that patients with OSA have nearly a 30% increased risk of recurrence after CA of AF compared with those without OSA.71,72 CPAP use reduced risk of recurrent AF by 55–58%.71,72 Another interesting observation is that in patients with AF and OSA undergoing cavo-tricuspid isthumus ablation of typical atrial flutter, the AF recurrence rate was significantly reduced in the group of CPAP users.73 Since it is believed that AF and atrial flutter may have common triggers, this study may support the idea that CPAP use
accarelli GV, Varker H, Lin J, Schulman KL. Increasing N prevalence of atrial fibrillation and flutter in the United States. Am J Cardiol 2009;104:1534–9. https://doi.org/10.1016/j. amjcard.2009.07.022; PMID: 19932788. 2. Fuster V, Rydén LE, Cannom DS, et al. 2011 ACCF/AHA/ HRS focused updates incorporated into the ACC/AHA/ESC 2006 Guidelines for the management of patients with atrial fibrillation. J Am Coll Cardiol 2011;57:e101–98. https://doi. org/10.1161/CIR.0b013e318214876d; PMID: 21392637. 3. Stewart S, Hart CL, Hole DJ, et al. A population-based study of the long-term risks associated with atrial fibrillation: 20-year follow-up of the Renfrew/Paisley study. Am J Med 2002;113:359–64. https://doi.org/10.1016/S00029343(02)01236-6; PMID: 12401529. 4. Friberg L, Hammar N, Pettersson H, et al. Increased mortality in paroxysmal atrial fibrillation: report from the Stockholm Cohort-Study of Atrial Fibrillation (SCAF). Eur Heart J 2007;28:2346–53. https://doi.org/10.1093/eurheartj/ehm308; PMID: 17670754. 5. Duran J, Esnaola S, Rubio R, et al. Obstructive sleep apnea– hypopnea and related clinical features in a populationbased sample of subjects aged 30 to 70 yr. Am J Respir Crit Care Med 2002;163:685–9. https://doi.org/10.1164/ ajrccm.163.3.2005065; PMID: 11254524. 6. Punjabi NM. The epidemiology of adult obstructive sleep apnea. Proc Am Thorac Soc 2008;5:136–43. https://doi. org/10.1513/pats.200709-155MG; PMID: 18250205. 7. Loke YK, Brown JW, Kwok CS, et al. Association of obstructive sleep apnea with risk of serious cardiovascular events: a systematic review and meta-analysis. Circ Cardiovasc Qual Outcome 2012;5:720–8. https://doi.org/10.1161/ CIRCOUTCOMES.111.964783; PMID: 22828826. 8. Chamberlain AM, Alonso A, Gersh BJ, et al. Multimorbidity and the risk of hospitalization and death in atrial fibrillation: a population-based study. Am Heart J 2017;185:74–84. https:// doi.org/10.1016/j.ahj.2016.11.008; PMID: 28267478. 9. Alonso A, Krijthe BP, Aspelund T, et al. Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium. J Am Heart Assoc 2013;2:e000102. https://doi. org/10.1161/JAHA.112.000102; PMID: 23537808. 10. Schnabel RB, Sullivan LM, Levy D, et al. Development of a risk score for atrial fibrillation (Framingham Heart Study): a community-based cohort study. Lancet 2009;373:739–45.
may reduce AF triggers. However, these were observational cohort studies, not prospective randomised studies, hence the strength of the evidence is not strong.
Unresolved Issues Recent evidence has shown that patients with OSA tend to have a hypercoagulable state.74 Hence, it may be worthwhile to assess if OSA should be considered an independent risk factor for stroke in patients with AF and if the efficacy and safety of oral anticoagulants in patients with AF is affected by OSA. Guidelines conflict on the use of screening tools, with the American College of Physicians recommending the use of tools such as the STOP-Band and Berlin questionnaires to screen for OSA while the United States Preventative Services Task Force guidelines do not. However, the screening tools do have significant predictive accuracy (sensitivity for moderate-to-severe OSA [AHI >15] is 93%, with negative predictive value of 90%).75,76 The sensitivity to detect severe OSA (AHI >30) is 100%). Future studies should try to assess the predictive accuracy of these tools to screen for OSA in patients with AF because they represent a high-risk population.
Clinical Perspective • The global burden of AF and obstructive sleep apnoea are increasing as the obesity epidemic worsens. • AF and obstructive sleep apnoea have common risk factors. • AF and obstructive sleep apnoea are independently associated with adverse cardiovascular outcomes. • Biological mechanisms link obstructive sleep apnoea to the development of AF. • Patients with obstructive sleep apnoea have a poor response to catheter ablation of AF.
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2018;15:1283–8. https://doi.org/10.1016/j.hrthm.2018.06.016; PMID: 30170662. Qaddoura A, Kabali C, Drew D, et al. Obstructive sleep apnea as a predictor of atrial fibrillation after coronary artery bypass grafting: A systematic review and meta‐analysis. Can J Cardiol 2014;30:1516–22. https://doi.org/10.1016/j.cjca.2014.10.014; PMID: 25475456. Gami AS, Hodge DO, Herges RM, et al: Obstructive sleep apnea, obesity, and the risk of incident atrial fibrillation. J Am Coll Cardiol 2007;49:565–71. https://doi.org/10.1016/j. jacc.2006.08.060; PMID: 17276180. Szymanski FM, Filipiak KJ, Platek AE, et al. Presence and severity of obstructive sleep apnea and remote outcomes of atrial fibrillation ablations-a long-term prospective, crosssectional cohort study. Sleep Breath 2015;19:849–56. https:// doi.org/10.1007/s11325-014-1102-x; PMID: 25566942. Stevenson IH, Teichtahl H, Cunnington D, et al. Prevalence of sleep disordered breathing in paroxysmal and persistent atrial fibrillation patients with normal left ventricular function. Eur Heart J 2008;29:1662–9. https://doi.org/10.1093/eurheartj/ ehn214; PMID: 18515807. Monahan K, Brewster J, Wang L, et al. Relation of the severity of obstructive sleep apnea in response to anti-arrhythmic drugs in patients with atrial fibrillation or atrial flutter. Am J Cardiol 2012;110:369–72. https://doi.org/10.1016/j. amjcard.2012.03.037; PMID: 22516529. Javaheri S, Parker TJ, Liming JD, et. al. Sleep apnea in 81 ambulatory male patients with stable heart failure. Types and their prevalences, consequences, and presentations. Circulation 1998;97:2154–9. https://doi.org/10.1161/01.CIR.97.21.2154; PMID: 9626176. Sin DD, Fitzgerald F, Parker JD, et al. Risk factors for central and obstructive sleep apnea in 450 men and women with congestive heart failure. Am J Respir Crit Care Med 1999;160:1101–6. https://doi.org/10.1164/ ajrccm.160.4.9903020; PMID: 10508793. Javaheri S. Sleep disorders in systolic heart failure: A prospective study of 100 male patients. The final report. Int J Cardiol 2006;106:21–8. https://doi.org/10.1016/j. ijcard.2004.12.068; PMID: 16321661. Ferrier K, Campbell A, Yee B, et al. Sleep-disordered breathing occurs frequently in stable outpatients with congestive heart failure. Chest 2005;128:2116–22. https://doi.org/10.1378/ chest.128.4.2116; PMID: 16236863.
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Obstructive Sleep Apnoea and AF 30. A llessie M, Lammers W, Bonke F, et al. Experimental evaluation of Moe’s multiple wavelet hypothesis of atrial fibrillation. In: Zipes DP, Jalife J (eds). Cardiac Electrophysiology and Arrhythmias. NY: Grune & Stratton, 1985;265–75. 31. Haïssaguerre M, Jais P, Shah DC, et al. Spontaneous initiation of atrial fibrillation by ectopic beats originating in the pulmonary veins. N Engl J Med 1998;339:659–66. https://doi. org/10.1056/NEJM199809033391003; PMID: 9725923. 32. Sanders P, Nalliah CJ, Dubois R, et al. Frequency mapping of the pulmonary veins in paroxysmal versus permanent atrial fibrillation. J Cardiovasc Electrophysiol 2006;17:965–72. https://doi. org/10.1111/j.1540-8167.2006.00546.x; PMID: 16948740. 33. Narayan SM, Baykaner T, Clopton P, et al. Ablation of rotor and focal sources reduces late recurrence of atrial fibrillation compared with trigger ablation alone: Extended follow-up of the confirm trial (conventional ablation for atrial fibrillation with or without focal impulse and rotor modulation). J Am Coll Cardiol 2014;63:1761–8. https://doi.org/10.1016/j. jacc.2014.02.543; PMID: 24632280. 34. Haissaguerre M, Hocini M, Denis A, et al. Driver domains in persistent atrial fibrillation. Circulation 2014;130:530–8. https://doi.org/10.1161/CIRCULATIONAHA.113.005421; PMID: 25028391. 35. Hansen BJ, Zhao J, Csepe TA, et al. Atrial fibrillation driven by micro-anatomic intramural re-entry revealed by simultaneous sub-epicardial and sub-endocardial optical mapping in explanted human hearts. Eur Heart J 2015;36:2390–401. https://doi.org/10.1093/eurheartj/ehv233; PMID: 26059724. 36. Shamsuzzaman AS, Winnicki M, Lanfranchi P, et al. Elevated C-reactive protein in patients with obstructive sleep apnea. Circulation 2002;105:2462–4. https://doi.org/10.1161/01. CIR.0000018948.95175.03; PMID: 12034649. 37. Tanigawa T, Yamagishi K, Sakurai S, et al. Arterial oxygen desaturation during sleep and atrial fibrillation. Heart 2006;92:1854–5. https://doi.org/10.1136/hrt.2005.081257; PMID: 17105888. 38. Stevenson IH, Roberts-Thomson KC, Kistler PM, et al. Atrial electrophysiology is altered by acute hypercapnia but not hypoxemia: Implications for promotion of atrial fibrillation in pulmonary disease and sleep apnea. Heart Rhythm 2010;7:1263–70. https://doi.org/10.1016/j.hrthm.2010.03.020; PMID: 20338265. 39. Iwasaki YK, Kato T, Xiong F, et al. Atrial fibrillation promotion with long-term repetitive obstructive sleep apnea in a rat model. J Am Coll Cardiol 2014;64:2013–23. https://doi. org/10.1016/j.jacc.2014.05.077; PMID: 25440097. 40. Mehra R, Benjamin EJ, Shahar E, et al. Association of nocturnal arrhythmias with sleep-disordered breathing: The Sleep Heart Health Study. Am J Respir Crit Care Med 2006;173:910–6. https://doi.org/10.1164/rccm.2005091442OC; PMID: 16424443. 41. Tanigawa T, Yamagishi K, Sakurai S, et al. Arterial oxygen desaturation during sleep and atrial fibrillation. Heart 2006;92:1854–5. https://doi.org/10.1136/hrt.2005.081257; PMID: 17105888. 42. Shantha GP, Pancholy SB. Effect of renal sympathetic denervation on apnea-hypopnea index in patients with obstructive sleep apnea: a systematic review and metaanalysis. Sleep Breath 2015;19:29–34. https://doi.org/10.1007/ s11325-014-0991-z; PMID: 24839239. 43. Arias MA, Garcia-Rio F, Alonso-Fernandez A, et al. Obstructive sleep apnea syndrome affects left ventricular diastolic function: Effects of nasal continuous positive airway pressure in men. Circulation 2005;112:375–83. https://doi.org/10.1161/ CIRCULATIONAHA.104.501841; PMID: 16009798. 44. Sanders P, Morton JB, Davidson NC, et al. Electrical remodeling of the atria in congestive heart failure: Electrophysiological and electroanatomic mapping in humans. Circulation 2003;108:1461–8. https://doi.org/10.1161/01. CIR.0000090688.49283.67; PMID: 12952837. 45. Sanders P, Morton JB, Kistler PM, et al. Electrophysiological and electroanatomic characterization of the atria in sinus node disease: Evidence of diffuse atrial remodeling.
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Circulation 2004;109:1514–22. https://doi.org/10.1161/01. CIR.0000121734.47409.AA; PMID: 15007004. Sanders P, Kistler PM, Morton JB, et al. Remodeling of sinus node function in patients with congestive heart failure: Reduction in sinus node reserve. Circulation 2004;110:897–903. https://doi.org/10.1161/01.CIR.0000139336.69955.AB; PMID: 15302799. Morton JB, Sanders P, Vohra JK, et al. Effect of chronic right atrial stretch on atrial electrical remodeling in patients with an atrial septal defect. Circulation 2003;107:1775–82. https://doi. org/10.1161/01.CIR.0000058164.68127.F2; PMID: 12665497. Linz D, Schotten U, Neuberger HR, et al. Negative tracheal pressure during obstructive respiratory events promotes atrial fibrillation by vagal activation. Heart Rhythm 2011; 8:1436–43. https://doi.org/10.1016/j.hrthm.2011.03.053; PMID: 21457790. Vgontzas AN, Papanicolaou DA, Bixler EO, et al. Elevation of plasma cytokines in disorders of excessive daytime sleepiness: role of sleep disturbance and obesity. J Clin Endocrinol Metab 1997;82:1313–6. https://doi.org/10.1210/ jcem.82.5.3950; PMID: 9141509. Vgontzas AN, Papanicolaou DA, Bixler EO, et al. Sleep apnea and daytime sleepiness and fatigue: Relation to visceral obesity, insulin resistance, and hypercytokinemia. J Clin Endocrinol Metab 2000;85:1151–8. https://doi.org/10.1210/ jcem.85.3.6484; PMID: 10720054. Zouaoui Boudjeltia K, Van Meerhaeghe A, Doumit S, et al. Sleep apnoea hypopnea index is an independent predictor of high-sensitivity creactive protein elevation. Respiration 2006;73:243–6. https://doi.org/10.1159/000090201; PMID: 16549947. Boldt A, Wetzel U, Lauschke J, et. al. Fibrosis in left atrial tissue of patients with atrial fibrillation with and without underlying mitral valve disease. Heart 2004;90:400–5. https:// doi.org/10.1136/hrt.2003.015347; PMID: 15020515. Platonov PG, Mitrofanova LB, Orshanskaya V, et al. Structural abnormalities in atrial walls are associated with presence and persistency of atrial fibrillation but not with age. J Am Coll Cardiol 2011;58:2225–32. https://doi.org/10.1016/j. jacc.2011.05.061; PMID: 22078429. Swartz MF, Fink GW, Sarwar MF, et al. Elevated pre-operative serum peptides for collagen I and III synthesis result in postsurgical atrial fibrillation. J Am Coll Cardiol 2012;60:1799–806. https://doi.org/10.1016/j.jacc.2012.06.048. PMID: 23040566. Guarino D, Nannipieri M, Iervasi G, et al. The role of the autonomic nervous system in the pathophysiology of obesity. Front Physiol 2017;8:665–7. https://doi.org/10.3389/ fphys.2017.00665; PMID: 28966594. Iftikhar IH, Bittencourt L, Youngstedt SD, et al. Comparative efficacy of CPAP, MADs, exercise-training, and dietary weight loss for sleep apnea: a network meta-analysis. Sleep Med 2017;30:7–14. https://doi.org/10.1016/j.sleep.2016.06.001; PMID: 28215266. Pathak RK, Middeldorp ME, Lau DH, et al. Aggressive risk factor reduction study for atrial fibrillation and implications for the outcome of ablation: The ARREST-AF cohort study. J Am Coll Cardiol 2014;64:2222–31. https://doi.org/10.1016/j. jacc.2014.09.028; PMID: 25456757. Linz D, Wirth K. Intrathoracic pressure oscillations during obstructive apneas disturb ventricular repolarisation. Eur J Appl Physiol 2012;112:4181–4. https://doi.org/10.1007/s00421-0122485-7; PMID: 22949051. Iwasaki YK, Kato T, Xiong F, et al. Atrial fibrillation promotion with long-term repetitive obstructive sleep apnea in a rat model. J Am Coll Cardiol 64:2013–23. https://doi.org/10.1016/j. jacc.2014.05.077; PMID: 25440097. Jongnarangsin K, Chugh A, Good E, et al. Body mass index, obstructive sleep apnea, and outcomes of catheter ablation of atrial fibrillation. J Cardiovasc Electrophysiol 2008;19:668–72. https://doi.org/10.1111/j.1540-8167.2008.01118.x; PMID: 18363693. Patel D, Mohanty P, Di Biase L, et al. Safety and efficacy of pulmonary vein antral isolation in patients with obstructive
sleep apnea: The impact of continuous positive airway pressure. Circ Arrhythm Electrophysiol 2010;3:445–51. https://doi. org/10.1161/CIRCEP.109.858381; PMID: 20689107. Fein AS, Shvilkin A, Shah D, et al. Treatment of obstructive sleep apnea reduces the risk of atrial fibrillation recurrence after catheter ablation. J Am Coll Cardiol 2013;62:300–5. https:// doi.org/10.1016/j.jacc.2013.03.052; PMID: 23623910. Holmqvist F, Guan N, Zhu Z, et al. Impact of obstructive sleep apnea and continuous positive airway pressure therapy on outcomes in patients with atrial fibrillation – results from the Outcomes Registry for Better Informed Treatment of Atrial Fibrillation (ORBIT-AF). Am Heart J 2015;169:647–54. https://doi.org/10.1016/j.ahj.2014.12.024; PMID: 25965712. Monahan K, Brewster J, Wang L, et al. Relation of the severity of obstructive sleep apnea in response to anti-arrhythmic drugs in patients with atrial fibrillation or atrial flutter. Am J Cardiol 2012;110:369–72. https://doi.org/10.1016/j. amjcard.2012.03.037; PMID: 22516529. Kanagala R, Murali NS, Friedman PA, et al. Obstructive sleep apnea and the recurrence of atrial fibrillation. Circulation 2003;107:2589–94. https://doi.org/10.1161/01. CIR.0000068337.25994.21; PMID: 12743002. Mohanty S, Mohanty P, Di Biase L, et al. Long-term outcome of catheter ablation in atrial fibrillation patients with coexistent metabolic syndrome and obstructive sleep apnea: impact of repeat procedures versus lifestyle changes. J Cardiovasc Electrophysiol 2014;25:930–8. https://doi.org/10.1111/jce.12468; PMID: 24903158. Anter E, Di Biase L, Contreras-Valdes FM, et al. Atrial substrate and triggers of paroxysmal atrial fibrillation in patients with obstructive sleep apnea. Circ Arrhythm Electrophysiol 2017;10:e005407. https://doi.org/10.1161/ CIRCEP.117.005407; PMID: 29133380. Dimitri H, Ng M, Brooks AG, et al. Atrial remodeling in obstructive sleep apnea: implications for atrial fibrillation. Heart Rhythm 2012;9:321–7. https://doi.org/10.1016/j. hrthm.2011.10.017; PMID: 22016075. Ramos P, Rubies C, Torres M, et al. Atrial fibrosis in a chronic murine model of obstructive sleep apnea: mechanisms and prevention by mesenchymal stem cells. Respir Res 2014;15:54–8. https://doi.org/10.1186/1465-9921-1554; PMID: 24775918. Fein AS, Shvilkin A, Shah D, et al. Treatment of obstructive sleep apnea reduces the risk of atrial fibrillation recurrence after catheter ablation. J Am Coll Cardiol 2013;62:300–5. https:// doi.org/10.1016/j.jacc.2013.03.052; PMID: 23623910. Li L, Wang ZW, Li J et al. Efficacy of catheter ablation of atrial fibrillation in patients with obstructive sleep apnoea with and without continuous positive airway pressure treatment: A meta-analysis of observational studies. Europace 2014;16:1309–14. https://doi.org/10.1093/europace/euu066; PMID: 24696222. Shukla A, Aizer A, Holmes D, et al. Effect of obstructive sleep apnea treatment on atrial fibrillation recurrence: A meta-analysis. J Am Coll Cardiol 2015;1:41–51. https://doi. org/10.1016/j.jacep.2015.02.014; PMID: 29759338. Bazan V, Grau N, Valles E, et al. Obstructive sleep apnea in patients with typical atrial flutter: Prevalence and impact on arrhythmia control outcome. Chest 2013;143:1277–83. https://doi.org/10.1378/chest.12-0697; PMID: 23117936. Hong SN, Yun HC, Yoo JH, et al. Association between hypercoagulability and severe obstructive sleep apnea. JAMA Otolaryngol Head Neck Surg 2017;143:996–1002. https://doi. org/10.1001/jamaoto.2017.1367; PMID: 28817760. Farney RJ, Walker BS, Farney RM, et al. The STOP-Bang equivalent model and prediction of severity of obstructive sleep apnea: relation to polysomnographic measurements of the apnea/hypopnea index. J Clin Sleep Med 2011;7:459–65. https://doi.org/10.5664/jcsm.1306 PMID: 22003340. Khan A, Patel J, Sharma D, et al. Obstructive sleep apnea screening in patients with atrial fibrillation: missed opportunities for early diagnosis. J Clin Med Res 2018;11:21–5. https://doi.org/10.14740/jocmr3635; PMID: 30627274.
Electrophysiology and Ablation
Individualised Approaches for Catheter Ablation of AF: Patient Selection and Procedural Endpoints Nicolas Johner Mehdi Namdar and Dipen C Shah Cardiology Division, University Hospital of Geneva, Geneva, Switzerland
Abstract Pulmonary vein isolation (PVI) is the cornerstone of AF ablation, but studies have reported improved efficacy with high rates of repeat procedures. Because of the large interindividual variability in the underlying electrical and anatomical substrate, achieving optimal outcomes requires an individualised approach. This includes optimal candidate selection as well as defined ablation strategies with objective procedure endpoints beyond PVI. Candidate selection is traditionally based on coarse and sometimes arbitrary clinical stratification such as AF type, but finer predictors of treatment efficacy including biomarkers, advanced imaging and electrocardiographic parameters have shown promise. Numerous ancillary ablation strategies beyond PVI have been investigated, but the absence of a clear mechanistic and evidence-based endpoint, unlike in other arrhythmias, has remained a universal limitation. Potential endpoints include functional ones such as AF termination or non-inducibility and substrate-based endpoints such as isolation of low-voltage areas. This review summarises the relevant literature and proposes guidance for clinical practice and future research.
Keywords AF, catheter ablation, pulmonary vein isolation, individualised, non-inducibility, termination, endpoint, predictor, outcome Disclosure: DCS is supported by the Swiss National Science Foundation. The authors have no conflicts of interest to declare. Received: 24 February 2019 Accepted: 6 June 2019 Citation: Arrhythmia & Electrophysiology Review 2019;8(3):184–90. DOI: https://doi.org/10.15420/aer.2019.33.2 Correspondence: Dipen C Shah, Cardiology Division, University Hospital of Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland. E: firstname.lastname@example.org Open Access: This work is open access under the CC-BY-NC 4.0 License which allows users to copy, redistribute and make derivative works for non-commercial purposes, provided the original work is cited correctly.
Catheter ablation (CA) is a widely used first-line treatment for AF. Randomised controlled trials (RCTs) have shown its superiority to pharmacological treatment in terms of symptom control, rhythm control and mortality in selected patients; observational studies have also suggested a decreased risk of stroke.1,2 Pulmonary vein isolation (PVI) is the standard endpoint, but studies have reported variable and improvable efficacy rates ranging from 20% to 80% depending on the study population.3 In addition, a substantial proportion of patients require repeat procedures. Pre-existing and/or progression of extrapulmonary vein (PV) AF substrate likely plays a significant role in PVI non-responders and numerous substrate modification strategies have been investigated. However, given the large interindividual variability of electrical and anatomical AF substrate, achieving optimal patient outcomes requires individualised management. To this effect, the following key issues need to be addressed by clinicians and researchers: how to identify optimal candidates likely to benefit from AF ablation and which procedure endpoint(s) should be aimed at. This non-systematic review summarises the current literature regarding clinical practice, the most commonly proposed solutions and recent developments.
standing persistent AF patients, respectively.3–5 Continuous duration of persistent AF and duration of AF history also correlate with postablation recurrence, and reports have suggested that ablation early in the course of the disease may prevent progression and even induce reverse atrial remodelling.6–9 As a result, substantial weight is generally accorded to AF type when evaluating the indication for AF ablation, as well as the ablation strategy itself. This is reflected in international recommendations.3–5
Optimal Candidate Selection
AF Risk Factors
Numerous risk factors for arrhythmia recurrence after AF ablation have been reported and are summarised in Supplementary Material Table 1. There is considerable overlap between risk factors for recurrence after AF ablation and risk factors for new-onset AF and progression.11 This suggests that underlying disease progression is involved in AF
AF Type There is overwhelming evidence that the clinical AF phenotype is associated with post-ablation outcome, with progressively higher rates of arrhythmia recurrence for paroxysmal, persistent and long-
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However, current definitions of AF types are derived from historybased arbitrary duration cut-offs rather than from prognostic studies and they lack objective validation with measured AF burden.10 Several large studies reported no association between AF type and recurrent AF after ablation when adjusting for other – likely stronger – predictors such as atrial fibrosis (see the imaging section). Therefore, while AF type is the most well established stratification tool for patient selection, it remains a relatively inaccurate predictor of ablation outcome as currently defined and other individual factors should also be considered.
© RADCLIFFE CARDIOLOGY 2019
Individualised AF Ablation recurrence after ablation. PV reconnection is traditionally considered to be a major cause of recurrence, but a recent meta-analysis found only a weak association, with 58% of AF-free patients also exhibiting PV reconnection.3,12 The importance of underlying disease progression is further supported by studies showing a decreased risk of post-ablation recurrence when treating modifiable AF risk factors such as obesity, hypertension or obstructive sleep apnoea.13
based atrial Fibrillation rhythm control versus rate control Trial in patients with heart failure and high burden Atrial Fibrillation (RAFT-AF; NCT01420393) will likely shed additional light on the issue. It should be noted that the above study populations are likely to be biased towards low-risk HF patients. In this selected population, current international recommendations are to use similar indications for AF ablation as for patients without HF.3
In contrast to this substantial literature, there are scarce data regarding clinical implementation of prognostic factors to guide patient selection for AF ablation. Likewise, few studies have examined hard outcomes following AF ablation. In this regard, it is crucial to consider the impact of sinus rhythm restoration on the course of the disease. For example, patients with progression risk factors may benefit from more aggressive management (with early ablation) to prevent disease progression, despite lower ablation success rates.14,15 Additionally, advances in ablation techniques and technology have led to fewer procedure-related complications, favourably shifting the benefit:harm ratio for increasingly broad populations.16,17
Based on current knowledge and ablation strategies, it may be reasonable to avoid ablation in patients estimated to have very low success probability based on substantial and unmodifiable risk factors. However, as discussed for heart failure (HF) in the next section, it will be crucial to determine if some of these patients may benefit from ablation despite lower success rates. Furthermore, observational data have shown risk factor management (RFM) to decrease the risk of post-ablation AF recurrence to rates comparable to low-risk patients.18 In the Aggressive Risk factor REduction STudy for Atrial Fibrillation and Implications for the Outcome of Ablation (ARREST-AF) observational cohort study, RFM including aggressive management of blood pressure, weight, lipids, glucose, sleep apnoea, smoking and alcohol consumption, was offered to AF patients undergoing CA.13 After an average 42-month follow-up, RFM was associated with a fivefold increase in arrhythmia-free survival compared with controls who underwent CA without structured RFM (87% versus 18%, respectively; p<0.001). While there are no prospective data investigating different ablation strategies in these populations, it should be noted that obstructive sleep apnoea has been associated with an increased prevalence of non-PV triggers.19
Heart Failure The role of CA in the management of AF patients with HF is a complex and rapidly evolving field and a detailed discussion is beyond the scope of this review. Briefly, HF with preserved (HFpEF) or reduced ejection fraction (HFrEF) and AF frequently coexist and are mutual risk factors.20 HF has been reported as a predictor of arrhythmia recurrence after AF ablation.21 However, RCTs and subsequent metaanalyses have demonstrated the efficacy of AF ablation in patients with HFrEF, showing a nearly 50% reduction in mortality compared with medical treatment over follow-ups of up to 60 months.1,22 RCTs also demonstrated an improvement in left ventricular ejection fraction following CA compared with pharmacological treatment.1,22 Early intervention may bring additional benefit: in a meta-analysis of 26 studies including a total of 1,838 HFrEF patients with a mean 23-month follow-up, recurrence rates were lower when ablation was performed earlier after first diagnosis of AF or HF.15 Conclusive data are lacking regarding HFpEF, but observational data suggest similar efficacy.23 Upcoming RCTs such as the Randomized Ablation-
ARRHYTHMIA & ELECTROPHYSIOLOGY REVIEW
Several scores based on clinical parameters have been designed and/or investigated to predict AF recurrence after ablation and are summarised in Supplementary Material Table 2. These multivariable scores remain modestly accurate, with area under the curve (AUC) values commonly in the 0.6–0.7 range, and have not been successfully implemented in prospective management strategies.
Imaging Image-based predictors of post-ablation AF recurrence are summarised in Supplementary Material Table 3. While numerous parameters have been described, most fall into three conceptual categories: measures of left ventricular (LV) dysfunction (discussed above), measures of atrial structural remodelling, and measures of atrial mechanical remodelling.
Imaging Atrial Structural Remodelling Atrial enlargement is a hallmark of atrial cardiomyopathy. A recent meta-analysis of observational studies confirmed that left atrial (LA) volume and LA volume index predicted the risk of AF recurrence after de novo CA regardless of the imaging modality (OR 1.032; 95% CI [1.012–1.052]; p=0.001).24 However, the effect was relatively small: the average difference between patients with and without recurrence was 0.8 ml for LA volume and 0.6 ml/m2 for LA volume index. Therefore, LA size alone does not appear to be a reliable prognostic indicator for clinical decision making. Atrial tissue fibrosis is a prominent feature of atrial structural remodelling in AF patients and an established determinant of AF progression.25 The Salt Lake City group quantified LA fibrosis by delayed-enhancement MRI and demonstrated an independent association with arrhythmia recurrence following AF ablation.26-28 The authors proposed a staging system based on the extent of fibrosis with Utah stages I through IV corresponding to <10%, ≥10%–<20%, ≥20%–<30% and ≥30% of the LA wall volume, respectively. The cumulative incidence of recurrent arrhythmia at one year after ablation was 15%, 33%, 46% and 51% for stages I–IV, respectively.28 At five years, the cumulative incidence of recurrent arrhythmia was 53%, 66%, 72% and 87%, respectively.27 Interestingly, a secondary analysis from the efficacy of Delayed Enhancement-MRI-guided ablation vs. Conventional catheter Ablation of Atrial Fibrillation (DECAAF) cohort found that the extent of preablation LA fibrosis not covered by ablation-induced scar at 3-month post-ablation MRI, or residual fibrosis, was strongly correlated with arrhythmia recurrence. This finding supports the notion that extra-PV AF substrate plays a significant role in AF recurrence after ablation.29 The on-going DECAAF II study will examine the efficacy of adjunctive fibrosis-guided ablation compared with PVI alone to prevent arrhythmia recurrence after AF ablation.
Imaging Atrial Mechanical Remodelling The mechanical function of the LA consists of the reservoir, conduit and booster pump functions, the three of which are altered in AF and have been shown to predict AF recurrence after ablation.30–32 In
Electrophysiology and Ablation a meta-analysis of eight prospective studies, LA strain by 2D speckletracking echocardiography (a measure of the booster pump function) predicted AF recurrence at an average 11.3 months post-ablation with 78% sensitivity, 75% specificity, mean AUC 0.798 (95% CI [0.70–0.94]).30 No study, however, has examined how these measures may be implemented in management strategies.
Electrocardiographic and Electrophysiological Parameters
Electrical Markers of Atrial Cardiomyopathy and Remodelling
A wide range of blood-based biomarkers have been associated with arrhythmia recurrence after AF ablation (Supplementary Material Table 4).33 These include markers of inflammation, myocardial injury, fibrosis and biomarkers associated with comorbidities. Similar to clinical risk factors, there is consistent overlap between markers associated with AF development/progression and markers associated with post-ablation recurrence. Selected biomarkers are discussed here based on novelty or their potential for clinical implementation.
Interatrial block (IAB), defined as a P wave ≥120 ms on any ECG lead, is a frequent condition generally resulting from impaired conduction within Bachmann’s region or adjacent atrial myocardium.47 IAB is associated with atrial fibrosis, LA enlargement and AF risk factors. In addition, IAB per se is a substrate for arrhythmia development and its association with supraventricular tachyarrhythmias is considered to be an arrhythmologic syndrome called Bayés syndrome.48 IAB is an established risk factor for AF recurrence after CA.48 In a recent meta-analysis of eight studies, the accuracy of IAB for the prediction of post-ablation AF recurrence (7–48 months of follow-up) was 72% pooled sensitivity, 58% pooled specificity, area under the summary ROC curve 0.66 (95% CI [0.62–0.70]).49
Inflammation and Oxidative Stress Various inflammatory biomarkers have been associated with AF; biomarkers associated with recurrence after ablation are summarised in Supplementary Material Table 4.34,35 It is likely that there are bidirectional causal relationships between AF and inflammation through local and systemic phenomena. Proposed mechanisms and mediators include endothelial injury, platelet activation, prothrombotic state, apoptosis, tissue injury, remodelling and fibrosis. How these markers may improve patient stratification and management remains to be determined prospectively. Treatments targeting inflammation in conjunction with ablation have shown promising results. Deftereos et al. randomised 161 paroxysmal AF patients undergoing PVI to receive either a 3-month course of colchicine 0.5 mg twice daily or placebo.36 Colchicine was associated with a considerably lower risk of early AF recurrence at 3-month followup (OR 0.38; 95% CI [0.18–0.80]) and resulted in more pronounced reductions in C-reactive protein and interleukin-6 compared with placebo. In a second study with similar design,37 colchicine was associated with fewer AF recurrences compared with placebo after a 15-month median follow-up (31.1% versus 49.5%; OR 0.46; 95% CI [0.26–0.81]) and health-related quality of life improved accordingly.
Fibrosis and Extracellular Matrix Remodelling Atrial fibrosis is a common endpoint of a variety of AF-promoting conditions, and AF itself induces remodelling and fibrosis.38 Fibrosis promotes AF through disruption of intermyocyte coupling, local conduction disturbances, arrhythmogenic fibroblast-myocyte interactions and heterogeneities in conduction properties and repolarisation.39 Circulating biomarkers of fibrosis associated with post-ablation AF recurrence are summarised in Supplementary Material Table 4.
Genetic Predictors of AF Recurrence Genome-wide association studies and candidate gene studies have identified numerous polymorphisms associated with the risk of newonset AF.40,41 A subset of these have been associated with AF recurrence after ablation, as well as with extra-PV triggers, pre-existent LA scars and LA diameter.42,43 In addition, genetic polymorphisms involved in inflammation, fibrosis and myocardial injury have been associated with post-ablation AF recurrence.44–46 While these data provide valuable insight into the underlying mechanisms, the modest predictive accuracy of genetic predictors has not translated into clinical implementation.
Surface ECG and electrophysiology study parameters provide key information about the integrity and electrophysiological properties of the myocardium. Supplementary Material Table 5 lists examples of electrical predictors of AF recurrence after ablation.
Low-voltage areas (LVAs) identified by electroanatomic mapping are a marker of fibrosis and correlate closely with late gadolinium enhancement at MRI.50,51 The extent of LVAs is an established predictor of recurrence after AF ablation.52,53 A causal relationship between LVAs and AF recurrence is supported by seemingly lower recurrence rates following PVI with adjunctive substrate modification targeting LVAs.54 However, the current literature is limited by the retrospective nature of most studies and the wide variability in ablation protocols; large RCTs are needed to determine the role of adjunctive LVA ablation in CA of paroxysmal and persistent AF.
Electrical Characteristics of AF AF is characterised by disorganised atrial electrical activity with multiple simultaneously active wavefronts propagating with unstable, variable activation sequences. As a result, metrics used to characterise AF electrical activity have focused on quantifying the degree of organisation using measures of cycle length, amplitude, spectral organisation and entropy, several of which have been reported to predict arrhythmia recurrence after AF ablation (Supplementary Material Table 5). Regardless of the specific measure, greater AF disorganisation/complexity (for example short AF cycle length, high dominant frequency, low f wave amplitude, low spectral organisation, high entropy) is predictive of worse rhythm outcomes, likely because these parameters are related to a shorter atrial refractory period, more numerous simultaneous wavelets and atrial fibrosis.55 Moreover, AF seems to progressively organise within the 60–120 seconds preceding spontaneous termination, as shown by Alcaraz and Rieta’s sample entropy, a non-linear measure of signal regularity.56,57 Importantly, organisation parameters show a progressive decline in AF complexity in response to successful ablation compared with unsuccessful ablation, as shown with AF cycle length, dominant frequency (DF) and spectral organisation index.58–60 AF electrical complexity parameters show promise to refine patient selection and have been proposed to monitor the effect of ablation during stepwise procedures.59 The impact on long-term outcomes remains to be determined. The extent to which AF complexity parameters could help identify ablation targets remains controversial. Atienza et al. performed biatrial DF mapping using 3D electroanatomic mapping in 50 consecutive patients (64% paroxysmal AF) and ablated sites of local maximum
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Individualised AF Ablation DF (DFmax), followed by PVI and post-ablation DF assessment.61 A significant reduction in DF in all chambers (left atrium, right atrium and coronary sinus [CS]) was associated with freedom from AF after an average 9-month follow-up, while patients with recurrent AF exhibited consistent DF reduction only in the LA. Successful ablation of all DFmax sites was a strong predictor of freedom from arrhythmia (78% versus 20%; p=0.001) compared to patients with residual untargeted DFmax sites. However, in a more recent multicentre RCT of 117 persistent AF patients, DF-based adjunctive substrate modification failed to improve outcomes compared to PVI alone.62 As a result of these conflicting data, international recommendations consider the strategy ‘of unknown usefulness’ (recommendation Class IIB).3
Functional Endpoints for AF Ablation Non-inducibility By analogy with other tachyarrhythmias in which persistent inducibility at procedure end is indicative of a poor arrhythmia prognosis, AF noninducibility has been investigated as a tailored procedural endpoint for AF ablation. AF inducibility has been tested using two different modalities with different conceptual goals: rapid atrial pacing, or burst pacing, assesses sustainability, i.e. the capacity of the heart to maintain AF over time, thereby assessing the extent of AF-maintaining substrate, while isoproterenol infusion assesses the propensity of the heart to initiate AF, thereby identifying AF-triggering foci.
Non-inducibility by Atrial Burst Pacing A normal heart should not sustain AF for more than a limited period of time – in the order of a few seconds for experimental models such as goats.63 In human studies, the majority of patients with healthy hearts did not sustain induced AF for ≥5 minutes, although the proportions varied with the induction protocol.64 Therefore, sustained induced AF is considered indicative of AF-maintaining substrate and, eventually, a marker of structural and electrophysiological remodelling. AF inducibility at procedure end (most commonly PVI alone) has been reported to predict AF recurrence in numerous studies.65,66 In addition, we recently demonstrated that a progression of AF inducibility from de novo AF ablation to repeat procedures was strongly associated with worse arrhythmia outcomes.67 Moreover, inducibility was shown to decline with additional ablation beyond PVI.58 As a result, inducibility has been used as a procedure endpoint in stepwise AF ablation protocols and recent evidence suggests that AF non-inducibility may be a better indicator of outcome than AF termination.58,68,69 Consistent with a relationship to AF substrate, AF inducibility has also been shown to be a strong predictor of new-onset AF in patients undergoing typical atrial flutter ablation.70 Finally, an ablation strategy aimed at achieving AF non-inducibility by electrogram-guided ablation lines has been found to improve arrhythmia outcomes in one RCT.71 However, the prognostic value of AF inducibility has not been reproduced consistently across studies.65,72 Moreover, the particular substrate modification strategy used to achieve AF non-inducibility is likely a critical determinant of outcome, yet none of the adjunctive ablation strategies investigated to date have an established benefit beyond PVI.3,73 The value of AF inducibility as a procedure endpoint is likely to be dependent on the induction protocol and on the cut-off duration used to define sustained AF; more data are needed to determine the limits of AF inducibility and sustainability in health and disease and how it relates to clinical outcomes after ablation.
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Non-inducibility by Isoproterenol Infusion High-dose isoproterenol infusion (typically up to 20–30 µg/min for ≥10 minutes) can be used to provoke non-PV triggers, allowing them to be located for ablation. While PV triggers are present in >90% of AF patients, including persistent and long-standing persistent AF,74 nonPV AF-triggering foci have also been reported in 11–32% of patients undergoing AF ablation.74–77 The most common locations include the superior vena cava, the CS, the ligament of Marshall, the posterior LA wall, the mitral valve annulus, the LA appendage and the crista terminalis/eustachian ridge.74–76,78 The presence of non-PV triggers at de novo AF ablation has been consistently reported as a predictor of AF recurrence.75,79–83 Crawford et al. reported AF inducibility by isoproterenol infusion to predict AF recurrence at 12 months with 33% sensitivity, 97% specificity and 83% accuracy.84 Recently, Hojo et al. assessed non-PV triggers in 216 patients (80% paroxysmal AF) who underwent de novo PVI followed by a second procedure at 6 months.85 The authors found a strong association between development of nonPV triggers and AF recurrence: 24.1% of patients with newly developed non-PV triggers had AF recurrence versus 7.4% of patients without newly developed non-PV triggers. Other observational studies have reported improved long-term outcomes after de novo AF ablation when non-PV triggers were identified by isoproterenol infusion and ablated.79 Of note, heterogeneous definitions of ‘significant’ non-PV triggers and ablation strategies have been used leading to heterogeneous results.86 Despite promising results, the lack of RCTs warrants caution. As a result, isoproterenol infusion and non-PV triggers ablation remains a Class IIB recommendation in the 2017 international expert consensus statement on catheter and surgical ablation of AF.3
Termination The rationale for using termination as an endpoint of CA is to demonstrate efficient modification/elimination of the atrial substrate necessary to sustain AF. By analogy with other tachyarrhythmias, termination of a long-lasting arrhythmia during radiofrequency delivery can be attributed to the functional elimination of a critical driving mechanism. Of note, this endpoint has limited value for paroxysmal AF ablation since spontaneous termination may be fortuitous. AF termination during stepwise ablation of persistent AF has been reported to predict AF recurrence in several studies.6,21,87–89 The mode of AF termination (directly to sinus rhythm versus via transformation into atrial tachycardia [AT]) was not predictive of recurrence in the majority of studies, but AF termination at index ablation seems associated with a greater proportion of recurrences in the form of AT relative to AF (with a lower total number of arrhythmia recurrences compared to patients without termination).65,87 This could be explained by differences in the lesion set between patients with termination and without termination, since studies investigating termination used stepwise ablation aimed at AF termination. It should be noted, that other studies have shown conflicting results.65 The question was addressed in a substudy of the Substrate and Trigger Ablation for Reduction of Atrial Fibrillation II (STAR AF II) trial, a large RCT on persistent AF ablation primarily designed to compare the efficacy of PVI alone, PVI with electrogram-guided substrate modification (CFAE) and PVI with linear ablations.90,91 Acute AF termination was achieved in 5%, 40% and 17% of patients in each procedure group, respectively (p<0.001). There was no significant difference in 18-month AF-free survival between patients with and without termination (52.7% versus 42.4%; p=0.09), or
Electrophysiology and Ablation Figure 1: Parameters to Consider for Patient Selection, Individualised AF Ablation Strategy and Procedural Endpoints Clinical recurrence risk factors AF type Arterial hypertension Obesity Metabolic syndrome Chronic kidney disease Obstructive sleep apnoea Other*
Echocardiography: • Left atrial size, morphology, function • Left ventricular function MRI: • Late gadolinium enhancement • T1 mapping CT: • Epicardial fat, PV anatomy
Inflammation and oxidative stress Myocardial stretch and injury Fibrosis and ECM remodelling Comorbidities Other*
Probability of mid/long-term sinus rhythm restoration Individualised assessment of risk/benefit
PVI AF termination? Non-inducibility? • Atrial burst pacing • Isoproterenol infusion Other?
AF ablation strategy
PVI Non-PV triggers ablation? Extra-PV substrate modification? • Fibrosis • Low-voltage areas • Electrogram-guided • Other?
Electrocardiography and electrophysiology study
Interatrial block AF electrical organisation and voltage High right atrial dominant frequency Low-voltage areas Non-PV triggers Other*
Genetic predisposition Inflammatory pathways Fibrosis Myocardial injury Extra-PV triggers Other*
Individualised management to improve ablation outcomes Risk factor modification • Blood pressure control • Weight loss • Glycaemic control • Sleep apnoea treatment • Smoking cessation and alcohol reduction • Lipid management Post-procedure anti-inflammatory treatment? Early intervention to prevent disease progression?
Specific management algorithms remain to be evaluated prospectively. * See text, including supplementary material, for more details. ECM = extracellular matrix; PV = pulmonary vein; PVI = pulmonary vein isolation.
between the three ablation strategies regardless of termination (59% of patients were AF-free at 18 months in the PVI group compared with 49% in the PVI + CFAE group and 46% in the PVI + linear ablation group; p=0.15). However, termination during the PVI step was predictive of AF-free survival (49.3% versus 35.7% in termination versus no termination, respectively; p=0.01). It is also noteworthy that AF termination may not indicate elimination of all AF drivers but only a subset which was active at the moment of ablation. Conversely, reverse atrial remodelling and/or trigger elimination may allow for more parsimonious ablation than the sometimes-extensive lesion set required to achieve acute termination. As suggested above, the role of termination per se has hardly been studied, given that the ablation strategies were guided by termination itself. In conclusion, AF termination seems to indicate a more favourable prognosis but alternatively may simply select a subgroup with a limited and ablationsensitive set of driver mechanisms.
Any discussion of the importance of patient selection and/or the choice of procedural endpoints necessarily has the greatest relevance in the context of a consistent ablation strategy. Obvious examples of this include the near uniform absence of a right atrial ablation strategy in most studies, and the lack of ablation within the CS in many studies. The lack of a consistent ablation lesion set with resulting demonstrable, reproducible and stable tissue alteration has also added a significant level of uncertainty to inferences derived from clinical studies. Nevertheless, slow but significant progress in achieving stable reproducible PV isolation and greater consistency in lesion creation has led to the acknowledgement of extra-PV mechanisms of AF sustenance and, in concert with lesion imaging technology such as MRI, may lead to objective standardisation of ablation lesion strategies and their resulting tissue effects.
Figure 1 summarises the parameters to consider for patient selection, individualised AF ablation strategy and procedural endpoints.
• Clinical AF phenotype is an established stratification criterion to determine the indication to catheter ablation, but more individualised and objective selection is needed to improve outcomes. • A number of predictors of AF ablation success based on clinical, electrophysiological, imaging and biological data may allow the identification of patients with very low probability of ablation success with current ablation strategies in these patients. • In selected patients, such as those with HF, sinus rhythm restoration by early intervention may prevent disease progression and improve long-term outcomes. • Individualised procedure endpoints, including AF termination and non-inducibility, have shown promise to achieve improved arrhythmia outcomes via patient-specific lesion sets, but the lack of an established ablation strategy limits clinical inference. • Substrate-based ablation strategies based on delayedenhancement MRI or selected electrophysiological surrogates are promising areas of progress.
Identified predictors of rhythm outcomes after AF ablation are significantly correlated with each other, probably because they measure the same underlying substrate. For example, while persistent AF, age and arterial hypertension are established indicators of poor prognosis, they were not significantly predictive of rhythm outcome after adjustment for fibrosis.92 A substantial limitation of the available evidence is its mostly observational nature. It should also be noted that some of the most frequently reported predictors of outcome also have a strong influence on the use (or lack thereof) of CA, which poses a substantial risk of bias in observational studies.93 Despite a considerable literature, no management algorithm for patient selection has arisen from these predictive tools to date. The predictive value of causal comorbidities is commonly limited by a lack of sensitivity, which is likely explained by the multifactorial nature of AF recurrence and progression.
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Individualised AF Ablation 1.
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Heart Rhythm 2009;6:33–40. https://doi.org/10.1016/j.hrthm.2008.10.024; PMID: 19121797. Atienza F, Almendral J, Ormaetxe JM, et al. Comparison of radiofrequency catheter ablation of drivers and circumferential pulmonary vein isolation in atrial fibrillation: a noninferiority randomized multicenter RADAR-AF trial. J Am Coll Cardiol 2014;64:2455–67. https://doi.org/10.1016/j. jacc.2014.09.053; PMID: 25500229. Wijffels MC, Kirchhof CJ, Dorland R, Allessie MA. Atrial fibrillation begets atrial fibrillation a study in awake chronically instrumented goats. Circulation 1995;92:1954–68. https://doi.org/10.1161/01.CIR.92.7.1954; PMID: 7671380. Huang W, Liu T, Shehata M, et al. Inducibility of atrial fibrillation in the absence of atrial fibrillation: what does it mean to be normal? Heart Rhythm 2011;8:489–92.
Electrophysiology and Ablation
https://doi.org/10.1016/j.hrthm.2010.11.036; PMID: 21111062. Baker M, Kumar P, Hummel JP, Gehi AK. Non-inducibility or termination as endpoints of atrial fibrillation ablation: what is the role? J Atr Fibrillation 2014;7:1125. https://doi.org/10.4022/ jafib.1125; PMID: 27957119. Lee K-N, Choi J-I, Kim YG, et al. Comparison between linear and focal ablation of complex fractionated atrial electrograms in patients with non-paroxysmal atrial fibrillation: a prospective randomized trial. Europace 2019;21:598–606. https://doi.org/10.1093/europace/euy313; PMID: 30649276. Johner N, Shah DC, Giannakopoulos G, et al. Evolution of postpulmonary vein isolation atrial fibrillation inducibility at redo ablation: electrophysiological evidence of extra-pulmonary vein substrate progression. Heart Rhythm 2019. https://doi. org/10.1016/j.hrthm.2019.02.026; PMID: 30818093; epub ahead of press. Haldar SK, Jones DG, Bahrami T, et al. Catheter ablation vs electrophysiologically guided thoracoscopic surgical ablation in long-standing persistent atrial fibrillation: the CASA-AF study. Heart Rhythm 2017;14:1596–603. https://doi. org/10.1016/j.hrthm.2017.08.024; PMID: 29101964. Pambrun T, Denis A, Duchateau J, et al. MARSHALL bundles elimination, Pulmonary veins isolation and Lines completion for ANatomical ablation of persistent atrial fibrillation: MARSHALL-PLAN case series. J Cardiovasc Electrophysiol 2019;30:7–15. https://doi.org/10.1111/jce.13797; PMID: 30461121. Romero J, Estrada R, Holmes A, et al. Atrial fibrillation inducibility during cavo-tricuspid isthmus dependent atrial flutter ablation for the prediction of clinical atrial fibrillation. Int J Cardiol 2017;240:246–50. https://doi.org/10.1016/j. ijcard.2017.01.131; PMID: 28606678. Oral H, Chugh A, Lemola K, et al. Noninducibility of atrial fibrillation as an end point of left atrial circumferential ablation for paroxysmal atrial fibrillation a randomized study. Circulation 2004;110:2797–801. https://doi.org/10.1161/01. CIR.0000146786.87037.26; PMID: 15505091. Santangeli P, Zado ES, Garcia FC, et al. Lack of prognostic value of atrial arrhythmia inducibility and change in inducibility status after catheter ablation of atrial fibrillation. Heart Rhythm 2018;15:660–5. https://doi.org/10.1016/j. hrthm.2017.10.023; PMID: 29056544. Latchamsetty R, Morady F. Source determination in atrial fibrillation. Arrhythmia Electrophysiol Rev 2018;7:165–8. https://doi. org/10.15420/aer:2018:25:2; PMID: 30416729.
74. S antangeli P, Zado ES, Hutchinson MD, et al. Prevalence and distribution of focal triggers in persistent and long-standing persistent atrial fibrillation. Heart Rhythm 2016;13:374–82. https://doi.org/10.1016/j.hrthm.2015.10.023; PMID: 26477712. 75. Shah D, Haissaguerre M, Jais P, Hocini M. Nonpulmonary vein foci: do they exist? Pacing Clin Electrophysiol 2003;26:1631–5. https://doi.org/10.1046/j.1460-9592.2003.t01-1-00243.x; PMID: 12914614. 76. Lin WS, Tai CT, Hsieh MH, et al. Catheter ablation of paroxysmal atrial fibrillation initiated by non-pulmonary vein ectopy. Circulation 2003;107:3176–83. https://doi. org/10.1161/01.CIR.0000074206.52056.2D; PMID: 12821558. 77. Lee SH, Tai CT, Hsieh MH, et al. Predictors of non-pulmonary vein ectopic beats initiating paroxysmal atrial fibrillation: implication for catheter ablation. J Am Coll Cardiol 2005;46:1054–9. https://doi.org/10.1016/j.jacc.2005.06.016; PMID: 16168291. 78. Di Biase L, Burkhardt JD, Mohanty P, et al. Left atrial appendage: an underrecognized trigger site of atrial fibrillation. Circulation 2010;122:109–18. https://doi. org/10.1161/CIRCULATIONAHA.109.928903; PMID: 20606120. 79. Zhao Y, Di Biase L, Trivedi C, et al. Importance of nonpulmonary vein triggers ablation to achieve long-term freedom from paroxysmal atrial fibrillation in patients with low ejection fraction. Heart Rhythm 2016;13:141–9. https://doi. org/10.1016/j.hrthm.2015.08.029; PMID: 26304713. 80. Yanagisawa S, Inden Y, Kato H, et al. Impaired renal function is associated with recurrence after cryoballoon catheter ablation for paroxysmal atrial fibrillation: A potential effect of non-pulmonary vein foci. J Cardiol.2017;69:3–10. https://doi. org/10.1016/j.jjcc.2016.07.008; PMID: 27499270. 81. Lo LW, Lin YJ, Chang SL, et al. Predictors and characteristics of multiple (more than 2) catheter ablation procedures for atrial fibrillation. J Cardiovasc Electrophysiol 2015;26:1048–56. https:// doi.org/10.1111/jce.12748; PMID: 26178628. 82. Takigawa M, Takahashi A, Kuwahara T, et al. Long-term outcome after catheter ablation of paroxysmal atrial fibrillation: Impact of different atrial fibrillation foci. Int J Cardiol 2017;227:407–12. https://doi.org/10.1016/j.ijcard.2016.11.028; PMID: 27838128. 83. Kuroi A, Miyazaki S, Usui E, et al. Adenosine-provoked atrial fibrillation originating from non-pulmonary vein foci: the clinical significance and outcome after catheter ablation. JACC Clin Electrophysiol 2015;1:127–35. https://doi.org/10.1016/j. jacep.2015.02.020; PMID: 29759355. 84. Crawford T, Chugh A, Good E, et al. Clinical value of noninducibility by high-dose isoproterenol versus rapid
atrial pacing after catheter ablation of paroxysmal atrial fibrillation. J Cardiovasc Electrophysiol 2010;21:13–20. https://doi.org/10.1111/j.1540-8167.2009.01571.x; PMID: 19682170. Hojo R, Fukamizu S, Kitamura T, et al. Development of nonpulmonary vein foci increases risk of atrial fibrillation recurrence after pulmonary vein isolation. JACC Clin Electrophysiol 2017;3:547–55. https://doi.org/10.1016/j. jacep.2016.12.008; PMID: 29759426. Della Rocca DG, Mohanty S, Trivedi C, et al. Percutaneous treatment of non-paroxysmal atrial fibrillation: a paradigm shift from pulmonary vein to non-pulmonary vein trigger ablation? Arrhythmia Electrophysiol Rev 2018;7:256–60. https://doi. org/10.15420/aer.2018.56.2; PMID: 30588313. O’Neill MD, Wright M, Knecht S, et al. Long-term follow-up of persistent atrial fibrillation ablation using termination as a procedural endpoint. Eur Heart J 2009;30:1105–12. https://doi. org/10.1093/eurheartj/ehp063; PMID: 19270341. Ammar S, Hessling G, Reents T, et al. Importance of sinus rhythm as endpoint of persistent atrial fibrillation ablation. J Cardiovasc Electrophysiol 2013;24:388–95. https://doi. org/10.1111/jce.12045; PMID: 23252615. Buttu A, Vesin J-M, Zaen JV, et al. A high baseline electrographic organization level is predictive of successful termination of persistent atrial fibrillation by catheter ablation. JACC Clin Electrophysiol 2016;2:746–55. https://doi. org/10.1016/j.jacep.2016.05.017; PMID: 29759754. Kochhäuser S, Jiang C-Y, Betts TR, et al. Impact of acute atrial fibrillation termination and prolongation of atrial fibrillation cycle length on the outcome of ablation of persistent atrial fibrillation: A substudy of the STAR AF II trial. Heart Rhythm 2017;14:476–83. https://doi.org/10.1016/j.hrthm.2016.12.033; PMID: 28011328. Verma A, Jiang C, Betts TR, et al. Approaches to catheter ablation for persistent atrial fibrillation. N Engl J Med 2015;372:1812–22. https://doi.org/10.1056/NEJMoa1408288; PMID: 25946280. McGann C, Akoum N, Patel A, et al. Atrial fibrillation ablation outcome is predicted by left atrial remodeling on MRI. Circ Arrhythm Electrophysiol 2014;7:23–30. https://doi.org/10.1161/ CIRCEP.113.000689; PMID: 24363354. Piccini JP, Stevens SR, Lokhnygina Y, et al. Outcomes after cardioversion and atrial fibrillation ablation in patients treated with rivaroxaban and warfarin in the ROCKET AF Trial. J Am Coll Cardiol 2013;61:1998–2006. https://doi.org/10.1016/j. jacc.2013.02.025; PMID: 23500298.
ARRHYTHMIA & ELECTROPHYSIOLOGY REVIEW
Electrophysiology and Ablation
The Role of Cardiac MRI in the Management of Ventricular Arrhythmias in Ischaemic and Non-ischaemic Dilated Cardiomyopathy Tom Nelson, 1,2 Pankaj Garg, 1,2 Richard H Clayton 3,4 and Justin Lee 1,2 1. Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK; 2. Department of Immunity, Infection and Cardiovascular Disease, University of Sheffield, Sheffield, UK; 3. INSIGNEO Institute for In-Silico Medicine, University of Sheffield, Sheffield, UK; 4. Department of Computer Science, University of Sheffield, Sheffield, UK
Abstract Ventricular tachycardia (VT) and VF account for the majority of sudden cardiac deaths worldwide. Treatments for VT/VF include anti-arrhythmic drugs, ICDs and catheter ablation, but these treatments vary in effectiveness and carry substantial risks and/or expense. Current methods of selecting patients for ICD implantation are imprecise and fail to identify some at-risk patients, while leading to others being overtreated. In this article, the authors discuss the current role and future direction of cardiac MRI (CMRI) in refining diagnosis and personalising ventricular arrhythmia management. The capability of CMRI with gadolinium contrast delayed-enhancement patterns and, more recently, T1 mapping to determine the aetiology of patients presenting with heart failure is well established. Although CMRI imaging in patients with ICDs can be challenging, recent technical developments have started to overcome this. CMRI can contribute to risk stratification, with precise and reproducible assessment of ejection fraction, quantification of scar and ‘border zone’ volumes, and other indices. Detailed tissue characterisation has begun to enable creation of personalised computer models to predict an individual patient’s arrhythmia risk. When patients require VT ablation, a substrate-based approach is frequently employed as haemodynamic instability may limit electrophysiological activation mapping. Beyond accurate localisation of substrate, CMRI could be used to predict the location of re-entrant circuits within the scar to guide ablation.
Keywords Cardiac MRI, risk stratification, cardiomyopathy, ventricular tachycardia ablation Disclosure: The authors have no conflicts of interest to declare. Received: 18 January 2019 Accepted: 25 April 2019 Citation: Arrhythmia & Electrophysiology Review 2019;8(3):191–201. DOI: https://doi.org/10.15420/aer.2019.5.1 Correspondence: Tom Nelson, Northern General Hospital, Herries Rd, Sheffield S5 7AU, UK. E: email@example.com Open Access: This work is open access under the CC-BY-NC 4.0 License which allows users to copy, redistribute and make derivative works for non-commercial purposes, provided the original work is cited correctly.
Ventricular tachycardia (VT) and VF occur mainly in people with impaired cardiac function and/or ischaemic heart disease, and account for the majority of sudden cardiac deaths worldwide.1 Treatment with anti-arrhythmic drugs such as amiodarone may be at best neutral in terms of mortality and carries significant long-term risks.2,3 While ICDs significantly improve survival for patients with significantly impaired left ventricular ejection fraction (LVEF), the devices also carry risks of infection and inappropriate shocks being given.4,5 Some patients may present with a ‘secondary prevention’ ICD indication such as sustained VT or VF arrest, but, in the primary prevention setting, the risk of arrhythmia is based on the presence and severity of structural heart disease. Selection of patients in this way lacks precision and fails to identify some at-risk patients while leading to overtreatment in others. Current guidelines recommend echocardiography as the first-line investigation for cardiac function due to ease of access, because the echocardiographic equipment is usually available in heart clinics, whereas cardiac MRI (CMRI) services currently tend to only be available in specialist (tertiary) centres.6 However, CMRI is superior in terms of both accuracy and reproducibility when quantifying LVEF and myocardial mass, and can overcome limitations
© RADCLIFFE CARDIOLOGY 2019
of inadequate echocardiographic windows. CMRI offers a one-stop investigation for accurately establishing cardiac structure, function and myocardial tissue characterisation.
Understanding the Substrate for Ventricular Arrhythmia Ischaemic Cardiomyopathy Studies of cardiac tissue obtained before transplantation or following left ventricular (LV) aneurysm surgery, as well as more recent human and animal CMRI studies, have confirmed our understanding of the structural changes that occur in ischaemic cardiomyopathy (ICM). Strands of surviving tissue within and at the periphery of the infarct region form tortuous and slowly-conducting channels which support re-entry, so the infarct border zone frequently has a heterogeneous appearance on CMRI.8–10 Fenoglio et al. demonstrated there were bundles of surviving myocytes in endocardial resection samples; some of these had a diameter of <100 µm, but it was not known which of these channels were mechanistically important.11 De Bakker et al. showed that differential slow conduction occurs with multiple tracts <200 µm.9 Recently, ultra-
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Electrophysiology and Ablation high (submillimetre) resolution ex vivo CMRI of infarcted porcine hearts showed conducting pathways were mainly subendocardial.12 However, in this study, a significant minority of pathways were observed to be entirely epicardial and would be inaccessible for endocardial catheter ablation. Urgent reperfusion (by either thrombolysis or angioplasty) for MI reduces infarct size and the incidence of subsequent chronic VT. In observational studies, VT cycle lengths were shorter, possibly suggesting a smaller circuit, in patients who had received revascularisation than in those who had not been revascularised.13–15 This would suggest that reperfusion strategies can introduce greater substrate heterogeneity within the infarcted area. Techniques that characterise and quantify the scar border zone or identify channels could improve risk stratification and treatment planning. However, while larger conducting channels may be identified using CMRI, it is likely that others are missed because of the limited spatial resolution of current clinical imaging. Where channels are too small to be visualised, measures of tissue heterogeneity may act as surrogates for the presence of ‘sub-resolution’ channels.
Non-ischaemic Cardiomyopathy The aetiology of VT in patients with non-ischaemic cardiomyopathy (NICM) is less well understood, partly because of the heterogeneity of underlying pathological processes in NICM. When regional fibrosis is detectable, it is often midwall or subepicardial, making access for catheter ablation challenging. These factors may explain why outcomes from NICM VT ablation are worse than those in ischaemic cardiomyopathy.16 In contrast to macro re-entrant VT, polymorphic VT or VF may occur due to distinct (but related) mechanisms. Replacement fibrosis can be patchy and/or diffuse, with disruption of the left ventricular microarchitecture.17 This diffuse fibrosis provides the substrate for conduction block and micro re-entry resulting in VF.18,19 This substrate is often dynamic with progressive fibrosis, reducing the long-term efficacy of targeted substrate modification.
manually identify areas of fibrosis, whereas semi-automated standard deviation (SD) or full width at half maximum (FWHM) techniques require less user input. The SD method defines abnormal voxels with more than 2, 3, 4, 5 or 6 standard deviations greater than the SI in a user-defined region of ‘normal’ myocardium. The FWHM method identifies tissues that fall below the SI of a user-defined area of fibrosis. Typical FWHM thresholds define a dense scar as one with >50% peak SI and a border zone between 35% and 50%.22–24 These techniques generate either a mass or percentage value of affected myocardium for the total scar burden, or for subdivisions of border zone and scar core. Although these techniques are reproducible, depending on the method and threshold chosen, significant inter-method variation is seen, and there is limited comparison with the gold standard of pathological specimens.25 In a small series, FWHM method correlates best with pathological specimens in animals and with manual segmentation in humans with ICM.24,26
T1 Mapping Conditions with diffuse tissue fibrosis are more challenging to detect with LGE if there are no unaffected myocardial segments. Measurement of absolute T1 relaxation values sidesteps the requirement for tissue inhomogeneity in LGE imaging. Spatial resolution is inferior to LGE imaging at approximately 1.4 × 1.9 × 6 mm and is challenging at higher heart rates, though native T1 mapping does not require the use of a contrast agent.27 As imaging protocols, field strength and acquisition methods vary, reference T1 values are specific to the vendor/ manufacturer. Unlike LGE, native T1 values are frequently abnormal in diffuse diseases of the myocardium, giving insights into the aetiology of NICM. T1 values are increased by tissue oedema and fibrosis, and are reduced by lipid overload (e.g. in Anderson-Fabry disease) and iron overload.28 For clinical use, mid-myocardial septal values for T1 are reported, though a map can be generated showing the native T1 values across an imaging slice. The T1 map may highlight focal areas of oedema as seen in acute myocarditis, acute myocardial infarction or takotsubo cardiomyopathy.29,30
Cardiac MRI Tissue Characterisation Late Gadolinium Enhancement Late gadolinium enhanced (LGE) CMRI imaging has become the de facto standard for imaging myocardial fibrosis. This approach uses gadolinium as a contrast agent to highlight areas of heterogeneity within the myocardium (e.g. fibrotic versus normal areas). In normal tissue, the washout of gadolinium is rapid, whereas in areas of myocardial fibrosis the washout is slower. By timing the image acquisition to occur ‘late’ when washout has occurred in normal tissue but not in fibrotic tissue, regions of normal and fibrosed tissue can be differentiated. This technique relies on setting the inversion time to ‘null’ distant normal myocardium, making it appear black. Areas of enhancement have been demonstrated to correlate well with both acute myocardial necrosis and chronic fibrosis in ischaemic pathological specimens as well as replacement fibrosis in non-ischaemic dilated cardiomyopathy.20,21 Typical image resolution is 1.4 × 1.4 × 10 mm (the 10 mm distance is the gap between slices).
Quantification of Late Gadolinium Enhancement Although a narrative report of scar distribution is typically given in clinical use, the volume of abnormal tissue can also be quantified based on signal intensity (SI). Manual planimetry requires the operator to
Contrast-enhanced T1 mapping allows the extracellular volume (ECV) to be estimated. By comparing pre- and post-contrast T1 values (referencing the T1 values of the blood pool and the patient’s haematocrit), a value for ECV is obtained. This is expressed as a fraction of the tissue volume; published normal values for ECV are approximately 25%.31,32 While native T1 values examine entire tissues, ECV characterises only the extracellular matrix and is therefore less affected by acute oedema. Higher ECV values are seen with expansion of the interstitium due to fibrosis or deposition and therefore correlate well with fibrotic changes at endomyocardial biopsy.33,34 As with native T1, ECV can be expressed as a global value or as a map highlighting regional variation. While ECV is raised in areas of chronic infarction, its main advantage over LGE for arrhythmic risk stratification is its potential to identify diffuse myocardial fibrosis in NICM.28,35
T2 Imaging Acute myocardial injury results in interstitial oedema. This occurs rapidly after myocardial infarction, and T2-weighted CMRI sequences, which identify oedema, can predict final infarct size.36 In chronic conditions such as cardiac sarcoidosis, myocarditis, transplant rejection and toxic cardiomyopathies, T2-weighted imaging accurately
ARRHYTHMIA & ELECTROPHYSIOLOGY REVIEW
Ischaemic and Non-ischaemic Dilated Cardiomyopathy Table 1: Comparison of Myocardial Tissue Characterisation Techniques Measurement
Evidence for use as a
of scar border
decision aid for risk
Late gadolinium enhancement
++ (ICM) +++ (NICM)
Extracellular volume mapping
ICM = ischaemic cardiomyopathy; NICM = non-ischaemic cardiomyopathy.
identifies myocardial oedema.37 While arrhythmic complications in these conditions may be predicted using CMRI, there is limited data to support T2 imaging for arrhythmic risk stratification in patients with ICM or NICM.38
Figure 1: Ischaemic Cardiomyopathy: Image Comparison Anterior MI
Comparison of Techniques LGE CMRI identifies the aetiology of left ventricular systolic dysfunction (LVSD), and permits the identification and quantification of myocardial fibrosis. As a semi-quantitative technique, LGE can demonstrate only relative differences between fibrotic and non-fibrotic myocardium. As a result, diffuse diseases of the myocardium may be missed with this technique. Newer techniques such as T1 and ECV mapping have the advantage of being quantitative and, as such, can be used to identify such diffuse myocardial fibrosis seen in some forms of NICM. Table 1 demonstrates these differences. Examples of these techniques are shown in Figure 1 and Figure 2.
Current Clinical Application of Cardiac MRI Current guidelines recommend echocardiography as the firstline investigation in patients presenting with heart failure or VT, although CMRI gains a class I recommendation if an infiltrative cause is suspected.39,40 With echocardiography or CMRI, regional wall motion abnormalities and wall thinning suggest an ischaemic aetiology, while global hypokinesis supports a non-ischaemic cause. However, assessment is highly dependent on image quality and CMRI can overcome inadequate echocardiographic windows.39 In patients presenting with VT, CMRI is particularly useful for identifying inflammatory or infiltrative aetiology as well as ischaemic and non-ischaemic cardiomyopathies. In one series, CMRI changed the working diagnosis in 50% of patients presenting with VT/VF.41 Myocardial infarction shows a subendocardial to full thickness pattern of LGE, which will conform to one or more coronary territories. Conversely, non-ischaemic dilated cardiomyopathy often has a more diffuse pattern of fibrosis.17 As a result, the location of regions highlighted by LGE in such patients is variable, but it is more commonly located in the midwall or epicardial regions of anteroseptal or inferolateral segments.42 Revascularisation of hypokinetic non-infarcted chronically ischaemic tissues may result in functional recovery.43 Hyperenhancement transmurality in LGE CMRI correlates well with myocardial recovery after revascularisation. In a series of 50 patients, regions with ≤25% transmurality were likely to demonstrate improved contractility, while those with >50% transmurality showed poor functional recovery after revascularisation.20 When myocardial ischaemia causes polymorphic VT/VF, revascularisation is indicated. However, in patients with sustained monomorphic VT, revascularisation is more contentious, since monomorphic VT usually reflects established substrate that may not be altered by revascularisation.
ARRHYTHMIA & ELECTROPHYSIOLOGY REVIEW
Ischaemic cardiomyopathy secondary to an anterior ST-elevation MI. In this short axis slice, there is subendocardial LGE in the left ventricular anterior wall. T1 native values are elevated in the same region. ECV demonstrates this is a dense scar (ECV >55%). ECV = extracellular volume; LGE = late gadolinium enhancement.
Indeed, in a case series of 65 patients with coronary disease and VT/ VF, surgical revascularisation did not appear to affect inducibility of
Electrophysiology and Ablation Figure 2: Non-ischaemic Cardiomyopathy: Image Comparison
including in patients without detectable LVSD.50–52 Total scar burden correlates with mortality and ICD discharges, even in multivariate models including LVSD (Table 2).51–56 Quantification of the scar border zone (rather than scar core) or quantifying the number of peri-infarct channels are alternative approaches to predicting VT/VF events.57–60
Non-ischaemic Cardiomyopathy As in ICM, the presence of LGE on CMRI in patients with NICM strongly predicts mortality and arrhythmic events across the spectrum of LV impairment.21,61–63 Patients with fibrosis identified by LGE are also less likely to achieve reverse remodelling with medical therapy.64 The spatial distribution of fibrosis is also important, with septal scarring conferring a higher risk of sudden cardiac death (SCD) than inferolateral variants, and subepicardial scarring conferring a higher risk than linear mid-wall fibrosis.65 Although patients with a low LVEF (<35%) have the highest individualised risk of SCD, this accounts for only ~20% of all cardiac arrests. The great majority of cardiac arrests occur outside this high-risk category.1 Patients with fibrosis identified by LGE have worse outcomes than those without and risk stratification of individuals based on the presence or absence of LGE rather than on LVEF alone may aid patient selection for ICDs.66 For example, compared with all those with LVEF <35% (i.e. using echocardiographic risk stratification alone), those with an LVEF >35% and LGE have similar risks of SCD.63 Moreover, these selected patients with preservation of pump function will often have a lower competing risk of non-arrhythmic death.
These two cases of non-ischaemic cardiomyopathy highlighting the utility of LGE, T1 and ECV. LGE+ is a case with mid-myocardial fibrosis (orange arrows), with globally high ECV. LGE− did not demonstrate any fibrosis on LGE imaging but had high native T1 and globally raised ECV, confirming the diagnosis of non-ischaemic cardiomyopathy. ECV = extracellular volume; LGE = late gadolinium enhancement.
arrhythmia, but was associated with good long-term outcomes.44 Several other observational studies have similarly found that a reduction in mortality is associated with either PCI or surgical revascularisation in patients presenting with VT/VF.45–49
Cardiac MRI Risk Stratification Current European Society of Cardiology guidelines for ICD implantation (in both ICM and NICM) are based upon LVEF and New York Heart Association class but not formal scar quantification.6 The more recent 2017 American Heart Association guidelines differ slightly, with a class IIa recommendation given for the use of CMRI imaging to aid risk stratification in patients with suspected NICM.40 To investigate the role of CMRI as a tool for risk stratification, PubMed was searched using the terms (‘Risk Assessment’[Mesh] OR ‘Prognosis’[Mesh] OR ‘Predictive Value of Tests’[Mesh]) AND (‘Myocardial Ischemia’[Mesh] OR ‘Dilated Cardiomyopathy’[Mesh]) AND (‘Magnetic Resonance Imaging’[Mesh]) OR ‘Gadolinium’[Mesh]) to identify studies using CMRI to guide risk stratification. These studies are summarised in Table 2.
Ischaemic Cardiomyopathy The presence of LGE with CMRI imaging strongly predicts mortality in patients with ischaemic cardiomyopathy, independently of LVEF,
In contrast with ICM, where scar-related monomorphic VT predominates, patients with NICM are more likely to experience polymorphic VT and VF.67 On a review of the literature, most studies examining CMRI for risk stratification in NICM do not differentiate between VT and VF (Table 2). This practical approach is helpful for treatment decisions. However, Piers et al. found that scarring predicts monomorphic VT but not polymorphic VT or VF, suggesting that factors other than macroscopic anatomical substrate may be important in arrhythmogenesis in NICM.68 Patients with NICM with no evidence of fibrosis on CMRI have fewer arrhythmic events, a lower risk of death and a higher likelihood of reverse remodelling. Careful patient selection for prophylactic ICD implantation in this population is required, and it therefore seems logical that identification of fibrosis using CMRI could more accurately identify those who would benefit, particularly patients with NICM and those with an LVEF >35%. However, no trial data exist to supports this approach.
Late Enhancement There is now persuasive evidence that quantification of the scar and/ or border zone burden can be used to help risk stratify patients with both ICM and NICM, in addition to measures of LVEF. The fact that this relationship exists across the range of LVSD suggests that fibrosis itself is an important determinant of arrhythmic risk, rather than being simply a marker of end-stage disease. While the presence of any degree of LGE predicts risk in both NICM and ICM, quantification of scar extent only appears to add substantial incremental risk prediction benefit in patients with ICM. However, the clinical applicability of fibrosis quantification is limited by a lack of consensus over which scar metrics and thresholds are the best predictors of outcomes, or how to apply these metrics to individuals.69
ARRHYTHMIA & ELECTROPHYSIOLOGY REVIEW
Ischaemic and Non-ischaemic Dilated Cardiomyopathy Table 2: Prognostic Impact of Cardiac MRI Author
Method for Scar
HR for Adverse Outcome (95% CI)
Quantification Ischaemic Cardiomyopathy Bello et al. 200554
≥2 SD above remote normal myocardium
Not given, p=0.02
Greater infarct mass and infarct surface area predicts inducible VT at EPS
Yan et al. 200658
≥2 SD above remote normal myocardium
1.45 (1.15–1.84) per 10% increase in scar border zone
Extent of the peri-infarct zone defined by delayed-enhancement CMRI is an independent predictor of post-myocardial infarction all-cause and cardiovascular mortality, after adjusting for LV volumes or LVEF
Schmidt et al. 2007119
Not given, p=0.02
Border zone mass was higher in those with inducible VT than those with no inducibility, but there was no difference in scar core mass
Roes et al. 2009120
1.49 1.01–2.20) per 10 g increase in scar border zone.
Extent of infarct border zone is the strongest predictor of subsequent ICD therapy
Kwon et al. 200950
≥2 SDs above remote normal myocardium
1.02 (1.003–1.03) per 1% increase in LV scar
Scar mass predicts mortality or transplantation
Kelle et al. 2009121
Number of AHA 17 segment model with enhancement
1.27 (1.064–1.518) per additional enhanced segment
Number of AHA segments involved predicts death and non-fatal myocardial infarction.
Heidary et al. 201057
FWHM border zone (remote max to 50%), FWHM scar core (>50%)
Not given, p=0.03
Total scar mass and border zone mass (but not scar core mass) predict adverse outcomes
Scott et al. 201153
The number of transmural scar segments (using AHA 17 segment model)
1.48 (1.18–1.84) in multivariate analysis
The number of transmural scar segments predicts subsequent ICD therapies
Krittayaphong et al. 2011122
1,148 Visual presence of LGE
3.92 (1.98–7.76) in multivariate analysis
LGE predicts MACE in a cohort with normal wall motion.
Boyé et al. 2011123
Not given, p=0.02
Infarct mass expressed as a percentage of LV mass predicts appropriate device therapy
Rubenstein et al. 201359
Between 2 and 3 SD above remote normal myocardium
1.97 (1.04–3.73) per 1% change in border zone mass in multivariate analysis
Border zone mass higher in those with VT inducibility (2.64% of LV mass) than those without (1.35%)
Alexandre et al. 2013124
Scar mass by manual planimetry
1.08 (1.04–1.12) unadjusted, 3.15 (1.35-7.33) in multivariate analysis (per 1g extra scar mass)
Scar mass predicts appropriate device therapy
Kwon et al. 2014125
≥2 SD above remote normal myocardium
1.34 (1.15–1.55) in multivariate analysis
Scar percentage strongly predicts mortality
Demirel et al. 2014126
2.01 (1.17–3.44) in multivariate analysis
Ratio of peri-infarct border zone to scar core is associated with appropriate ICD therapy
Rijnierse et al. 2016127
Not given, p=0.07
Trend towards higher scar burden in those with inducible VT (not significant)
Non-ischaemic Cardiomyopathy Assomull et al. 200662
Visual presence of midwall LGE
3.4 (1.4–8.7) for presence of LGE
Presence of midwall fibrosis predicts death or hospitalisation
Wu et al. 200861
Visual presence of LGE
8.2 (2.2–30.9) in multivariate analysis
Presence of LGE predicts cardiovascular death, ICD therapy and HF hospitalisation
Iles et al. 2011128
Visual presence of LGE
Not given, p=0.01
Patients with LGE had significantly higher rates of appropriate ICD therapy
Lehrke et al. 2011129
Visual presence of LGE, SD >2 for quantification
3.5 for presence of scar. 5.28 using threshold of scar >4.4% total LV mass
Presence of LGE predicts cardiac death, ICD therapy or HF hospitalisation
Neilan et al. 2013130
Both FWHM and SD methods used
14.5 (6.1–32.6) for LGE presence, 1.15 (1.12–1.18) for each 1% increase in scar volume
Presence and volume of LGE predicts cardiovascular death or ICD therapy
Gulati et al. 2013131
Visual presence, FWHM
2.96 (1.87–4.69) for presence of LGE, 1.1 (1.06–1.17) per 1% extra LGE
LGE presence, extent predicts mortality, independently of LVEF
Machii et al. 2014132
Visual presence of LGE
Not given, p=0.02 for extensive LGE versus no LGE
Lower event-free survival in patients with extensive LGE (Continued)
ARRHYTHMIA & ELECTROPHYSIOLOGY REVIEW
Electrophysiology and Ablation Table 2: Cont. Perazzolo-Marra et al. 2014133
Visual presence of LGE
3.8 (1.3–10.4) in multivariate analysis
LGE presence, but not extent, predicts adverse arrhythmic outcome
Masci et al. 2014134
Visual presence of LGE
4.02 (2.08–7.76) in multivariate analysis
LGE presence predicts adverse outcomes in patients with asymptomatic LVSD
Piers et al. 201568
Visual presence, FWHM
2.71 (1.10–6.69) for LGE presence
LGE predicts monomorphic VT, but not polymorphic VT/VF
Shin et al. 2016135
8.45 (2.91–24.6) for LGE extent ≥ 8%, increasing to 6.98 (1.74–28.0) for those with subepicardial pattern of disease
Presence of LGE strongly predicts arrhythmic events, risk varies with location of fibrosis
Visual presence of LGE
Presence of LGE predicts VT inducibility
1.1 (1.07–1.17) per 10 ms change in T1 time, multivariate analysis
Higher T1 values predict mortality and HF outcomes
Halliday et al. 201763
Visual presence of LGE, FWHM for quantification
9.2 (3.9–21.8) in patients with LVEF > 40%
A 17.8% event rate (median follow-up 4.6 years) in patients with LGE
Halliday et al. 201665
LGE extent of 0 to 2.55%, 2.55% to 5.10%, and >5.10%, respectively, were 1.59 (0.99 to 2.55), 1.56 (0.96 to 2.54), and 2.31 (1.50 to 3.55) for all-cause mortality
The presence and pattern, rather than the extent, of LGE predicts all-cause mortality
8.29 (3.92–17.5) unadjusted, 8.65 (2.45–30.5) in multivariate analysis
Presence of LGE predicts cardiac events in patients with suspected CAD
Mueller et al. 2016136 Puntmann et al. 2016
Studies Including Both ICM and NICM Kwong et al. 2006138
Klem et al. 201151
1560 Number of segments with LGE
1.007 (1.005–1.009) unadjusted, 1.004 (1.002–1.007) in multivariate analysis
Number of segments with LGE incrementally prediction of all-cause mortality over LVSF and clinic parameters
Gao et al. 201256
1.4 (1.21–1.62) unadjusted
Scar quantification predicts arrhythmic events
Dawson et al. 2013139
Visual presence of LGE, FWHM for quantification
3.5 (2.01–6.13) for presence of LGE, 1.12 per 5% extra LGE
In patients presenting with VT, LGE predicts arrhythmic events
Almehmadi et al. 2014140
2.4 (1.2–4.6) in multivariate analysis
Midwall striation predicts sudden death or appropriate ICD therapy
Chen et al. 201570
Native T1 value
1.1 (1.04–1.16) per 10 ms change in T1 time, multivariate analysis
Myocardial T1 predicts ventricular arrhythmia independently of scar quantification
Mordi et al. 2015141
Visual presence of LGE
2.14 (1.06–4.33) in multivariate analysis
LGE predicts MACE in all-comers attending for CMRI
Acosta et al. 201860
FWHM 40–60% (border zone), >60% (scar core)
1.06 (1.04–1.08) for border zone mass (g)
Scar mass, border zone mass and border zone channel mass all predict ICD therapy or SCD
Olausson et al. 201835
2.17 (1.17–4.00) for each 5% increase in ECV
Diffuse fibrosis (as evidenced by ECV) predicts appropriate ICD therapy
Studies showing the prognostic effect of CMRI data in ischaemic cardiomyopathy and non-ischaemic cardiomyopathy. AHA = American Heart Association; CMRI = cardiac MRI; EPS = electrophysiology study; ECV = extracellular volume; FWHN = full width at half maximum; HF = heart failure; LGE = late gadolinium enhancement; LV = left ventricle; LVEF = left ventricular ejection fraction; MACE = major adverse cardiac event; VT = ventricular tachycardia.
Extracellular Volume and T1 Mapping for Risk Stratification Alternative metrics, such as native T1 values and ECV, measure diffuse myocardial fibrosis. In patients with both ICM and NICM, myocardial T1 values (at sites spatially discrete from areas of LGE) incrementally improved risk stratification in a model that already included LVEF, QRS duration, and metrics of scar core and border zone (using LGE).70 In a similar study using ECV rather than T1, high ECV values correlated with mortality.71 In two small case series, high ECV values correlated with ICD therapies.35,72 These studies suggest that, when dense scar is surrounded by diffusely fibrotic myocardium, VT/VF is more likely than if the scar is encompassed by normal myocardium. ECV and T1 mapping techniques have a sound physiological basis for identifying diffusely abnormal myocardium not identified with LGE imaging. ECV is of particular interest as a marker of risk in patients
with NICM who do not have identifiable LGE, since it offers the ability to identify diffuse interstitial fibrosis. Complementary assessment of diffuse and regional disease by ECV mapping and LGE respectively may provide incremental benefit for risk stratification in both ICM and NICM. ECV may also have value in further characterising the density of discrete scars, although data to support this use are limited.
Ablation for Ventricular Tachycardia For patients with a high burden of VT, catheter ablation can successfully reduce ICD shocks.73–75 These procedures can be challenging, with significant morbidity and mortality, since VT is frequently poorly tolerated and precise localisation of re-entrant circuits using traditional electrophysiological techniques is often challenging. VT ablation therefore often targets the myocardial scar substrate.76 Differing approaches to substrate ablation have been described – linear transection, core isolation, scar homogenisation or abolition of late
ARRHYTHMIA & ELECTROPHYSIOLOGY REVIEW
Ischaemic and Non-ischaemic Dilated Cardiomyopathy potentials.77–80 Often, this requires extensive, time-consuming ablation in haemodynamically fragile individuals, which could be streamlined with a more detailed appreciation of the underlying substrate. CMRI can be used to predict the location of re-entrant circuits and channels within the scar to guide ablation lesions, the success of which can be predicted by computer modelling.81,82
Figure 3: Image Post-processing of 2D Cardiac MRI Images A
Planning The configuration of LGE on CMRI allows the operator to estimate the likelihood of successful ablation and identify whether epicardial access is required. Predominantly subendocardial ischaemic scarrelated VT is usually treatable with endocardial ablation.75 Conversely, VT ablation in NICM may be hampered by inaccessibility of the substrate, and epicardial access may be required for patients with inferolateral and/or subepicardial scarring.83 Epicardial access is typically not required for patients with VT originating from a septal intramural scar, although outcomes from ablation of ‘deep’ substrate are poorer, as might be expected.84
Short axis late gadolinium enhancement images (A) are contoured to identify endo- and epicardial boundaries, before a full width at half maximum thresholding approach identifies areas of dense scar (red) and border zone (yellow), then (B) the short axis stack is reconstructed to form a 3D volume (C) which can be imported into electroanatomical maps software.
Figure 4: 3D Multiplanar Reconstruction
Image Fusion Conventional 3D electroanatomical maps (EAMs) generated during ablation procedures may be inaccurate because of poor catheter contact or reach, and contact mapping of entire cardiac chambers is time consuming.
geometries can be used simply as a road map for the operator during ablation procedures. Alternatively, fusion of these 3D geometries with the EAM system can leverage the accurate and high resolution anatomical detail of clinical imaging, allowing the operator to observe CMRI (and/or CT) images directly in the mapping software to reduce the time spent generating EAMs.86–89 Contact mapping can be focused on regions of interest determined in advance, e.g. by using algorithms for localising the VT origin based on 12-lead ECG morphology or by non-invasive mapping (ECGI) techniques.90–95 While image fusion has the potential to streamline ablation procedures, as yet, the benefits of such an approach have not been formally evaluated, and widespread applicability is not assured since it requires significant clinical and imaging expertise.
Future Directions Overcoming Technical Limitations of Cardiac MRI Many patients at risk of VT/VF have cardiac implantable electronic devices (CIEDs).96 Historically, MRI has been contraindicated in patients with CIEDs due to safety concerns. However, with advances in CIED technology such as MRI-conditional devices, growing experience and appropriate precautions and monitoring, CMRI can often be performed safely even in patients with historic non-conditional devices.69,97,98 Nevertheless, images may be significantly degraded by the presence of CIEDs, particularly the anteroseptal regions of the left ventricle in patients with left-sided pulse generators that lie in close proximity to the heart. Wideband sequences are described which can reduce these artefacts.99 LGE imaging is usually obtained by multiple short axis planes through the heart. This results in excellent in-plane resolution, but a large slice width (approximately 10 mm) between images. Reconstructions of the heart can suffer with a ‘partial voluming’ artefact that can overestimate the infarct
ARRHYTHMIA & ELECTROPHYSIOLOGY REVIEW
8.0e+02 750 700 650 600 550 500 450 400 350 300 250 200 150 100
Left ventricular scar map
Clinical CMRI studies can be reconstructed into 3D geometries demonstrating the distribution of a scar (Figure 3). With further refinement using 3D acquisition and image-processing methods, channels that might facilitate re-entry can be identified in advance (Figure 4).85 These
0.0e+00 Conventional clinical 2D late gadolinium enhancement imaging can pose challenges for reconstruction including slice alignment. This example of 3D multiplanar reconstruction (performed at our own institution) yields more realistic geometry with areas of dense scar (red) and border zone (blue). Putative conducting channels have been indicated and numbered in preparation for ventricular tachycardia ablation.
border zone.100 This effect can be mitigated by evolving techniques such as 3D image acquisition or super-resolution image reconstruction.101,102 Histological studies have demonstrated myocyte fibre disarray at the border zone of a chronic infarction.103 Due to anisotropic conduction of myocytes, knowledge of fibre orientation is potentially important to understand propensity to arrhythmia. Diffusion tensor imaging can demonstrate fibre direction and may therefore inform computer models of arrhythmia, although this use of CMRI is in its infancy.12,104,105
Ventricular Tachycardia Stimulation and Modelling Inducibility of VT during an electrophysiology study (EPS) by programmed ventricular stimulation (PVS) pacing from a right ventricular site predicts arrhythmic events in ICM.106 This meta-analysis demonstrated PVS had the power to predict subsequent arrhythmic events (pooled OR 4.00, 95% CI [2.30–6.96]). Depending on patient selection and the number of extrastimuli used, the sensitivity, specificity and predictive value of this test varies, although is not commonly used clinically due to its invasiveness, cost and insufficient negative predictive value. In NICM, assessment with PVS is less well studied and probably less effective than with ICM.107
Electrophysiology and Ablation Scar-related re-entry often relies upon functional block as well as anatomical barriers to conduction. 108 Scar quantification methods do not account for these complex mechanisms, but computer modelling has the potential to improve risk stratification by combining a personalised anatomical model with simulation of tissue electrophysiology. This method allows simulated PVS performed from multiple sites in both ventricles. In a retrospective study of 41 patients with severe LVSD, by comparing these patientspecific simulations with clinical outcomes, a positive ‘virtual-heart arrhythmia risk predictor’ simulation was associated with adverse outcomes (OR 4.05 (95% CI [1.20–13.8]), which is similar to published results from invasive PVS. Work is ongoing to determine the utility of such simulations in preserved LVSF.82,109 Simulated PVS methods are computationally significantly more challenging in NICM where myocardial fibrosis is less confluent and more heterogeneous, and the microscopic nature of the substrate is difficult to fully characterise with clinical imaging. Moreover, the substrate in NICM is often progressive and, as such, risk stratification at a single time point may fail to accurately estimate lifetime risk. These methods are promising but are potentially limited by simplifications and assumptions in models of cell and tissue electrophysiology, the computational resources required, and the resolution of currently available clinical imaging. Despite encouraging preliminary studies, there are significant obstacles to be overcome before these approaches can be used routinely in clinical practice.110 Constructing a personalised computational model of anatomy and electrophysiology requires calibration from clinical images and data that are often noisy and incomplete, so methods for embedding uncertainties and variability into computational models are an area of active research.111 Whether these approaches can be used to guide ICD implantation in the future remains to be seen. Technological advances in imaging and modelling, along with clinical studies of their utility, will help advance this promising concept.
Future Clinical Studies Tissue characterisation to determine who needs and, perhaps more importantly, who does not need an ICD is a complex but evolving field. Estimates of risk currently do not allow for disease progression, and it is unclear how frequently investigations should be repeated, particularly for the dynamic substrate that occurs in some forms of NICM. The effect of dynamic conditions such as electrolyte disturbance, volume overload and myocardial ischaemia on arrhythmic risk remains unknown. In the DANish Randomized, Controlled, Multicenter Study to Assess the Efficacy of Implantable Cardioverter Defibrillator in Patients With Nonischemic Systolic Heart Failure on Mortality (DANISH) trial (NCT00542945), investigators found no overall mortality benefit for primary prevention ICD implantation in patients with NICM.112 However, outcomes were improved by ICD implantation for those in prespecified subgroups – namely younger patients and those with lower levels of N-terminal pro-brain natriuretic peptide (NT-proBNP) – who, presumably, had a lower risk of non-sudden death. Since CMRI studies have consistently demonstrated a higher arrhythmic burden in those with evidence of LGE, a clinical trial that used CMRI-based risk stratification in NICM patients with LVEF <35% would provide clinically useful information. Similarly, the Cardiac Magnetic Resonance GUIDEd Management of Mild-moderate Left Ventricular Systolic Dysfunction (CMR_GUIDE) trial
(NCT01918215) will identify patients who have evidence of LGE but do not qualify for ICD treatment under current guidelines (LVEF 35–50%), to determine whether prophylactic ICD implantation is beneficial.113 The Programmed Ventricular Stimulation to Risk Stratify for Early CardioverterDefibrillator (ICD) Implantation to Prevent Tachyarrhythmias Following Acute Myocardial Infarction (PROTECT-ICD) trial will examine whether a multiparametric risk stratification algorithm (including echocardiography, CMRI and PVS) used post-infarction will identify those who may benefit from early ICD implantation.114
Contribution to Novel Therapies Recent developments in CMRI and electrophysiology mapping systems have shown real-time tracking and visualisation of catheter position during ablation procedures to be feasible and safe for an ‘anatomical’ ablation of the cavo-tricuspid isthmus.115,116 Advantages of such a system include 3D visualisation of catheter position within complex anatomical structures (including the ability to see surrounding structures) and real-time lesion evaluation. This technology has the potential to improve outcomes in ablation procedures, but significant technological challenges remain for its use in ventricular arrhythmia. Stereotactic body radiotherapy has recently been reported as a novel, non-invasive treatment for VT.117,118 It is dependent on accurate anatomical localisation of arrhythmic substrate to determine the radiotherapy target. CMRI imaging is the ideal modality for treatment planning.
Conclusion CMRI imaging can accurately quantify cardiac function, and characterise the myocardial substrate to refine risk stratification to identify people who may benefit from ICD implantation and revascularisation. Although large-scale trials in this area are required, it is likely that measures of scar quantification will become increasingly recognised by guidelines in future. A multiparametric approach using imaging and other criteria may provide the most accurate risk assessment in the future, although the interaction between each of the metrics discussed is complex and requires careful study. Advanced techniques such as automated image segmentation and channel detection, or computer simulation of electrophysiology, offer significant potential, but are still in the early stages of development. Significant challenges remain in overcoming technological barriers and understanding how best to use the considerable information gained from a CMRI study. Nevertheless, CMRI offers clinicians and researchers an increasingly comprehensive way to diagnose, risk stratify and tailor the treatment of patients with cardiomyopathy.
Clinical Perspective • Cardiac MRI (CMRI) is the gold standard imaging modality for ejection fraction and myocardial tissue characterisation. • CMRI evidence of fibrosis independently predicts arrhythmic risk, even in multiparametric models which include clinical risk factors and ejection fraction, in both ischaemic and nonischaemic cardiomyopathies. • CMRI can be used to inform and guide ablation procedures by characterising the ventricular tachycardia substrate. • Novel metrics such as extracellular volume mapping and channel identification have the potential to aid the electrophysiologist and provide a more robust method of risk stratification.
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magnetic resonance for risk stratification in chronic heart failure: prognostic value of late gadolinium enhancement in patients with non-ischaemic dilated cardiomyopathy. Heart 2011;97:727–32. https://doi.org/10.1136/hrt.2010.205542; PMID: 21097819. 130. Neilan TG, Coelho-Filho OR, Danik SB, et al. CMR Quantification of myocardial scar provides additive prognostic information in nonischemic cardiomyopathy. JACC Cardiovasc Imaging 2013;6:944–54. https://doi.org/10.1016/j. jcmg.2013.05.013; PMID: 23932642. 131. Gulati A, Jabbour A, Ismail TF, et al. Association of fibrosis with mortality and sudden cardiac death in patients with nonischemic dilated cardiomyopathy. JAMA 2013;309:896–908. https://doi.org/10.1001/jama.2013.1363; PMID: 23462786. 132. Machii M, Satoh H, Shiraki K, et al. Distribution of late gadolinium enhancement in end-stage hypertrophic cardiomyopathy and dilated cardiomyopathy: differential diagnosis and prediction of cardiac outcome. Magn Reson Imaging 2014;32:118–24. https://doi.org/10.1016/j. mri.2013.10.011; PMID: 24315973. 133. Perazzolo Marra M, De Lazzari M, Zorzi A, et al. Impact of the presence and amount of myocardial fibrosis by cardiac magnetic resonance on arrhythmic outcome and sudden cardiac death in nonischemic dilated cardiomyopathy. Heart Rhythm 2014;11:856–63. https://doi.org/10.1016/j. hrthm.2014.01.014; PMID: 24440822. 134. Masci PG, Doulaptsis C, Bertella E, et al. Incremental prognostic value of myocardial fibrosis in patients with non-ischemic cardiomyopathy without congestive heart failure. Circ Heart Fail 2014;7:448–56. https://doi.org/10.1161/ CIRCHEARTFAILURE.113.000996; PMID: 24647118. 135. Shin DG, Lee H-J, Park J, et al. Pattern of late gadolinium enhancement predicts arrhythmic events in patients with non-ischemic cardiomyopathy. Int J Cardiol 2016;222:9–15.
https://doi.org/10.1016/j.ijcard.2016.07.122; PMID: 27458824. 136. Mueller KAL, Heck C, Heinzmann D, et al. Comparison of ventricular inducibility with late gadolinium enhancement and myocardial inflammation in endomyocardial biopsy in patients with dilated cardiomyopathy. PLoS One 2016;11:e0167616. https://doi.org/10.1371/journal. pone.0167616; PMID: 27930686. 137. Puntmann VO, Carr-White G, Jabbour A, et al. T1-mapping and outcome in nonischemic cardiomyopathy: allcause mortality and heart failure. JACC Cardiovasc Imaging 2016;9:40–50. https://doi.org/10.1016/j.jcmg.2015.12.001; PMID: 26762873. 138. Kwong RY, Chan AK, Brown KA, et al. Impact of unrecognized myocardial scar detected by cardiac magnetic resonance imaging on event-free survival in patients presenting with signs or symptoms of coronary artery disease. Circulation 2006;113:2733–43. https://doi.org/10.1161/ CIRCULATIONAHA.105.570648; PMID: 16754804. 139. Dawson DK, Hawlisch K, Prescott G, et al. Prognostic role of CMR in patients presenting with ventricular arrhythmias. JACC Cardiovasc Imaging 2013;6:335–44. https://doi.org/10.1016/j. jcmg.2012.09.012; PMID: 23433931. 140. Almehmadi F, Joncas SX, Nevis I, et al. Prevalence of myocardial fibrosis patterns in patients with systolic dysfunction: prognostic significance for the prediction of sudden cardiac arrest or appropriate implantable cardiac defibrillator therapy. Circ Cardiovasc Imaging 2014;7:593–600. https://doi.org/10.1161/CIRCIMAGING.113.001768; PMID: 24902587. 141. Mordi I, Bezerra H, Carrick D, Tzemos N. The combined incremental prognostic value of LVEF, late gadolinium enhancement, and global circumferential strain assessed by CMR. JACC Cardiovasc Imaging 2015;8:540–9. https://doi. org/10.1016/j.jcmg.2015.02.005; PMID: 25890580.
Electrophysiology and Ablation
Mapping and Imaging in Non-paroxysmal AF Michael Ghannam and Hakan Oral Cardiac Arrhythmia Service, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, US
Abstract Despite intense research efforts, maintenance of sinus rhythm in patients with non-paroxysmal AF remains challenging with suboptimal outcomes. A major limitation to the success of current ablation-based treatments is that our understanding of AF pathophysiology is incomplete. Advances in imaging and mapping tools have been reported to improve ablation outcomes. However, the role of these new approaches on the clinical care of patients with AF remains to be validated and better understood before wide adoption can occur. This article reviews the current techniques of imaging and mapping that can be applied in the management of patients with non-paroxysmal AF with a focus on their relevance to catheter ablation. Future applications and opportunities for new knowledge are also discussed.
Keywords AF, catheter ablation, atrial mapping, atrial imaging, computed tomography, cardiac MRI Disclosure: The authors have no relevant conflicts of interest to declare. Received: 29 January 2019 Accepted: 25 April 2019 Citation: Arrhythmia & Electrophysiology Review 2019;8(3):202â€“9. DOI: https://doi.org/10.15420/aer.2019.18.1 Correspondence: Hakan Oral, Cardiovascular Center, SPC 5853, 1500 East Medical Center Drive, Ann Arbor, MI 48109-5853, US. E: firstname.lastname@example.org Open Access: This work is open access under the CC-BY-NC 4.0 License which allows users to copy, redistribute and make derivative works for non-commercial purposes, provided the original work is cited correctly.
Maintenance of sinus rhythm in patients with non-paroxysmal AF is often challenging and complex. Catheter ablation is usually superior to anti-arrhythmic drug therapy alone. However, recurrence rates are high and have remained suboptimal. Although pulmonary vein isolation (PVI) is usually effective in treating paroxysmal AF, it is not sufficient for many patients with non-paroxysmal AF, particularly those with longstanding persistent AF. A major limitation is that our understanding of mechanisms of AF is incomplete. Electroanatomical remodelling adds substantially to the complexity of AF. Recent advances in imaging and mapping technology may facilitate better understanding and mapping of AF and subsequently improve outcomes of therapy. The purpose of this article is to review current techniques of imaging and mapping that can be applied in the management of patients with non-paroxysmal AF with a focus on their relevance to catheter ablation. Future applications and opportunities for new knowledge will also be discussed.
with non-paroxysmal AF, ectopic triggers can include pulmonary vein and non-pulmonary vein triggers such as the superior vena cava, coronary sinus or crista terminalis.3 Electroanatomical remodelling facilitates trigger formation, shortening of atrial refractory periods and the promotion of fibrosis.4â€“6 Elimination of focal triggers in addition to PVI improves ablation outcomes.7 Two competing theories on AF maintenance have been proposed and have formed the basis for various ablation techniques. The first is the multiple wavelet theory, which describes AF as self-perpetuating rhythm independent of focal discharges but rather supported by a critical mass of myocardium allowing constant formation and dispersal of wavelets.8,9 The second is a localised source model in which focal areas of re-entry or discharges sustain AF. These organised regions result in disorganised fibrillation due to wave-break as the impulses encounter tissue with anisotropy and conduction heterogeneity.10 These competing mechanisms may not be mutually exclusive, particularly in patients with non-paroxysmal AF.11
Mechanisms of AF Descriptions of AF wavefront properties are influenced by the study tools (optical mapping versus electroanatomical contact or noncontact mapping) and can explain some of the discrepancies in experimental studies and clinical observations.1,2 Imaging studies have shown a close association with abnormal atrial architecture and clinical AF, but a unified model relating ultrastructural remodelling to arrhythmia maintenance is less than complete. Multidisciplinary approaches incorporating imaging, basic science, computer modelling, and clinical observations are vital for addressing these knowledge gaps. Current models of non-paroxysmal AF often focus on both triggers and an atrial substrate able to perpetuate the arrhythmia. For patients
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Contemporary ablation strategies for non-paroxysmal AF are based on elimination of prevalent triggers (including pulmonary vein and other thoracic arrhythmogenic sites), dynamic real-time mapping of drivers of AF such as rotors and focal discharges or modification of the atrial substrate using a combination of anatomical lesion sets. These approaches have been used alone or in combination with variable clinical outcomes. Hybrid techniques such as combined endocardial and epicardial ablation have been proposed to target the posterior wall and epicardial sources of AF.12 Modulation of autonomic inputs through ablation of ganglionated plexi have also been considered as an adjunctive ablation strategy.13
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Non-paroxysmal AF Cardiac and Thoracic Imaging Multimodality imaging can improve management of patients with nonparoxysmal AF and assist with ablation strategies (Figure 1). Traditional measurements of atrial size and morphology have been supplemented with functional and structural assessments through techniques such as strain imaging and late-gadolinium enhanced cardiac magnetic resonance imaging (LGE-CMR). Nuclear imaging such as PET can measure atrial inflammation and metabolism as it pertains to AF.14–16 In a recent pilot study, left atrial uptake of 18F-fluorodeoxyglucose was assessed through PET-CT scans.16 Left atrial metabolism and scarring were found to be increased in patients with greater AF burdens. Further validation of these novel techniques is needed. Intraprocedural imaging, including intracardiac echocardiography and 3D rotational angiography, can be integrated with fluoroscopy and electroanatomical mapping to provide real-time information of challenging anatomy.17 Current clinical applications of imaging include procedural planning, patient selection, prognostication of arrhythmia recurrence and evaluation of postoperative complications, such as pulmonary vein stenosis.
Atrial Size and Morphology Atrial volume can be measured through a variety of non-invasive modalities including echocardiography, CT or CMR imaging with good agreement among the studies.18–21 3D echocardiography is more accurate and reproducible than 2D echocardiography and is preferred when available (Figure 1A).22 Increased atrial size is linked to poorer outcomes after ablation particularly in patients with non-paroxysmal AF.23 Abnormal atrial morphology (increased sphericity) is a marker of advanced remodelling and also a predictor of poor outcomes including stroke (Figure 1B).24,25 These phenotypic changes identify patients who may require more aggressive management to maintain sinus rhythm and prevent thromboembolic complications. Anatomic variation in pulmonary vein anatomy is observed in 20–30% of patients undergoing catheter ablation and may impact long-term ablation outcomes.26–28 Pre-procedural imaging of the pulmonary veins can be helpful in selecting an ablation strategy although the impact on outcomes, procedural safety, fluoroscopic exposure and long-term costs are less established.29–31 Cross-sectional imaging can also identify the course of thoracic structures such as phrenic vein and coronary arteries. Understanding the proximity of these structures to the atrium can be critical when ablating near the left atrial appendage (LAA) or left lateral ridge,both of which are commonly targeted in patients with nonparoxysmal AF.32 The left main coronary artery and left circumflex artery may course near the LAA ostia and cases of ablation-related coronary vasospasm have been reported.32 Integrating electroanatomical data, intracardiac ultrasound and cross-sectional imaging is often used to avoid these complications. The oesophagus and its relationship to the posterior atrial wall can be visualised on cross-sectional imaging and superimposed into electroanatomical mapping systems, which may avoid ablation related oesophageal injury. Due to lateral oesophageal motility that occurs during ablation procedures, real-time monitoring of the oesophageal location and luminal temperature is likely superior to a static pre-procedural assessment.33
Atrial Wall Thickness The atrium is a thin-walled, pliable structure whose size and geometry are dependent upon volume status and loading conditions. There is regional and inter-patient variability in wall thickness; pathologic and in vivo studies generally agree that the average left atrial wall thickness
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is between 1–4 mm and can range between 0.5–12 mm.22,34 There is an age-related increase in left atrial wall thickness and men generally have thicker atrial walls than women.35,36 While atrial volumes and structure are impacted by AF, a direct relationship between atrial wall thickness and AF burden is less clearly demonstrated.37–39 Given its high spatial resolution, CT can most accurately measure atrial wall thickness in vivo although this is not without limitations. CT-based measurements are generally reported to be lower than ex vivo tissue samples, which may relate to the importance of loading conditions, the effect of pathological sample preparation or an inherent limitation to CT scanning.11 Assessment of left atrial wall thickness may have implications for patients with non-paroxysmal AF undergoing ablation procedures. Takahashi et al. performed high-resolution CT scans on 50 patients with AF and compared this to 25 control patients without AF. There was an increase in the thickness of the pulmonary vein–left atrial wall junction that was dependent on AF burden. Patients with thicker walls had higher incidences of ATP-provoked dormant conduction.38 Suenari et al. demonstrated that regional atrial thickness at the left lateral ridge was an independent predictor or arrhythmia recurrence after PVI.40 In a retrospective study of patients with non-paroxysmal AF undergoing LAA isolation, ostial wall thickness measured on pre-procedural CT scan was found to correlate with electrical reconnection. A retrospective evaluation of patients undergoing PVI reported that the ratio of ablation lesion force-time-integral to the underlying atrial wall thickness could accurately predict conduction gaps and dormant conduction.41 Wall thickness information could facilitate a strategy of tailored ablation energy delivery. Ablation energy delivery is generally titrated both by electrogram parameters (impedance changes and electrogram attenuation) as well as empirically based on anatomic location, with lower power delivery along the posterior wall and higher power delivery along thicker structures such as the left atrial ridge. This paradigm does not account for the wide range of wall thickness seen through all regions of the atrium. In a study of 60 patients with persistent AF undergoing catheter ablation, there was a similar range of wall thickness in areas normally found to be thinner such as the posterior wall (0.7–3.1 mm), as there were in thicker regions such as the left lateral ridge (0.5–3.5 mm) and mitral isthmus origin (0.9–2.8 mm). Whether a wall-thickness-guided ablation strategy would improve lesion durability is unclear. Opportunities for study also exist with other forms of titratable ablation energy such as the visually-guided laser balloon ablation system.42 Investigational ablation technologies using low-intensity collimated ultrasound can directly measure left atrial wall thickness which may in the future be incorporated into dosing strategies.43
Atrial Strain Pathological atrial remodelling can lead to chamber dilation, geometric distortion and fibrosis, which may lead to a substrate for arrhythmias including AF. These structural alterations also affect wall compliance and myocyte contractility with subsequent impairment of the normal atrial function as a conduit, reservoir and a contractile chamber. Realtime imaging with echocardiography and CMR can quantify atrial function through atrial strain assessment, which has been correlated with adverse cardiovascular outcomes include atrial arrhythmias.44–46 Abnormal atrial strain is associated with an increased incidence of AF as well as higher post-ablation recurrence rates in patients with
Electrophysiology and Ablation Figure 1: Left Atrial Imaging A
Step 1: Acquire axial views of left atrium Single centre
Step 3: Quantify enhancement
Step 2: Isolate and identify left atrial wall
Step 4: Render 3D model of left atrium
A: The use of 3D ultrasound to evaluate left atrial volume and functional parameters. An automated 3D reconstruction is superimposed on a greyscale echocardiography dataset. B: Cardiac MRI reconstruction of left atrial volume and morphology. Reconstruction of the 3D anatomic shell can be compared to a theoretical sphere shaped atrium to calculate the sphericity, which may hold prognostic significance. C: Late gadolinium enhanced cardiac MRI can be used to measure location of atrial fibrosis. The extent of fibrosis can be used to categorise patients into a novel scoring system, which has prognostic importance for arrhythmia recurrence after ablation procedures. Sources: Mor-Avi et al.;21 Siebermai et al.58 Reproduced with permission from Elsevier. den Uijl et al.24 Reproduced with permission from John Wiley and Sons.
paroxysmal and non-paroxysmal AF.47–49 In a study of 65 patients with paroxysmal and persistent AF who underwent multi-modality imaging, abnormal atrial strain parameters correlated with the extent of LGECMR derived atrial fibrosis independent of left atrial volumes.50 Atrial strain and the extent of fibrosis were more severe in patients with persistent rather than paroxysmal AF. These observations highlight the overlapping relationship between structural, mechanical, and electrical atrial remodelling. Unlike traditional measures of left atrial compliance and function that rely on pulmonary vein and transmitral flow, atrial strain can be assessed while in sinus rhythm or AF.36 Wall strain may also be a useful surrogate for assessing wall fibrosis as measured on LGE-MRI51 and correlates with ablation outcomes.
Atrial Fibrosis Advances in spatial and temporal resolution of CMR imaging allow for accurate imaging of the thin-walled atrial structures (Figure 1C). Multiple parameters including atrial wall thickness, functional
left atrial parameters and fibrosis can be obtained simultaneously through CMR.44,52,53 Assessment of atrial fibrosis through LGE-CMR has been performed for over a decade and has been shown to correlate spatially with both histopathological samples and endocardial voltage mapping.54–56 However, there can be significant technical challenges that limit its broad applicability. Patients can be imaged while in AF although both irregular and rapid heart rates can limit image quality or render fibrosis assessment impossible.57–59 Rhythm and rate control prior to imaging can be challenging in patients with non-paroxysmal AF. Cardiac devices may be sources of significant artifact.60 Beyond image acquisition challenges, interpretation of LGE-CMR images is variable among centres and the optimal processing technique remains to be better described. Many protocols require expert operator input to select appropriate detection thresholds,limiting the external validation of these studies.61 Despite these current challenges, inter- and intraobserver agreement in high-volume centres remains high.62 The efficacy of DE-MRI-guided ablation vs. Conventional catheter Ablation of Atrial Fibrillation (DECAAF) study was a large multicentre observational study of patients with AF undergoing catheter based ablation who had LGE-CMR performed prior to their procedures.63 Of the 260 patients included in the final analysis, 75 (28.8%) had persistent AF and 17 (6.5%) permanent AF. The majority of patients had PVI only (68.1%). Arrhythmia recurrence post-ablation was related to the extent of global atrial fibrosis (unadjusted HR 1.06; 95% CI [1.03–10.8]; p<0.001) and was independent of covariates including age, sex, hypertension, congestive heart failure, left ventricular ejection fraction, left atrial volume or AF subtype among others. Similar relationships between the degree of left atrial fibrosis and outcomes after ablation have been shown in single centre studies.64,65 LGE-MRI has also been used to evaluate left atrial substrate post-catheter ablation. Applications such as assessment of lesion quality, identifying conduction gaps, or quantifying residual fibrosis post-ablation have also been reported.60,65–70 However, the clinical significance of these approaches remains to be determined.71 Given the correlation of atrial fibrosis and ablation outcomes in observational studies, LGE-CMR could be used in selection of ablation candidates. In the DECAAF study, the addition of fibrosis data to traditional clinical risk factors to recurrence prediction models resulted in a small but significant increase in prognostic accuracy for the recurrence of AF (risk difference 0.05; 95% CI [0.01–0.09]). The clinical utility of such strategies has not been prospectively investigated. For patients with non-paroxysmal AF, LGE-CMR defined fibrosis may be used to guide the ablation strategy by providing a personalised ablation approach. A sub-study of the DECAAF trial examined 177 patients with follow up LGE-CMR performed 90 days after their initial ablation procedure. The amount of residual fibrosis (preablation fibrosis that was not targeted through ablation lesions) was associated with arrhythmia occurrence.72 Targeting LGE-CMR defined fibrosis may improve ablation outcomes and is the focus of the on-going DECAAF-2 trial.73 Novel strategies incorporating LGEMRI defined substrate into in silico atrial models to predict optimal ablation sites are also under investigation.74 Given the challenges in technique and reproducibility and lack of prospective studies, the current role of LGE-CMR for management of patients with non-paroxysmal AF remains limited. LGE-CMR defined fibrosis may harbour critical drivers of persistent AF.75 Substrate-based
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Non-paroxysmal AF Figure 2: Mapping of Atrial Electrical Activity Right atrium
Spatio-temporal dispersion (With or without fractionation)
Delineation of a dispersion area
A 3-4 B 5-6 B 7-8 C 9-10 C11-12
No spatio-temporal dispersion (With or without fractionation)
Ablation at a dispersion area
D15-16 E17-18 E19-20
0 ms 1 cm
Time Left atrium
D Phase +π
2 t=813 ms
900 1,000 1,100 0.2 mV 100 ms
Invasive and non-invasive mapping techniques integrate multiple local and far-field electrocardiogram signals to represent wavefront propagation during AF. Analysis of these visualisations can help localise focal drivers of AF or the substrate necessary to sustain the arrhythmia. Panels A and B demonstrate simultaneous, bi-atrial activation maps created with a novel mapping system (Abbott). A: Left atrial rotor with counterclockwise activation (red to blue) with passive activation of the right atrium. B: Two focal sources present simultaneously, a counterclockwise right atrial rotor as well as a focal left atrial source showing centrifugal activation. C: Dispersion mapping using a PENTARAY™ (Biosense Webster) with both spatial and temporal dispersion indicating sites of interest. D: Epicardial phase mapping during AF generated by a noninvasive body mapping system (ECVUE, CardioInsight Technologies). Rotational activation is demonstrated in the posterior right upper atrium. Sources: Narayan et al.;102 Seitz et al.105 Reproduced with permission from Elsevier. Haissaguerre et al.94 Reproduced with permission from Wolters Kluwer Health.
ablation strategies (targeting fibrosis and channels based on atrial voltage mapping) have shown some efficacy but in a randomised clinical trial did not add benefit compared to traditional step-wise ablation strategies.76–78 In lieu of specific targets, quantifying fibrosis may guide ablation by further refining classification of AF beyond the current paradigm (paroxysmal, persistent, long-standing persistent).79 The degree of remodelling and fibrosis does not necessarily correlate directly with AF subtype,63,76 which may explain in part why some patients with paroxysmal AF require more extensive ablation for clinical success, while others with non-paroxysmal AF have success with PVI alone.78,80,81
Mapping Atrial Electrical Activity AF can be triggered or sustained by focal drivers that cluster in specific regions of the left and right atrium.82 These regions are more prevalent in patients with non-paroxysmal than paroxysmal AF likely due to advanced remodelling associated with greater AF burden. Early strategies aimed at ablating these critical regions were based on analysis of local bipolar electrograms assessed on a point-by-point basis during AF. The various electrogram morphologies were thought to represent evidence of electrophysiological phenomenon associated with AF drivers such as high-frequency discharges, shortened refractory periods, micro-re-entry, or local conduction block.10,83 Such approaches include complex fractionated electrograms (CFAE),dominant frequency analysis, high Shannon entropy, or sites with activity spanning large portions of the AF cycle length.84–86 These approaches have been the subject of multiple clinical trials with equivocal results regarding their net clinical benefit, partially due to the subjective nature of identifying such sites and the low-specificity of these sites in locating critical drivers.87
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CFAE ablation has been one of the more commonly performed mapping approaches, with various mapping systems incorporating automated detection algorithms.83 Specific criteria for CFAEs have been proposed but most often operators will assess points manually, reducing the reproducibility of these techniques. CFAE-based ablation strategies in addition to PVI in patients with persistent AF were the subject of the multicentre Substrate and Trigger Ablation for Reduction of Atrial Fibrillation II (STAR AF II) trial.88 In this multicentre, randomised controlled trial, 589 patients were assigned to undergo PVI alone, or PVI with the addition of linear lesions, or the addition of left atrial CFAE ablation. The addition of CFAE ablation was associated with longer procedural and fluoroscopic times without improved long-term freedom from AF (18 month freedom from AF 59% versus 49%; p=0.15 for between-group differences). However, in another study CFAE ablation as a component of a stepwise ablation strategy aimed at restoration of sinus rhythm via ablation improved outcomes in patients with persistent and long-standing persistent AF.89
Non-invasive Mapping of AF As opposed to point-by-point electrogram analysis, newer techniques can provide real-time mapping of electrical activity simultaneously across the atrium (Figure 2). These wide-field mapping techniques allows for direct visualisation of driver activity and assessment of their temporal stability. These techniques have provided key insights into the mechanism of AF and have corroborated with some observations made using optical and in silico mapping techniques.90–92
urce: h th.
Electrophysiology and Ablation Body surface potentials generated from atrial electrical activity can be used to create real-time epicardial activation maps (Figure 2D). This approach was first used to non-invasively map AF by Guillem and colleagues using phase maps generated by 56 electrodes placed on the chest and back.92 Additional techniques using 256-electrode vests integrated with CT imaging have been developed to provide high resolution (6 mm) 3D electroanatomical mapping. 93 Similar electrocardiographic imaging (ECGI) systems are available commercially and have been integrating into AF ablation strategies. Haissaguerre et al. used an ECGI system in 103 patients with persistent AF undergoing a driver-based ablation strategy and compared this with a historical control group who had undergone a step-wise ablation approach.94 Patients underwent ablation starting in regions with the highest density of drivers with additional linear lesions made if AF failed to terminate. There was a median of four driver regions per patient, and the number of drivers increased with AF burden. As compared with the control group, patients undergoing the targeted ablation approach had similar clinical results (1 year freedom from AF 85% versus 87% in control group; p=non-significant) with less radiofrequency energy delivery (35±21 versus 65±33 minutes; p<0.0001). Similar results from multicentre studies using non-contact mapping of persistent AF have been reported95 with favourable long-term outcomes (77% 1-year freedom from AF). There are several limitations to non-invasive mapping that may limit its clinical applicability. Electrical activity generated by the atrium is generally low amplitude when measured on body surface electrodes, which may degrade signal quality. These systems reconstruct signals from epicardial structures only while there may be differential endocardial-epicardial activation during AF.96 Sites identified as focal drivers could simply represent a wavefront breakthrough site from a passive endocardial structure not critical to AF propagation. Importantly, far-field signal contamination remains as a major challenge. Finally, the inter-atrial septum and LAA, which may harbour sites critical for AF, are poorly visualised with this technique.93,97,98
Invasive Mapping of AF In a prior study, a multielectrode array catheter (EnSite™, Abbott) was used to reconstruct virtual unipolar electrograms from 64 non-contact electrodes to display voltage and activation maps on a 3D anatomical map during AF.99 Noncontact mapping (NCM) has been used to map AF activation patterns,99,100 although some studies did not find that rotors or focal sources were prevalent or necessary for AF propagation. NCM has been used successfully to identify conduction gaps in linear lesions sets, localise premature atrial contractures and target atrial tachycardias.101 However, the use of NCM to ablate AF sources has not been described. Focal impulse and rotor modulation (FIRM) mapping uses bi-atrial contact basket catheters along with a novel mapping system (Topera, Abbott; Figures 2A and 2B). This approach was the focus of the CONventional ablation for atrial fibrillation with or without Focal Impulse and Rotor Modulation (CONFIRM) trial, a single centre prospective randomised controlled trial of 92 patients undergoing ablation of AF (72% with persistent AF).102 All patients underwent FIRM mapping and were randomised in 1:2 fashion to undergo FIRM directed ablation followed by conventional ablation versus a conventional ablation approach alone. In this series, rotors and focal impulses were seen in 97% of patients, demonstrated temporal stability of at least 10 minutes and were more numerous in patients with persistent versus
paroxysmal AF. The FIRM-guided strategy resulted in higher rates of AF termination or slowing ablation and improved rates of single-procedure freedom from AF (82.4% versus 44.9% after median follow up of 273 days; p<0.001). Similar findings on the spatial and temporal stability of rotors and focal sources have been demonstrated in multicentre registries using the FIRM mapping approach.82 Regional atrial mapping with the use of multipole catheters (as opposed to point-by-point mapping or panoramic approaches) has been applied successfully to AF mapping and ablation (Figure 2C). Contact mapping avoids reliance on far-field signal interpretation inherent to non-invasive mapping while the use of multiple simultaneous points provides better analysis of the spatiotemporal components of electrogram surrogates of AF drivers, which may improve the specificity of ablation targets.103,104 Seitz et al. performed a prospective study of 105 patients with AF, including 80 with non-paroxysmal AF, who underwent AF ablation guided by spatiotemporal dispersion observed with the use of a 20-electrode multispline catheter (PentaRay ®, Biosense Webster).105 These patients were compared with a historical cohort who underwent a conventional ablation approach (PVI followed by stepwise approach for patient with persistent AF). Patients who underwent the dispersion-guided ablation approach had lower rates of AF/AT recurrence after single or multiple procedures (45% versus 64%; log-rank p=0.026 and 15% versus 41%; log-rank p<0.001, respectively). In separate experiments the authors used optical mapping and numerical simulations to recreate their clinical findings of increased dispersion near the vicinity of active drivers. However, a limitation of this approach is that in the absence of a panoramic map of both atria it can be difficult to identify the primary drivers and true activation patterns. These combined experimental and clinical reports suggest that regional contact mapping may be useful tool for mapping and ablating non-paroxysmal AF. However, prospective studies are needed for further validation. Invasive mapping of AF triggers can be performed with the use of traditional multipolar catheters strategically placed to maximise diagnostic yield.106 Catheters can be simultaneously placed in regions commonly harbouring arrhythmogenic triggers such as the superior vena cava, cristae terminals, LAA and coronary sinus. AF can then be induced with high-dose isoproterenol infusion and sites of triggers can be targeted for ablation. These triggers may be more prevalent in patients with long-standing persistent AF compared to those with persistent AF and paroxysmal AF.107 Trigger mapping has been shown in retrospective series to be a useful strategy in patients with prior failed ablations and long-standing persistent AF.108,109
Challenges and Future Direction Real-time mapping to study AF mechanisms and/or guide ablation of non-paroxysmal AF has been implemented in various forms with promising, albeit mixed, results. Such discrepancies are likely because of the heterogeneous nature of non-paroxysmal AF as well as the relative merits and limitations of the mapping and ablation approaches. Acute termination of AF during targeted ablation is often demonstrated supporting the mechanistic validity of these approaches. However, it is unclear if AF termination is a clinically meaningful procedural endpoint.10 Focal drivers may cluster in regions (such as the antrum of the pulmonary veins or posterior wall) often targeted in conventional ablation approaches, which may further limit the incremental benefit of additional ablation. Overall, superiority to anatomic or stepwise ablation approaches has not been convincingly demonstrated. For
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Non-paroxysmal AF example, in the multicentre experience reported by Knecht et al. on the use of non-contact guided mapping of AF, radiofrequency ablation time and long-term clinical success were similar to traditional ablation approaches for persistent AF.95 Multicentre reports and meta-analyses on the use of FIRM guided ablation have not consistently shown therapeutic benefit compared to traditional ablation strategies.110–113
triggers, or tailored ablation of specific targets based on real-time dynamic mapping of AF mechanisms incorporating advanced imaging/ mapping systems remain to be determined and will largely depend on further advances in these technologies.114,115
Clinical Perspective Conclusion Imaging and mapping technology continue to evolve providing a better understanding of anatomy, arrhythmic substrate and patterns of AF activation. These tools have been successfully implemented into ablation planning and execution at some centres. Future advances in imaging/mapping fidelity and automation could improve ease of use and facilitate real-world implementation. The outcomes and clinical utility of ablation of predetermined targets based on anatomical landmarks with/without additional ablation of
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ARRHYTHMIA & ELECTROPHYSIOLOGY REVIEW
• Advanced mapping and imaging tools are critical components of investigation and clinical management of patients with nonparoxysmal AF. • Used together, these tools may help facilitate tailored ablation approaches although the prospective use of these techniques compared with existing strategies must be further investigated. • Improvements in mapping and imaging fidelity, automation and reproducibility would help increase the widespread adoption of these techniques.
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Electrophysiology and Ablation
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Lee G, McLellan AJA, Hunter RJ, et al. Panoramic characterization of endocardial left atrial activation during human persistent AF: Insights from non-contact mapping. Int J Cardiol 2017;228:406–11. https://doi.org/10.1016/ j.ijcard.2016.11.085; PMID: 27870970. 100. Yamabe H, Kanazawa H, Ito M, et al. Prevalence and mechanism of rotor activation identified during atrial fibrillation by noncontact mapping: Lack of evidence for a role in the maintenance of atrial fibrillation. Heart Rhythm 2016;13:2323–30. https://doi.org/10.1016/j.hrthm.2016.07.030; PMID: 27484715. 101. Kumagai K, Nakashima H. Noncontact mapping-guided catheter ablation of atrial fibrillation. Circ J 2009;73:233–41. https://doi.org/10.1253/circj.CJ-08-0700; PMID: 19060418. 102. Narayan SM, Krummen DE, Shivkumar K, et al. Treatment of atrial fibrillation by the ablation of localized sources: CONFIRM (Conventional Ablation for Atrial Fibrillation With or Without Focal Impulse and Rotor Modulation) trial. J Am Coll Cardiol 2012;60:628–36. https://doi.org/10.1016/ j.jacc.2012.05.022; PMID: 22818076. 103. Rostock T, Rotter M, Sanders P, et al. High-density activation mapping of fractionated electrograms in the atria of patients with paroxysmal atrial fibrillation. Heart Rhythm 2006;3:27–34. https://doi.org/10.1016/j.hrthm.2005.09.019; PMID: 16399048. 104. Narayan SM, Wright M, Derval N, et al. Classifying fractionated electrograms in human atrial fibrillation using monophasic action potentials and activation mapping: Evidence for localized drivers, rate acceleration, and nonlocal signal etiologies. Heart Rhythm 2011;8:244–53. https://doi. org/10.1016/j.hrthm.2010.10.020; PMID: 20955820. 105. Seitz J, Bars C, Théodore G, et al. AF ablation guided by spatiotemporal electrogram dispersion without pulmonary vein isolation. J Am Coll Cardiol 2017;69:303–21. https://doi. org/10.1016/j.jacc.2016.10.065; PMID: 28104073. 106. Della Rocca DG, Mohanty S, Trivedi C, et al. Percutaneous treatment of non-paroxysmal atrial fibrillation: A paradigm shift from pulmonary vein to non-pulmonary vein trigger ablation? Arrhythmia Electrophysiol Rev 2018;7:256–60. https://doi. org/10.15420/aer.2018.56.2; PMID: 30588313. 107. Hung Y, Lo L-W, Lin Y-J, et al. Characteristics and long-term catheter ablation outcome in long-standing persistent atrial fibrillation patients with non-pulmonary vein triggers. Int J Cardiol 2017;241:205–11. https://doi.org/10.1016/ j.ijcard.2017.04.050; PMID: 28456483.
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Non-paroxysmal AF 108. Lin D, Frankel DS, Zado ES, et al. Pulmonary vein antral isolation and nonpulmonary vein trigger ablation without additional substrate modification for treating longstanding persistent atrial fibrillation. J Cardiovasc Electrophysiol 2012;23:806–13. https://doi.org/10.1111/j.15408167.2012.02307.x; PMID: 22509772. 109. Mohanty S, Trivedi C, Gianni C, et al. Procedural findings and ablation outcome in patients with atrial fibrillation referred after two or more failed catheter ablations. J Cardiovasc Electrophysiol 2017;28:1379–86. https://doi.org/10.1111/ jce.13329; PMID: 28841251. 110. Buch E, Share M, Tung R, et al. Long-term clinical outcomes of focal impulse and rotor modulation for treatment of
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atrial fibrillation: A multicenter experience. Heart Rhythm 2016;13:636–41. https://doi.org/10.1016/j.hrthm.2015. 10.031; PMID: 26498260. 111. Mohanty S, Mohanty P, Trivedi C, et al. Long-term outcome of pulmonary vein isolation with and without focal impulse and rotor modulation mapping. Circ Arrhythm Electrophysiol 2018;11:e005789. https://doi.org/10.1161/CIRCEP.117.005789; PMID: 29545360. 112. Ramirez FD, Birnie DH, Nair GM, et al. Efficacy and safety of driver-guided catheter ablation for atrial fibrillation: A systematic review and meta-analysis. J Cardiovasc Electrophysiol 2017;28:1371–8. https://doi.org/10.1111/jce.13313; PMID: 28800192.
113. Baykaner T, Rogers AJ, Meckler GL, et al. Clinical implications of ablation of drivers for atrial fibrillation: a systematic review and meta-analysis. Circ Arrhythm Electrophysiol 2018;11:e006119. https://doi.org/10.1161/CIRCEP.117. 006119; PMID: 29743170. 114. Romero J, Michaud GF, Avendano R, et al. Benefit of left atrial appendage electrical isolation for persistent and longstanding persistent atrial fibrillation: a systematic review and meta-analysis. Europace 2018;20:1268–78. https://doi. org/10.1093/europace/eux372; PMID: 29342299. 115. Vein of Marshall ethanol infusion for persistent atrial fibrillation. Available at: https://clinicaltrials.gov/ct2/show/ NCT01898221 (accessed 29 April 2019).
Electrophysiology and Ablation
Understanding AF Mechanisms Through Computational Modelling and Simulations Konstantinos N Aronis, 1,2 Rheeda L Ali, 1 Jialiu A Liang, 1 Shijie Zhou 1 and Natalia A Trayanova 1 1. Department of Biomedical Engineering and the Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, US; 2. Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD, US
Abstract AF is a progressive disease of the atria, involving complex mechanisms related to its initiation, maintenance and progression. Computational modelling provides a framework for integration of experimental and clinical findings, and has emerged as an essential part of mechanistic research in AF. The authors summarise recent advancements in development of multi-scale AF models and focus on the mechanistic links between alternations in atrial structure and electrophysiology with AF. Key AF mechanisms that have been explored using atrial modelling are pulmonary vein ectopy; atrial fibrosis and fibrosis distribution; atrial wall thickness heterogeneity; atrial adipose tissue infiltration; development of repolarisation alternans; cardiac ion channel mutations; and atrial stretch with mechano-electrical feedback. They review modelling approaches that capture variability at the cohort level and provide cohort-specific mechanistic insights. The authors conclude with a summary of future perspectives, as envisioned for the contributions of atrial modelling in the mechanistic understanding of AF.
Keywords AF mechanisms, computational modelling, arrhythmia simulations, precision medicine, personalised electrophysiology Disclosure: NAT received funding support from National Institutes of Health (DP1-HL123271, U01-HL141074) and Leducq (16CVD02). KNA received National Institutes of Health award 5T32HL007227-42. RA received a fellowship from Johns Hopkins University. All other authors have no conflicts of interest to declare. Received: 8 March 2019 Accepted: 17 June 2019 Citation: Arrhythmia & Electrophysiology Review 2019;8(3):210â€“9. DOI: https://doi.org/10.15420/aer.2019.28.2 Correspondence: Natalia A Trayanova, Johns Hopkins University, 3400 N. Charles Street, Baltimore MD 21218, US. E: email@example.com Open Access: This work is open access under the CC-BY-NC 4.0 License which allows users to copy, redistribute and make derivative works for non-commercial purposes, provided the original work is cited correctly.
The pathophysiology of AF is complex and incompletely understood to date.1,2 AF is a progressive disease of the atria involving a multitude of mechanisms related to its initiation, maintenance and progression. Experimental evidence suggest that AF is characterised by alternations in atrial size, shape electrophysiology, autonomic innervation, and cardiomyocyte metabolism, as well as development of atrial fibrosis.1 However, there are several challenges in translating these experimental findings into actionable treatment strategies applicable in clinical practice.3 The interplay between experimentally observed mechanisms on a cohort-specific or patient-specific basis, and their contribution in development and progression of AF is yet to be elucidated. Our incomplete understanding of AF mechanisms is reflected in the modest efficacy of current therapeutic approaches, particularly in patients with persistent AF, with recurrence rates of up to ~50%, despite advances in mapping and ablation technology.4â€“8 Computational modelling and simulations are essential tools in physical sciences and engineering, and over the last decades have been increasingly utilised in cardiac electrophysiology in the study of complex arrhythmias, such as AF.9 Multi-scale models of cardiac electrophysiology provide a framework for integrating experimental and clinical findings, and linking micro-scale phenomena to whole-organ emergent behaviours. Computational modelling is
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now an essential part of mechanistic research in AF, because it can complement experimental observations and suggest novel mechanisms. Furthermore, whole-atria simulations are currently used in designing novel, individualised therapeutic strategies, contributing to the ongoing efforts towards precision medicine in cardiology.10,11 In this article, we focus on recent advancements in applications of atrial modelling in elucidating AF mechanisms. We summarise studies that use atrial modelling to investigate AF mechanisms that have taken place since our last review on the subject in 2014.12 Recent advances in the use of atrial modelling in AF therapeutics and ablation planning are summarised in a separate contemporary review by our group.10 Specifically, we summarise advancements in development of multi-scale AF models and then focus on the mechanistic links between alternations in atrial structure and electrophysiology with AF through the lens of computational modelling. We highlight how AF modelling complements experimental data, in ways that would not be possible outside the framework of simulations, as well as how AF models have revealed novel AF mechanisms. We also review modelling approaches that capture cohort-level variability and provide cohort-specific mechanistic insights. We conclude the review with a summary of the future perspectives for the contributions of atrial modelling in the mechanistic understanding of AF, towards the goal of understanding patient-specific AF mechanisms that would allow for personalised treatment.
ÂŠ RADCLIFFE CARDIOLOGY 2019
AF Mechanisms Through Computational Modelling and Simulations
Multi-scale atrial models are mathematical models that link electrophysiological phenomena at the cell, tissue and whole atria scale (Figure 1). The cell scale includes the equations that describe the kinetics of different ionic channels and regulatory proteins, that are coupled to produce the transmembrane potential of an atrial myocyte. The tissue scale includes cell-to-cell coupling and fibre orientation that govern electrical propagation. Electrical propagation is most commonly modelled using the mono-domain formulation coupled with cell models, while the bi-domain formulation is less frequently used due to its high computational cost. Last, the whole atria scale includes the entire complexity of atrial 3D anatomy and distribution of fibrosis. In this section, first we focus on the current state and recent advances in the cell-scale representation of atrial electrophysiology in patients with AF. Next, we describe advances in tissue-scale and wholeatrial scale representation of atrial myocardium (atrial geometry, ultrastructure and fibrosis); the description is brief, as this topic has been extensively covered in a separate review by our group.10 Last, we summarise advances in development of atrial models that incorporate mechano-electrical feedback.
Cell-scale Representation of Atrial Electrophysiology in AF Biophysically Detailed Models of Atrial Cellular Electrophysiology Biophysically detailed models of cellular electrophysiology typically follow the Hodgkin–Huxley model and represent current flow through ion channels, pumps, and exchangers, as well as sub-cellular calcium cycling. Markov models of ion channels are increasingly used to describe channel gating and modulation; and are important in modelling of the electrophysiological effect of medications and ion channel mutations.13–16 The most commonly used human atrial cell models are those developed by Courtemanche-Ramirez-Nattel, Nygren et al., Koivumaki et al., Maleckar et al., Grandi et al. and Coleman et al.17–22 The details of these and other atrial cell models have been recently reviewed.12,23 These cell models have been used in studies that investigate AF mechanisms using tissue and whole-atria simulations.24–38 Whole atria simulations using biophysically detailed cell models are typically computationally expensive, requiring execution on high-performance computer clusters.
Models Including the Ultra-rapid Outward K+ Current The ultra-rapid outward K+ current (IKur) is a major repolarising current in human atria and accounts for the relatively short action potential duration (APD) of the atria.39 Until recently, available atrial cellular electrophysiology models did not account for experimentally-observed IKur inactivation dynamics. Aguilar et al. incorporated an experimentally derived formulation of IKur in the biophysically detailed ionic model of Courtemanche et al.40 This formulation accurately reproduces time, voltage- and frequency-dependent inactivation of the channel.40 This ionic model has been used in tissue-level simulations to gain mechanistic insights in the role of IKur in the presence or absence of AF-induced ionic remodelling. The model of Aguilar et al. has not been used in organ-level simulations yet. The significance of accurate modelling of IKur is that inhibition of IKur has been considered as an ideal anti-arrhythmic drug development strategy, due to the selective atrial localisation of IKur. Computational modelling has been used to evaluate the effect of different IKur inhibitors in terminating AF, understand the effect of AF-induced ionic remodelling on the efficacy of IKur inhibition and define optimal IKur inhibitors properties, such as
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Figure 1: Multi-scale Model Generation Cell scale (μm) Atrial cardiomyocyte membrane kinetics INa
Tissue scale (mm) Fibre orientation
Organ scale (cm) MRI
Ito IKr IKs IK1 INaK Action potential traces Stimulus
Cell 1 Normal Fibrotic Simulation 400 ms
Cell 2 Normal Fibrotic
Recent Advances in Atrial Modelling
Cell 1 100 ms
Cell 2 0 ms
Atrial models are constructed in three different spatial scales: cell-level scale, tissue-level scale and organ-level scale. Cell-level atrial electrophysiology: schematic of the membrane channels and an example of the atrial action potentials obtained in fibrotic (green) and normal cells (black). Tissue scale: myocyte coupling and atrial tissue architecture create a preferential direction for wave propagation along the atrial fibres. Organ scale electrophysiology: lategadolinium enhanced MRI can be used to segment normal tissue from fibrotic region and for the 3D reconstruction of the atrial geometry to simulate atrial activation.
kinetics and state-dependent binding, that maximise AF selectivity in human atrial cardiomyocytes.14,40,41 The efficacy of IKur inhibition in patients with paroxysmal AF has recently been evaluated in two clinical trials. DIAGRAF-IKUR demonstrated that IKur inhibitor S66913 failed to demonstrate efficacy in patients with paroxysmal AF.42 The second trial evaluates BMS-919373 and has recently been completed, but the results are not available yet (NCT02156076).
Phenomenological Atrial Models Phenomenological cell models do not provide a biophysically accurate description of cellular scale electrophysiology, but constitute simplified formulations that reproduce an accurate action potential shape and important electrophysiological properties, such as APD restitution, conduction velocity restitution and wave curvature. The two phenomenological models that have been used in tissue- or organlevel atrial simulations are the atrial Bueno-Orovio–Cherry–Fenton model and the Mitchel-Schaeffer model. The Bueno-Orovio–Cherry–Fenton model is a phenomenological cell model that describes ventricular electrophysiology, using four state variables.43 The atrial Bueno-Orovio–Cherry–Fenton model has been adapted to capture atrial electrophysiology and in 2D tissuescale simulations it accurately replicates the characteristic features of re-entrant excitation patterns as they are observed in similar simulations using biophysically detailed models.44 The modified Mitchel-Schaeffer model has two state variables and four parameters, and has been adapted to be robust to any pacemaker behaviour.45 The simplicity of the Mitchel-Schaeffer model allows for ultra-fast computation. Phenomenological models, since they are considerably less complex than biophysically detailed models, require significantly reduced run time and are computationally more efficient. Given their reduced complexity, phenomenological models are suitable for calibration to clinical measurements, such as activation sequences (as described in the following section), less prone to overfitting and easier to validate compared to biophysically detailed models.46–49 However, since phenomenological models do not incorporate specific
Electrophysiology and Ablation ionic channels, they may be less suitable for studies evaluating the complex effect of antiarrhythmic medications.
Development of Patient-specific Action Potential Models of Atrial Myocardium Development of patient-specific atrial action potential models is an active area of research, since differences in cellular scale electrophysiology can dramatically affect the emergent atrial fibrillation dynamics in tissue- or organ-scale simulations.47–49 This was highlighted in a recent study by Lombardo et al.47 In this study, action potential morphology, APD restitution and conduction velocity restitution were assessed in five patients with AF during invasive electrophysiological studies, using monophasic action potential catheters and standard multi-electrode electrophysiology catheters. These parameters were used to calibrate a phenomenological (Fenton-Karma) and a biophysically detailed (Koivumäki) atrial ionic model with a stochastic optimisation approach. Parameters of the calibrated models were significantly different from published sets and between patients. Both biophysically detailed and phenomenological models produced spiral waves on 2D simulations that had similar dynamics for each patient, but largely varied between patients.47 These results underscore the need for development of patient-specific atrial action potential models. The Mitchel-Schaeffer model has been used for development of patient-specific atrial action potential models in tissue- and organscale studies, as it has a very small number of parameters, allowing for computationally tractable calibration.46,48,49 Calibration of a tissuescale formulation of the Mitchel-Schaeffer model using synthetic data and patient-derived atrial effective refractory periods and conduction velocity restitution was done in five patients with AF.48,49 Calibrated models to different patients yielded different spiral wave dynamics in simple 2D simulations.48,49 Atrial effective refractory period, local activation time and conduction velocity measurements from multiple locations in the left atrium, have been used to personalise the action potential parameters and tissue conductivity of a 2D implementation of the Mitchel-Schaeffer model on a realistic 3D atrial surface in seven patients with paroxysmal AF.46 These models were derived during pacing from the coronary sinus and then validated, by accurately predicting the activation sequence of the left atrium during pacing from the high right atrium.46 Future studies should focus on developing optimisation approaches that are suitable for the unique nature of cardiac electrophysiology models. Cardiac electrophysiology models involve highly non-linear partial differential equations, with high-dimensional, frequently discontinuous parameter spaces and complex geometry. Recent research has suggested that hybrid optimisation approaches that combine stochastic and deterministic processes at each iteration are superior to purely stochastic or deterministic approaches.50 AF models with personalised electrophysiology should be validated by predicting the location of re-entrant drivers that sustain AF and comparing it with clinical observations. It remains unclear how close we can come to electrophysiological personalisation of atrial models, and what level of personalisation is relevant for accurate mechanistical assessment and clinical predictions in patients with AF.
Modelling of Atrial Geometry, Ultrastructure and Fibrosis A detailed review of the state-of-the-art methods for modelling atrial geometry, ultrastructure (fibre orientation) and fibrosis has
recently been published by our team.10 In brief, atrial models can have idealised or realistic geometry. Models with idealised atrial geometry abstract the left atrium (LA) as an ellipsoid surface (or volume) with orifices that correspond to the four pulmonary veins and the mitral valve (i.e. topologically equivalent to the LA).51,52 Models with realistic atrial geometry are reconstructed from medical imaging and specifically from cardiac MRI, cardiac CT scans or invasively acquired electroanatomic maps.31,35–37,46,53–70 The reconstructed models can be 3D surface models (manifolds), 3D bilayer models or full-thickness volumetric 3D models.31,35–37,46,53–60,62–70 Myofibre orientation is incorporated in 3D atrial models using fibre orientation atlases derived from histology, rule-based methods or methods that use morphological data of the endo- and epicardial surfaces and the local solutions of Laplace’s equations.65,71,72 Fibre orientation is critical for accurate organ-scale simulations.73 There are ongoing efforts to develop fibre orientation atlases derived from diffusion-tensor imaging cardiac MRI and incorporate them in atrial models.74 Novel approaches that derive fibre orientation from electroanatomic mapping or local electrograms are at different stages of development.75,76 Atrial fibrosis can be detected on late gadolinium enhancement MRI (LGE-MRI) as areas of increased gadolinium uptake using different thresholding techniques.31,35,77,78 However, the precision in imaging of AF with currently available LGE-MRI technologies remains controversial. Areas of fibrosis can be then represented in atrial models as electrical conduction disturbances (lower conductivity, edge splitting, or percolation), transforming growth factor-beta1 ionic channel effects, myocyte-fibroblast coupling, discrete microstructural alternations in gap junction connectivity, and combinations of the preceding.25,33,35,79–81 Selection of fibrosis modelling methodology is critical as the specific representation of fibrosis has a significant effect on rotor dynamics.62,81 There is an urgent need for quantification of the uncertainty related to imaging, fibrosis detection and fibrosis representation, and incorporation of this uncertainty in model predictions.82,83
Modelling of Mechano-electrical Feedback Atrial mechano-electric feedback refers to alterations in cell- or tissue-scale electrophysiological properties as a result of changes in the loading conditions of the atria. Atrial stretch has been associated with alternations in myocardial electrophysiology in experimental studies, but its role in AF pathophysiology remains unknown.84,85 Incorporation of mechano-electric feedback in atrial models, although methodologically challenging, is necessary to understand the contribution of atrial mechanics and atrial stretch in the pathophysiology of AF. There are limited studies incorporating mechano-electrical feedback in atrial models, since simulations using strong electromechanical coupling are extremely computationally demanding. Strong electromechanical coupling refers to the modelling approach where changes in electrophysiological state variables result in atrial tissue deformation, and atrial tissue deformation alters the electrophysiological parameters that determine different ionic currents and the action potential. The first 2D atrial model that incorporated mechano-electrical feedback was developed by Brocklehurst et al. by strongly coupling the electrophysiological model of Colman et al. to the mechanical myofilament model of Rice et al., with parameters modified based on experimental data.86–88 A stretch-activated channel was incorporated into the model to simulate the mechano-electrical feedback. Satriano et al. developed a 3D implementation of a strongly coupled electromechanical atrial
ARRHYTHMIA & ELECTROPHYSIOLOGY REVIEW
AF Mechanisms Through Computational Modelling and Simulations Figure 2: Mechanistic Insights from AF Modelling
Pulmonary vein ectopy
Atrial APD alternans
PV Gap junction
Adipose tissue deposition
Fibrosis and fibrosis distribution 400 ms
0 ms Normal
100 200 Time (ms)
Action potential duration restitution
Loss of function K+ channel mutation
100 ms Shortened APD
100 ms Prolonged APD
Ion channel mutation Gain of function + K channel mutation
Delayed rectifier potassium current (IKur)
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Clockwise: Pulmonary vein ectopy and AF inducibility, gap junctional uncoupling, development of repolarisation alternans, atrial stretch with mechano-electrical feedback, characterisation of the fibrotic atrial substrate, tissue restitution properties, ion channel mutations, atrial wall thickness heterogeneity and adipose tissue deposition (Source: Mahabadi et al. 2017.120 Reproduced with permission from the Public Library of Science). APD = action potential duration; PV = pulmonary vein.
model using reconstructed images from a porcine heart and ex vivo experimental validation.89
Mechanistic Insights into AF Initiation and Maintenance Using Computational Modelling Atrial computational modelling allows integration of experimental and clinical findings, and provides insights in the fundamental mechanisms involved in initiation and perpetuation of AF. Increased pulmonary vein ectopy is the primary mechanism of paroxysmal AF initiation.90 Maintenance of persistent AF occurs due to electroanatomical remodelling of the atria. Re-entrant drivers within regions of structural or functional inhomogeneities have a significant role in maintenance of persistent AF.91 Structural and electrical remodelling have been incorporated in atrial models to investigate potential links between the altered electroanatomical substrate in AF, and the dynamics of AF re-entrant drivers. Key structural and functional alternations that are mechanistically linked to AF and have been studied using atrial modelling are pulmonary vein (PV) ectopy, presence of atrial fibrosis and its distribution, atrial wall thickness heterogeneity, atrial adipose tissue infiltration, development of repolarisation alternans, cardiac ion channel mutations, and atrial stretch with mechano-electrical feedback (Figure 2). Although atrial autonomic innervation and remodelling have a significant role in AF maintenance, current atrial modelling studies have not incorporated the distribution or remodelling of autonomic nerve fibres, primarily due to the limited ability to visualise these structures with clinically available imaging technologies.92,93
ARRHYTHMIA & ELECTROPHYSIOLOGY REVIEW
Pulmonary Vein Ectopy as an AF Trigger and the Role of Pulmonary Veins in AF Maintenance Increased PV ectopy is the primary mechanism of arrhythmia initiation in paroxysmal AF.90 Cellular mechanisms that have been proposed for the generation of PV ectopy include increased automaticity and afterdepolarisations of the cardiomyocytes in the PV sleeves.94 There are limited modelling studies evaluating PV ectopy, as previously reviewed.12 Briefly, PV ectopy has been modelled as increased automaticity of PV atrial cells, through the incorporation of a hyperpolarisation-activated inward current to human atrial cell models, and micro-reentry within the PV sleeves. Recently, Roney et al. demonstrated that the electrophysiological properties and the extent of fibrosis of the PVs are associated with patient-specific susceptibility to AF initiation and maintenance.31 Short PV APD and slow conduction velocity at the LA/PV junction was associated with increased arrhythmia susceptibility, while longer PV APD was protective. The presence of PV fibrosis was associated with increased incidence of re-entrant drivers in the PVs region.31 Future studies should evaluate how a biophysically and structurally detailed model of ectopy in the PV sleeves would drive the atria into paroxysmal AF.
Role of Fibrosis in AF Dynamics There is conflicting clinical evidence on the role of fibrosis in re-entrant drivers dynamics in patients with AF. In two studies (n=12â€“41), re-entrant drivers observed during AF, localised in the boundary zones between fibrotic and non-fibrotic atrial myocardium.58,95 In three studies, there
Electrophysiology and Ablation was no association between re-entrant driver localisation and LGE-MRI fibrosis.96–98 Both invasive and non-invasive methods for rotor detection were used in these studies.58,95–98 The discrepancy between the results of these studies could be due to differences in patient cohorts, LGEMRI acquisition, image processing, and fibrosis definition, and electrical signal processing and rotor detection strategies. In the setting of this clinical equipoise, atrial modelling provides unique insights in the role of fibrosis in AF dynamics. Atrial modelling studies strongly support that the extent and distribution of atrial fibrosis are critical determinants of AF initiation, maintenance, and re-entrant driver dynamics during AF. In a sensitivity analysis of simulations using realistic atrial geometry, the extent and distribution of fibrosis had a greater impact on re-entrant driver localisation over alternations in tissue wavelength.31 Although diffuse fibrosis is sufficient for initiation of AF in simulations,patient-specific fibrosis distribution determines re-entrant driver dynamics.25,32–35 In two separate studies using patient-specific atrial geometry and fibrosis distribution derived from LGE-MRI, the re-entrant drivers that occurred during AF localised in the boundary zones between fibrotic and nonfibrotic atrial myocardium.33,34 These zones had a highly specific fibrosis spatial pattern, characterised by high fibrosis density and entropy, and corresponded to atrial areas with high degree of intermingling between ﬁbrotic and non-ﬁbrotic atrial myocardium.33
and patients with AF (n=2) the effect of wall thickness heterogeneity on re-entrant drivers trajectories was more prominent in the right atrium (RA), while in the LA, re-entrant driver trajectory was primarily determined by fibrosis distribution. In the RA, re-entrant drivers drifted toward and anchored to the large wall thickness gradient between the crista terminalis and the surrounding atrial wall. In the absence of such a gradient, re-entrant drivers drifted toward the superior vena cava or the tricuspid valve. In the presence of fibrosis, re-entrant drivers anchored to either the fibrotic region or between the crista terminalis and the fibrotic region, depending on the location in the RA from where they were elicited. The more uniform wall thickness of LA resulted in LA re-entrant drivers drifting towards the PVs in the absence of fibrosis, or anchoring in the fibrotic region in the presence of fibrosis.37 A limitation of these studies is that fibre orientation was not included in the reconstructed atrial models. These findings highlight the complex interplay between atrial geometry, wall thickness gradients and fibrosis distribution that ultimately determine the dynamics of AF re-entrant drivers.
Adipose Tissue and its Effect on AF Dynamics There is emerging evidence that AF-induced remodelling is characterised by increased deposition of epicardial adipose tissue and adipose tissue infiltration in the atrial myocardium. The presence of adipose tissue in or around the atrial myocardium has a paracrine pro-fibrotic effect.102 Only one study to date uses atrial modelling
The role of fibrosis in AF dynamics has also been studied using a probabilistic approach with cellular automata models.99 Cellular automata are simple models where a set of rules dictates the transition of each cell between a resting, excited or refractory state. Fibrotic atrial tissue has been incorporated in cellular automata models as decreased probability of a cell to be connected with its transverse neighbours, reflecting the lateralisation of connexin-43 that is present in fibrotic atrial tissue.99 Progressive fibrosis significantly changed the frequency, duration, burden and dynamics of AF episodes. Similar to the simulations using biophysically detailed atrial models described above, micro-reentrant wavefronts in cellular automata models anchored at regions with critical fibrotic architectural patterns. The number of local critical patterns of fibrosis rather than the extent of global fibrosis determined AF dynamics.
to gain insight in the potential effects of fibro-fatty infiltration on AF dynamics.26 In 2D simulations, the Courtemanche cell model was modified to represent atrial electrophysiology similar to what is experimentally observed when myocytes are co-cultured with adipocytes (69–87% increase in APD and 2.5–5.5A mV increase in resting membrane potential). The presence of adipose tissue-induced remodelling substantially affected spiral wave dynamics resulting in complex arrhythmias and wave breakup. Future studies are needed to elucidate the electrophysiological properties of adipocytes, and the electrophysiological alternations of atrial myocytes induced by the presence of adipose tissue. Incorporation of these findings in atrial models will allow us to understand how the presence of adipose tissue in or around the myocardium affect the propensity for and dynamics of AF.
The association between patient-specific fibrosis distribution and re-entrant driver dynamics has been experimentally validated using a single ex vivo atrial preparation from a patient with longstanding persistent AF.55 In this study, a detailed 3D atrial model was reconstructed from both LGE-MRI and histology sections. Simulations using this model demonstrated that AF re-entrant drivers localise in areas with distinct structural features, specifically intermediate wall thickness and fibrosis as well as twisted myofibre orientation. Future studies, however, are needed to ascertain the association between re-entrant driver dynamics and fibrosis, as well as the contribution of re-entrant drivers to AF pathophysiology as it remains controversial.100,101
Evaluating the Role of Atrial Stretch in AF Dynamics by Modelling Mechano-electrical Feedback
Role of Wall Thickness Heterogeneity in AF Re-entrant Driver Dynamics Atrial wall thickness heterogeneity is a structural property of the atria that has a significant impact on AF re-entrant drivers’ trajectory and localisation.36,37 In simulations using models with both idealised and realistic atrial geometry, re-entrant drivers drift from thicker to thinner regions along ridge-line structures.36,37 In simulations using bi-atrial models reconstructed from MRIs of healthy volunteers (n=4)
The role of atrial stretch in the onset and maintenance of AF is incompletely understood to date. Acute atrial stress is associated with conduction slowing and complex signal formation in the PV-LA junction in humans and prolongation of atrial refractory period in animal studies.84,85 There is accumulating evidence that most voltage-sensitive ion channels that give rise to the cardiac action potential can be mechanically modulated, and that medications can affect the mechanosensitivity of ion channels (i.e. ranolazine inhibits mechanosensitivity of NaV1.5).103,104 Computational modelling has been used to integrate the cell-level electrophysiological alternations induced by atrial stretching with the distribution of stretch on the atria, and its time evolution over the cardiac cycle, incorporating mechano-electric feedback in atrial models as described in above. In 2D simulations using the electromechanical model of Brocklehurst et al., the presence of mechano-electrical feedback significantly affected spiral wave tip trajectories, stability, excitation frequencies and meandering range (Figure 3).86 Contrary to this, in whole organ 3D simulations using the Satriano et al. model, the role of stretch-
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AF Mechanisms Through Computational Modelling and Simulations activated channels was very small during a single-beat of sinus rhythm.87 Simulations using the model of Satriano et al., however, demonstrated that higher strain occurs in areas adjacent to the mitral valve annulus, rim of the appendage, pulmonary vein trunks and Bachmann’s bundle.89 These are regions where atrial arrhythmias are most likely to occur. Further studies are needed to assess the potential impact of mechano-electrical feedback on AF dynamics in whole heart simulations.
Figure 3: Electro-mechanical Dynamics of Spiral Waves –2 –1.5 –1 –0.5 0 0.5 1 1.5 2 ×104 2.5 x (μm) 2 1.5 1 0.5 0 –0.5
Mechanisms of Atrial Repolarisation Alternans and their Role in AF Dynamics Atrial repolarisation alternans is the beat-to-beat alternation in APD that is observed in atrial myocytes when they are excited at fast rates. The onset of atrial repolarisation alternans has been associated with increased risk for development of AF in animal models and limited human data.105–107 Simulations using biophysically detailed atrial models have provided a unique insight in the sub-cellular mechanisms of atrial repolarisation alternans.27 Furthermore, multiscale models with realistic atrial geometry comprehensively describe how the cell-scale phenomenon of repolarisation alternans results in the organ-scale behaviour of AF.38 In simulations, decreased ryanodine receptor inactivation has a central role in development of repolarisation alternans. Specifically, decreased ryanodine receptor inactivation results in augmentation of Ca++ alternans, ultimately manifesting as repolarisation alternans.27 These results are consistent with experimental findings demonstrating that for the same sarcoplasmic reticulum Ca++ load, repolarisation alternans can occur due to changes of ryanodine receptor refractoriness.108,109 In 3D simulations with realistic atrial geometry, elevated propensity to calcium-driven repolarisation alternans due to chronic AF electrical remodelling was associated with increased vulnerability to ectopy-induced arrhythmia. The presence of Ca++-induced electrical instabilities promoted disorganisation of AF through increased repolarisation heterogeneities, resulting in unstable scroll waves meandering and breaking in multiple wavelets.38
Mechanisms of AF in the Presence of Ion Channel Mutations Rare cases of AF are associated with mutations in genes that encode critical cardiac ionic channels.110–115 The sparsity of clinical data on these cases renders computational modelling a critical methodology for studying the pathophysiology of AF in the presence of genetic mutations. Mutations related to AF studied in a computational modelling framework are those in genes encoding for K+ channels and specifically hERG gene encodes for IKr channels and is associated with short-QT syndrome 1 (representative mutations L532P and N588K), KCNQ1 gene encodes for IKs channels and is associated with short-QT syndrome and familial/juvenile AF syndrome (representative mutations G229D, V307L and V141M), and KCNJ2 gene encodes for IKr1 which is an inward-rectifier K+ current, associated with short-QT syndrome 3 (representative mutations D172N and E299V). The effect of these mutations on cardiac dynamics has been evaluated in tissue-scale simulations,24 and 3D atrial modelling studies incorporating realistic atrial geometry.28–30 Gain-of-function mutations in hERG, KCNQ1 and KCNJ2 genes result in shorter action potential duration. Atrial models incorporating gainof-function mutations in these genes had shorter APD and refractory period compared to wild-type models.24,28–30 These models exhibited greater inducibility of spiral wave re-entry in tissue level (Figure 4) and organ-level simulations, reduced tissue excitation wavelength, which caused greater inducibility of spiral wave re-entry in organ-level
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–1 –1.5 –2 –2.5
t = 2,200 ms
t = 2,250.4 ms
t = 2,400 ms
t = 2450 ms
t = 2,300 ms
t = 2,350.4 ms
×104 20 0 V (mV) –20 –40 –60 –80
t = 2,500 ms
t = 2,550 ms
Snapshots of tissue deformation and membrane action potential conduction in the full electro-mechanical model during a spiral wave excitation in a 2D electro-mechanical coupling model of human atrial tissue (x, y: μm). Source: Brocklehurst et al. 2017.86 Reproduced from the Public Library of Science.
Figure 4: Trajectories of the Rotor Centre A
Stimulation width (mm) 20
Stimulation time (ms) +6 +10
L532P –1:1 0 C
2 3 4 Time (s) D
4 cm L532P–1:1
Trajectories of the rotor centre in 2D tissue patch for different stimulation widths (A) and times (B) in the modified Courtemanche–Ramirez–Nattel (control) model and the same model adapted to capture the electrophysiological effects of two representative hERG mutations that are associated with AF (L532P and N588K). The control model failed to initiate a spiral wave by using the S1–S2 cross-field protocol. The models adapted to L532P initiated a rotor for all investigated setups except a stimulus width of 20 mm (A). In contrast, the model adapted to N588K failed to initiate a rotor for stimuli <40 mm or later than 2 ms (B). Source: Loewe et al. 2014.24 Reproduced with permission from Oxford University Press.
simulations, and increased lifespan of re-entrant drivers.24,28–30 The presence of the KCNQ1 gain of function mutation G229D was associated with increased propensity for spiral wave breakup.28
Electrophysiology and Ablation Figure 5: Simulated Normal Sinus Rhythm and Chronic AF nSR
Time (ms) 600
500 PCL (ms)
All models Calibrated
Populations of ionic models calibrated to recordings from patients with AF have been used in 3D simulations of idealised51 and realistic118 atrial geometry models to provide novel insights in the mechanisms involved in AF maintenance. Higher expression of INa and ICaL was associated with perpetuation of AF.51 ICaL inhibition resulted in increased re-entrant drivers meandering and ultimately re-entrant driver collision and termination of AF, particularly in models with decreased INa.51 Prolongation of APD in all phases of repolarisation caused slowing and regularisation of fibrillatory dynamics. Specifically, inhibition of IK1, INaK and INa resulted in organisation of AF.118 The same inhibition of ionic currents was able to produce different effects on AF dynamics in atrial simulations using cells modelled to have the same variability as human experimental data.51,118 The next frontiers in development of cohort-specific model populations are to develop model populations calibrated to different AF sub-types according to AF burden (i.e. paroxysmal, persistent and long-standing persistent AF) and use them in simulations to gain subtype-specific mechanistic insights, and to incorporate biomarkers that further refine model selection by describing more specific disease phenotypes.
Simulated normal sinus rhythm (nSR) and chronic AF (cAF) populations: steady-state action potential (AP, upper) and calcium transient amplitude (CaT, middle) traces and a few representative dynamic restitution curves (lower). AP and CaT traces and restitution curves of the nSR baseline are also shown for comparison. The x-axis in restitution curves represents pacing cycle length in ms. The populations (1,000 models each) were functionally calibrated, resulting in 213 models in the nSR population and 357 in the cAF population. AP = action potential; APD = action potential duration; cAF = chronic AF; CaT = calcium transient amplitude; PCL = pacing cycle length. Source: Vagos et al. 2017.117 Reproduced with permission from AIP Publishing.
Population of Models to Capture Cohort-specific Variability Population of cell models calibrated to cohort-specific distributions of action potential properties are a powerful tool to capture the intersubject, cohort-specific variability of atrial electrophysiology. In a study applying this approach, an initial population of >2,000 biophysically detailed atrial cell models were generated, in a way that each model had a unique set of ionic conductance combinations, stochastically selected over a wide range around their values in the original model.116 Distributions of different properties of action potential morphology were estimated from experimental recordings in patients with AF. A subset of the initial population of models was then selected, such as the selected models had an action potential morphology that lies within the experimentally-derived action potential distribution (Figure 5).116,117 Analysis of populations of models calibrated to recordings from atrial preparations of patients with AF (n=149) and sinus rhythm (n=214) demonstrated that models calibrated to AF have variations in IK1, IKur, and Ito conductances consistent with AF-induced remodelling.116 Populations of models calibrated to action potential recordings from atria in sinus rhythm and AF identify potential ionic determinants of inter-subject variability in human APD and action potential morphology.116 Populations
of models calibrated to patients with AF have smaller APD variability and more stable dynamic restitution compared to populations of models calibrated patients in sinus rhythm.117
Computational modelling of AF has emerged as a critical part of the scientific effort to better understand the complexity and variability in AF pathophysiology. Atrial models are becoming more sophisticated and capture fine details of atrial anatomy, ultrastructure, and fibrosis distribution. Personalisation of atrial models is slowly extending beyond geometrical, image-based model personalisation to functional and electrophysiological personalisation that can be cohort-specific or patient-specific. Simulations using atrial models have provided important insight in the mechanisms underlying AF, highlighting the importance of the atrial geometry, fibrotic substrate and altered atrial electrophysiology in initiation and maintenance of AF. There are some limitations in the mechanistic insights that one can gain using currently available atrial models. There is a need for more accurate PV ectopy models. Future studies should focus on development of accurate models of PV electrophysiology, structure and fibrosis distribution, that can be used to investigate how patientspecific predisposition to PV ectopy, in conjunction with patientspecific substrate, result in the onset and maintenance of AF. Computationally efficient models that incorporate mechano-electrical feedback need to be developed, to understand the impact of different loading conditions on the electrophysiological heterogeneities of the atrial and how these heterogeneities affect AF initiation and dynamics. Models with mechano-electrical feedback can also be used to evaluate the haemodynamic effect of AF and the potential benefit of rhythm control. Current atrial models capture patient-specific atrial anatomy and fibrosis distribution, but there are only limited studies incorporating patient-specific atrial electrophysiology. Future modelling approaches should focus on developing models that capture patient-specific atrial electrophysiology. This could be accomplished by calibration of currently available models using patientspecific electrophysiological measurements, or utilisation of genomic and/or transcriptomic data. Addition of genomic and/or transcriptomic
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AF Mechanisms Through Computational Modelling and Simulations data to atrial models could allow for electrical personalisation by incorporating the predicted impact that different polymorphisms have on ionic channels and currents. There is still incomplete understanding of the electrophysiological substrate present in different stages of AF progression. Characterisation of atrial electrophysiological properties, such as APD, APD restitution, conduction velocity, conduction velocity anisotropy and restitution at different stages of AF progression could allow computational models to more accurately describe AF dynamics, and provide insights in the mechanisms involved in progression of AF. Currently available models do not include a patient-specific representation of fibre orientation. Technological progress that will allow for derivation of patient-specific fibre orientation from imaging or electrophysiological measurements is an area of current investigation, with the potential to facilitate incorporation of patient-specific fibre orientation in atrial models. Currently available models do not include information about autonomic innervation of the heart. Future models should incorporate both the global effects of autonomic tone on atrial electrophysiology, as well as the local effects of atrial ganglionic innervation and remodelling. The considerable uncertainty in atrial models arises both from natural variation in experimental and clinical data (aleatory uncertainty), and lack of knowledge (epistemic uncertainty). The impact of uncertainty on the outputs of atrial models is not well understood.83 There are limited studies on verification, validation and uncertainty quantification of atrial models.52,53 Future studies should prioritise uncertainty quantification and incorporation of it in atrial models and specifically determine how uncertainties in the cell-scale atrial models contribute to uncertainties in emergent behaviours of organ-level models, and how these uncertainties can be visualised and interpreted by experimentalists or clinicians. Statistical approaches such as Monte Carlo techniques, polynomial chaos expansions and Gaussian process emulation can be used to incorporate uncertainty in atrial models. A
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more detailed discussion of these techniques is provided in the review by Mirams et al.83 The mechanistic insights in AF provided by computational modelling and simulations will continue to grow in a virtuous cycle with experimental and clinical cardiac electrophysiology findings. The next frontier for atrial multi-scale modelling is to expand beyond the “wholeorgan” scale to the “whole-patient” scale and incorporate mechanistic links for all clinical factors related to onset and progression of AF. Machine learning approaches have the potential to be combined with computational modelling to raise computational modelling to the ‘whole-patient’ scale.119 Development of a patient-specific understanding of the mechanisms for AF initiation and progression is the most essential step towards precision medicine and development of personalised AF prevention or therapeutic strategies.
Clinical Perspective • Atrial models can be used to gain mechanistic insights into AF. • A complex interplay between atrial geometry, fibrosis distribution and wall thickness heterogeneity determines re-entrant driver localisation during AF. • While there is conflicting clinical evidence on re-entrant driver localisation with respect to atrial fibrosis, in simulations, re-entrant drivers coalesced in areas at the border between fibrotic and healthy myocardium. • Adipose tissue deposition in the left atrium promotes wave breakup and complex arrhythmia formation. • Development of atrial repolarisation alternans promotes repolarisation heterogeneity that results in arrhythmia instabilities and wave breakup. • Atrial modelling can be used to predict the effect of different ion channel mutations on AF initiation and maintenance.
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Comparing reentrant drivers predicted by image-based computational modeling and mapped by electrocardiographic imaging in persistent atrial fibrillation. Front Physiol 2018;9:414. https://doi. org/10.3389/fphys.2018.00414; PMID: 29725307. 59. Phung TN, Moyer CB, Norton PT, et al. Effect of ablation pattern on mechanical function in the atrium. Pacing Clin Electrophysiol 2017;40:648–54. https://doi.org/10.1111/ pace.13086; PMID: 28370137. 60. Zahid S, Whyte KN, Schwarz EL, et al. Feasibility of using patient-specific models and the “minimum cut” algorithm to predict optimal ablation targets for left atrial flutter. Heart Rhythm 2016;13:1687–98. https://doi.org/10.1016/j. hrthm.2016.04.009; PMID: 27108938. 61. Roney CH, Bayer JD, Cochet H, et al. Variability in pulmonary vein electrophysiology and fibrosis determines arrhythmia susceptibility and dynamics. PLoS Comput Biol 2018;14:e1006166. https://doi.org/10.1371/journal. pcbi.1006166; PMID: 29795549. 62. Roney CH, Bayer JD, Zahid S, et al. Modelling methodology of atrial fibrosis affects rotor dynamics and electrograms. Europace 2016;18:iv146–5. https://doi.org/10.1093/europace/ euw365; PMID: 28011842. 63. Roney CH, Cantwell CD, Bayer JD, et al. Spatial resolution requirements for accurate identification of drivers of atrial fibrillation. Circ Arrhythm Electrophysiol 2017;10:e004899. https://doi.org/10.1161/CIRCEP.116.004899; PMID: 28500175. 64. Shim J, Hwang M, Song JS, et al. Virtual in-silico modeling guided catheter ablation predicts effective linear ablation lesion set for longstanding persistent atrial fibrillation: multicenter prospective randomized study. Front Physiol 2017;8:792. https://doi.org/10.3389/fphys.2017.00792; PMID: 29075201. 65. Fastl TE, Tobon-Gomez C, Crozier A, et al. Personalized computational modeling of left atrial geometry and transmural myofiber architecture. Med Image Anal 2018;47:180–90. https://doi.org/10.1016/j.media.2018.04.001; PMID: 29753182. 66. Hwang M, Kwon SS, Wi J, et al. Virtual ablation for atrial fibrillation in personalized in-silico three-dimensional left atrial modeling: comparison with clinical catheter ablation. Prog Biophys Mol Biol 2014;116:40–7. https://doi.org/10.1016/j. pbiomolbio.2014.09.006; PMID: 25261813. 67. Labarthe S, Bayer J, Coudiere Y, et al. A bilayer model of human atria: mathematical background, construction, and assessment. Europace 2014;16 Suppl 4:iv21-iv29. https://doi. org/10.1093/europace/euu256; PMID: 25362166. 68. Bayer JD, Roney CH, Pashaei A, et al. Novel radiofrequency ablation strategies for terminating atrial fibrillation in the left atrium: a simulation study. Front Physiol 2016;7:108. https://doi. org/10.3389/fphys.2016.00108; PMID: 27148061. 69. Hwang M, Lim B, Song JS, et al. Ganglionated plexi stimulation induces pulmonary vein triggers and promotes atrial arrhythmogenecity: In silico modeling study. PLoS One 2017;12:e0172931. https://doi.org/10.1371/journal. pone.0172931; PMID: 28245283. 70. Lim B, Hwang M, Song JS, et al. Effectiveness of atrial fibrillation rotor ablation is dependent on conduction velocity: An in-silico 3-dimensional modeling study. PLoS One 2017;12:e0190398. https://doi.org/10.1371/journal. pone.0190398; PMID: 29287119. 71. Krueger MW, Schmidt V, Tobón C, et al. Modeling atrial fiber orientation in patient-specific geometries: a semi-automatic rule-based approach. 2011:223–32. https://doi.org/10.1371/ journal.pone.0190398; PMID: 29287119. 72. Labarthe S, Coudiere Y, Henry J, Cochet H. A semi-automatic method to construct atrial fibre structures: A tool for atrial simulations. 2012 Computing in Cardiology 2012;881–4. 73. Krueger MW, Schmidt V, Tobón C, et al. Modeling atrial fiber
orientation in patient-specific geometries: a semi-automatic rule-based approach Functional Imaging and Modeling of the Heart. Berlin, Heidelberg: Springer; 2011: 223–32. Pashakhanloo F, Herzka DA, Ashikaga H, et al. Myofiber architecture of the human atria as revealed by submillimeter diffusion tensor imaging. Circ Arrhythm Electrophysiol 2016;9:e004133. https://doi.org/10.1161/CIRCEP.116.004133; PMID: 27071829. Roney CH, Whitaker J, Sim I, et al. A technique for measuring anisotropy in atrial conduction to estimate conduction velocity and atrial fibre direction. Comput Biol Med 2019;104:278–90. https://doi.org/10.1016/j.compbiomed. 2018.10.019; PMID: 30415767. Muzikant AL, Hsu EW, Wolf PD, Henriquez CS. Region specific modeling of cardiac muscle: comparison of simulated and experimental potentials. Ann Biomed Eng 2002;30:867–83. PMID: 12398418. Oakes RS, Badger TJ, Kholmovski EG, et al. Detection and quantification of left atrial structural remodeling with delayedenhancement magnetic resonance imaging in patients with atrial fibrillation. Circulation 2009;119:1758–67. https://doi. org/10.1161/CIRCULATIONAHA.108.811877; PMID: 19307477. Khurram IM, Beinart R, Zipunnikov V, et al. Magnetic resonance image intensity ratio, a normalized measure to enable interpatient comparability of left atrial fibrosis. Heart Rhythm 2014;11:85–92. https://doi.org/10.1016/j. hrthm.2013.10.007; PMID: 24096166. Boyle PM, Zahid S, Trayanova NA. Towards personalized computational modelling of the fibrotic substrate for atrial arrhythmia. Europace 2016;18:iv136–145. https://doi. org/10.1093/europace/euw358; 28011841. MacCannell KA, Bazzazi H, Chilton L, et al. A mathematical model of electrotonic interactions between ventricular myocytes and fibroblasts. Biophys J 2007;92:4121–32. https:// doi.org/10.1529/biophysj.106.101410; PMID: 17307821. Gokhale TA, Medvescek E, Henriquez CS. Modeling dynamics in diseased cardiac tissue: Impact of model choice. Chaos 2017;27:093909. https://doi.org/10.1063/1.4999605; PMID: 28964161. Gray RA, Pathmanathan P. Patient-specific cardiovascular computational modeling: diversity of personalization and challenges. J Cardiovasc Transl Res 2018;11:80–8. https://doi. org/10.1007/s12265-018-9792-2; PMID: 29512059. Mirams GR, Pathmanathan P, Gray RA, et al. Uncertainty and variability in computational and mathematical models of cardiac physiology. J Physiol 2016;594:6833–47. https://doi. org/10.1113/JP271671; PMID: 26990229. Walters TE, Lee G, Spence S, et al. Acute atrial stretch results in conduction slowing and complex signals at the pulmonary vein to left atrial junction: insights into the mechanism of pulmonary vein arrhythmogenesis. Circ Arrhythm Electrophysiol 2014;7:1189–97. https://doi.org/10.1161/CIRCEP.114.001894; PMID: 25516579. Satoh T, Zipes DP. Unequal atrial stretch in dogs increases dispersion of refractoriness conducive to developing atrial fibrillation. J Cardiovasc Electrophysiol 1996;7:833–42. https://doi. org/10.1111/j.1540-8167.1996.tb00596.x; PMID: 8884512. Brocklehurst P, Ni H, Zhang H, Ye J. Electro-mechanical dynamics of spiral waves in a discrete 2D model of human atrial tissue. PLoS One 2017;12:e0176607. https://doi. org/10.1371/journal.pone.0176607; PMID: 28510575. Colman MA, Aslanidi OV, Kharche S, et al. Pro-arrhythmogenic effects of atrial fibrillation-induced electrical remodelling: insights from the three-dimensional virtual human atria. J Physiol 2013;591:4249–72. https://doi.org/10.1113/ jphysiol.2013.254987; PMID: 23732649. Rice JJ, Wang F, Bers DM, de Tombe PP. Approximate model of cooperative activation and crossbridge cycling in cardiac muscle using ordinary differential equations. Biophys J 2008;95:2368–90. https://doi.org/10.1529/ biophysj.107.119487; PMID: 18234826. Satriano A, Vigmond EJ, Schwartzman DS, Di Martino ES. Mechano-electric finite element model of the left atrium. Comput Biol Med 2018;96:24–31. https://doi.org/10.1016/ j.compbiomed.2018.02.010; PMID: 29529527. Haissaguerre M, Jais P, Shah DC, et al. Spontaneous initiation of atrial fibrillation by ectopic beats originating in the pulmonary veins. N Engl J Med 1998;339:659–66. https://doi. org/10.1056/NEJM199809033391003; PMID: 9725923. Cochet H, Dubois R, Yamashita S, et al. Relationship between fibrosis detected on late gadolinium-enhanced cardiac magnetic resonance and re-entrant activity assessed with electrocardiographic imaging in human persistent atrial fibrillation. JACC Clin Electrophysiol 2018;4:17–29. https://doi. org/10.1016/j.jacep.2017.07.019; PMID: 29479568. Linz D, Elliott AD, Hohl M, et al. Role of autonomic nervous system in atrial fibrillation. Int J Cardiol 2018. https://doi. org/10.1016/j.ijcard.2018.11.091; PMID: 30497894. Kim MY, Sikkel MB, Hunter RJ, et al. A novel approach to mapping the atrial ganglionated plexus network by generating a distribution probability atlas. J Cardiovasc Electrophysiol 2018;29:1624–34. https://doi.org/10.1111/jce.13723; PMID: 30168232. de Bakker JM, Ho SY, Hocini M. Basic and clinical electrophysiology of pulmonary vein ectopy. Cardiovasc Res. 2002;54:287–94. https://doi.org/10.1016/s00086363(01)00532-6; PMID: 12062334. Haissaguerre M, Shah AJ, Cochet H, et al. Intermittent drivers anchoring to structural heterogeneities as a major pathophysiological mechanism of human persistent atrial
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AF Mechanisms Through Computational Modelling and Simulations fibrillation. J Physiol 2016;594:2387–98. https://doi.org/10.1113/ JP270617; PMID: 26890861. 96. Chrispin J, Gucuk Ipek E, Zahid S, et al. Lack of regional association between atrial late gadolinium enhancement on cardiac magnetic resonance and atrial fibrillation rotors. Heart Rhythm 2016;13:654–60. https://doi.org/10.1016/j.hrthm. 2015.11.011; PMID: 26569460. 97. Schade A, Nentwich K, Costello-Boerrigter LC, et al. Spatial relationship of focal impulses, rotors and low voltage zones in patients with persistent atrial fibrillation. J Cardiovasc Electrophysiol 2016;27:507–14. https://doi.org/10.1111/ jce.12913; PMID: 26732468. 98. Sohns C, Lemes C, Metzner A, et al. First-in-man analysis of the relationship between electrical rotors from noninvasive panoramic mapping and atrial fibrosis from magnetic resonance imaging in patients with persistent atrial fibrillation. Circ Arrhythm Electrophysiol 2017;10:e004419. https://doi.org/10.1161/CIRCEP.116.004419; PMID: 28790104. 99. Manani KA, Christensen K, Peters NS. Myocardial architecture and patient variability in clinical patterns of atrial fibrillation. Phys Rev E 2016;94:042401. https://doi.org/10.1103/ PhysRevE.94.042401; PMID: 27841583. 100. Child N, Clayton RH, Roney CR, et al. Unraveling the underlying arrhythmia mechanism in persistent atrial fibrillation: results from the STARLIGHT study. Circ Arrhythm Electrophysiol 2018;11:e005897. https://doi.org/10.1161/ CIRCEP.117.005897; PMID: 29858382. 101. de Groot NM, Houben RP, Smeets JL, et al. Electropathological substrate of longstanding persistent atrial fibrillation in patients with structural heart disease: epicardial breakthrough. Circulation 2010;122:1674–82. https://doi. org/10.1161/CIRCULATIONAHA.109.910901; PMID: 20937979. 102. Abe I, Teshima Y, Kondo H, et al. Association of fibrotic remodeling and cytokines/chemokines content in epicardial adipose tissue with atrial myocardial fibrosis in patients with atrial fibrillation. Heart Rhythm 2018;15:1717–27. https://doi. org/10.1016/j.hrthm.2018.06.025; PMID: 29908372. 103. Peyronnet R, Nerbonne JM, Kohl P. Cardiac mechano-gated ion channels and arrhythmias. Circ Res 2016;118:311–29. https://doi.org/10.1161/CIRCRESAHA.115.305043; PMID: 26838316. 104. Beyder A, Strege PR, Reyes S, et al. Ranolazine decreases
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mechanosensitivity of the voltage-gated sodium ion channel Na(v)1.5: a novel mechanism of drug action. Circulation 2012;125:2698–706. https://doi.org/10.1161/ CIRCULATIONAHA.112.094714; PMID: 22565935. 105. Fuller H, Justo F, Nearing BD, et al. Eleclazine, a new selective cardiac late sodium current inhibitor, confers concurrent protection against autonomically induced atrial premature beats, repolarization alternans and heterogeneity, and atrial fibrillation in an intact porcine model. Heart Rhythm 2016;13:1679–86. https://doi.org/10.1016/j.hrthm.2016.04.015; PMID: 27108587. 106. Verrier RL, Fuller H, Justo F, et al. Unmasking atrial repolarization to assess alternans, spatiotemporal heterogeneity, and susceptibility to atrial fibrillation. Heart Rhythm 2016;13:953–61. https://doi.org/10.1016/ j.hrthm.2015.11.019; PMID: 26592850. 107. Narayan SM, Franz MR, Clopton P, et al. Repolarization alternans reveals vulnerability to human atrial fibrillation. Circulation 2011;123:2922–30. https://doi.org/10.1161/ CIRCULATIONAHA.110.977827; PMID: 21646498. 108. Shkryl VM, Maxwell JT, Domeier TL, Blatter LA. Refractoriness of sarcoplasmic reticulum Ca2+ release determines Ca2+ alternans in atrial myocytes. Am J Physiol Heart Circ Physiol 2012;302:H2310–20. https://doi.org/10.1152/ ajpheart.00079.2012; PMID: 22467301. 109. Wang L, Myles RC, De Jesus NM, et al. Optical mapping of sarcoplasmic reticulum Ca2+ in the intact heart: ryanodine receptor refractoriness during alternans and fibrillation. Circ Res 2014;114:1410–21. https://doi.org/10.1161/ CIRCRESAHA.114.302505; PMID: 24568740. 110. Andreasen L, Nielsen JB, Christophersen IE, et al. Genetic modifier of the QTc interval associated with early-onset atrial fibrillation. Can J Cardiol 2013;29:1234–40. https://doi. org/10.1016/j.cjca.2013.06.009; PMID: 24074973. 111. Sinner MF, Pfeufer A, Akyol M, et al. The non-synonymous coding IKr-channel variant KCNH2-K897T is associated with atrial fibrillation: results from a systematic candidate gene-based analysis of KCNH2 (HERG). Eur Heart J 2008;29:907–14. https://doi.org/10.1093/eurheartj/ehm619; PMID: 18222980. 112. Hayashi K, Konno T, Tada H, et al. Functional characterization of rare variants implicated in susceptibility to lone atrial
fibrillation. Circ Arrhythm Electrophysiol 2015;8:1095–104. https:// doi.org/10.1161/CIRCEP.114.002519; PMID: 26129877. 113. Sarquella-Brugada G, Campuzano O, Iglesias A, et al. Short QT and atrial fibrillation: A KCNQ1 mutation-specific disease. Late follow-up in three unrelated children. HeartRhythm Case Rep 2015;1:193–7. https://doi.org/10.1016/j.hrcr.2015.02.005; PMID: 28491547. 114. Steffensen AB, Refsgaard L, Andersen MN, et al. IKs Gainand loss-of-function in early-onset lone atrial fibrillation. J Cardiovasc Electrophysiol 2015;26:715–23. https://doi. org/10.1111/jce.12666; PMID: 25786344. 115. Deo M, Ruan Y, Pandit SV, et al. KCNJ2 mutation in short QT syndrome 3 results in atrial fibrillation and ventricular proarrhythmia. Proc Natl Acad Sci USA 2013; 110:4291–6. https://doi.org/10.1073/pnas.1218154110; PMID: 23440193. 116. Sanchez C, Bueno-Orovio A, Wettwer E, et al. Inter-subject variability in human atrial action potential in sinus rhythm versus chronic atrial fibrillation. PLoS One 2014;9:e105897. https://doi.org/10.1371/journal.pone.0105897; PMID: 25157495. 117. Vagos MR, Arevalo H, de Oliveira BL, et al. A computational framework for testing arrhythmia marker sensitivities to model parameters in functionally calibrated populations of atrial cells. Chaos 2017;27:093941. https://doi. org/10.1063/1.4999476; PMID: 28964122. 118. Sanchez C, Bueno-Orovio A, Pueyo E, Rodriguez B. Atrial fibrillation dynamics and ionic block effects in six heterogeneous human 3D virtual atria with distinct repolarization dynamics. Front Bioeng Biotechnol 2017;5:29. https://doi.org/10.3389/fbioe.2017.00029; PMID: 28534025. 119. Cantwell CD, Mohamied Y, Tzortzis KN, et al. Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling. Comput Biol Med 2019;104:339–51. https://doi.org/10.1016/j.compbiomed.2018.10.015; PMID: 30442428 120. Mahabadi AA, Balcer B, Dykun I, et al. Cardiac computed tomography-derived epicardial fat volume and attenuation independently distinguish patients with and without myocardial infarction. PLoS One 2017;12:e0183514. https://doi.org/10.1371/journal.pone.0183514; PMID: 28837682.
Drugs and Devices
Improving Cardiac Resynchronisation Therapy George Thomas, Jiwon Kim and Bruce B Lerman Department of Medicine, Division of Cardiology, Cornell University Medical Center, New York, US
Abstract CRT is a cornerstone of therapy for patients with heart failure and reduced ejection fraction. By restoring left ventricular (LV) electrical and mechanical synchrony, CRT can reduce mortality, improve LV function and reduce heart failure symptoms. Since its introduction, many advances have been made that have improved the delivery of and enhanced the response to CRT. Improving CRT outcomes begins with proper patient selection so CRT is delivered to all populations that could benefit from it, and limiting the implantation of CRT in those with a small chance of response. In addition, advancements in LV leads and delivery technologies coupled with multimodality imaging and electrical mapping have enabled operators to place coronary sinus leads in locations that will optimise electrical and mechanical synchrony. Finally, new pacing strategies using LV endocardial pacing or His bundle pacing have allowed for CRT delivery and improved response in patients with poor coronary sinus anatomy or lack of response to traditional CRT.
Keywords Heart failure, cardiac resynchronisation, biventricular pacemaker, biventricular defibrillator, His bundle pacing, leadless pacing Disclosure: GT reports Biotronik research grant and consulting. The other authors have no conflicts of interest to declare. Received: 1 November 2018 Accepted: 21 June 2019 Citation: Arrhythmia & Electrophysiology Review 2019;8(3):220â€“7. DOI: https://doi.org/10.15420/aer.2018.62.3 Correspondence: George Thomas, Division of Cardiology, Cornell University Medical Center, 525 East 68th Street, Starr 4, New York, NY 10021, US. E: firstname.lastname@example.org Open Access: This work is open access under the CC-BY-NC 4.0 License which allows users to copy, redistribute and make derivative works for non-commercial purposes, provided the original work is cited correctly.
CRT is an essential treatment for patients with heart failure and reduced ejection fraction as it can restore left ventricular (LV) electrical and mechanical synchrony. It has been shown to increase quality of life, improve functional status, reduce hospitalisation, improve LV systolic function and reduce mortality in properly selected patients.1,2 While CRT is an effective therapy, approximately 30% of patients treated with CRT do not benefit from it and some patients are negative responders. Improving outcomes with CRT begins with appropriate selection.
QRS Duration and Morphology Since CRT targets electrical dyssynchrony, QRS duration and morphology have been used to determine which patients will receive maximum benefit from CRT. Based on subgroup analysis of large CRT trials, current guidelines consider CRT implants to be a Class I indication in patients with left bundle branch block (LBBB) and QRS >150 msec, with softer recommendations for QRS <150 msec and non-LBBB patients.3 An analysis of data from the Multicenter Automatic Defibrillator Implantation Trial-Cardiac Resynchronization Therapy (MADIT-CRT) trial demonstrated that only patients with LBBB had a reduction in heart failure events and that non-LBBB patients may have been harmed by CRT.4 Similar data were published from the Resynchronization/ Defibrillation for Ambulatory heart Failure Trial (RAFT).5 No benefits were found to result from CRT in right bundle branch block (RBBB) patients in five randomised controlled clinical trials or in a subset of 1,233 patients with non-LBBB QRS morphology from four randomised trials.6,7 However, data on QRS morphology are mixed and no large CRT trial has used QRS morphology as an enrolment criterion. There is no standardised definition of LBBB, especially with respect to predicting
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electrical dyssynchrony and response to CRT. In addition, some patients with RBBB and intraventricular conduction delay may have a LBBB-like activation pattern of the left ventricle and could respond to CRT.8 To this end, the MADIT-CRT trial showed that, in patients with non-LBBB morphologies and PR intervals >230 msec, there was a 67% reduction in risk of the combined primary endpoint of heart failure and death and a 76% reduction in the risk of death in the CRT device (CRT-D) arm versus the ICD-only arm.9 Data from the REsynchronization reVErses Remodeling in Systolic left vEntricular dysfunction (REVERSE) trial also showed benefit in patients with RBBB receiving CRT.10 QRS duration is a good predictor of CRT response. QRS duration is a criterion for every large clinical trial showing benefit in CRT. Trials randomising patients with narrow QRS (<120 msec or 130 msec) show no benefit or potential harm for patients receiving CRT, even in the presence of mechanical dyssynchrony.11 An analysis of four trials by Cleland et al. showed that QRS duration was the best predicator of benefit from CRT placement, irrespective of QRS morphology, with response seen once QRS duration was >130 msec.12 Subgroup analyses have shown that the greatest CRT benefit is derived in the cohort of patients with a QRS duration of 150 msec, as reflected in the guidelines for CRT placement.3,10,12 Since correcting dyssynchrony is the core benefit of CRT, imaging has been added to 12-lead ECG to expand and refine the population of patients that might benefit from this therapy. The presence of mechanical dyssynchrony on echocardiography or MRI has been shown to predict which patients perform best after CRT placement.13,14 However, the Predictors of Response to CRT (PROSPECT) trial, which
ÂŠ RADCLIFFE CARDIOLOGY 2019
Cardiac Resynchronisation Therapy prospectively studied echocardiographic measures of dyssynchrony, found they had only modest sensitivity and specificity to predict CRT response with significant variability in the measurement of dyssychrony studied.15 It was conjectured that this was due to the complexity of the dyssynchrony parameters and significant interobserver variability. Newer measures of mechanical dyssynchrony, as assessed by echocardiography and MRI, have shown promise.13,14 Apical rocking and septal flash are simple visual echocardiographic parameters of a LBBB-like contraction pattern that is characterised by late contraction of the lateral left ventricle. Apical rocking specifically refers to a short septal motion of the apex due to early contraction of the septum and late contraction of the lateral wall.13 Septal flash is defined by early contraction of the septum causing a short rapid inward motion of the septum.13 The Relationship of Visually Assessed Apical Rocking and Septal Flash to Response and Long-term Survival Following CRT (PREDICT-CRT) trial assessed 1,060 patients for these parameters and found a 15% reduction in LV end-systolic volume in 77% of patients when both apical rocking and septal flash were present in contrast to 69% if only apical rocking was present and 56% in those with septal flash alone. These parameters were more predictive of echocardiographic response to CRT and long-term survival than QRS morphology, duration and other clinical variables.13 Shoal et al. used cardiac MRI to assess the usefulness of the U-shaped contraction pattern, defined by a line of block in mechanical contraction between the septum and lateral left ventricle, consistent with a true LBBB contraction pattern, to predict CRT outcome. Patients who had a U-shaped propagation had an 80% response rate versus a 26% response rate in the group with homogenous propagation group (p<0.001).16 Similarly, patients without dyssynchrony on MRI, based on a circumferential uniformity ratio estimate >0.70, had no clinical benefit with CRT and a 12-fold increased mortality rate.14 Invasive mapping studies have shown that QRS duration and morphology may not always be predictive of prolonged LV activation times that correlate with CRT response. Non-invasive ECG mapping techniques have been used to identify patients who may have abnormally late-activating regions of the left ventricle. In the Markers and Response to CRT (MARC) study of 240 patients who were followed prospectively, vectorcardiography-derived QRSarea was shown to be more predictive of echocardiographic response to CRT than QRS morphology and duration. The vectorcardiography was mathematically constructed from a standard digital 12-lead ECG and consists of three orthogonal leads X, Y, and Z that form a 3D vector loop.17 In addition, high-resolution non-invasive electrocardiographic imaging (ECGI) has shown promise in defining patients who are more likely to respond to CRT. Multiple studies have shown that ECGI measures of electrical dyssynchrony obtained using the CardioInsight™ system (Medtronic) better correlate with acute haemodynamic and long-term clinical response to CRT than the presence of LBBB.18 Another smaller ECGI system with a 53-electrode ECG belt (Heartscape Technologies) has also shown the ability to predict echocardiographic response to CRT better than QRS duration or morphology.19
Lead Placement Another strategy for improving CRT outcome is optimisation of lead placement. Since the lateral left ventricle is the latest-activating
ARRHYTHMIA & ELECTROPHYSIOLOGY REVIEW
area in LBBB, the lateral or posterolateral left ventricle – in general – is the preferred target for LV lead placement, but the optimal place for such placement may vary for a given patient. Early data showed that the placement of anterolateral or posterolateral leads was superior to anterior lead placement.20 However, analysis of the Comparison of Medical Therapy, Pacing and Defibrillation in Heart Failure (COMPANION) trial showed that no particular lead location was associated with an improved response.21 Other studies show that apical LV lead placement is associated with worse outcomes than non-apical leads.22 The Targeted Left Ventricular Lead Placement to Guide CRT (TARGET) and Speckle Tracking Assisted Resynchronization Therapy for Electrode Region (STARTER) studies used speckle tracking echocardiography to determine the area of latest mechanical activation for placement of the LV lead. Patients randomised to the targeted LV lead placement group had lower rates of the combined endpoint of heart failure and death.23,24 This approach is limited by the availability of specialised software and the need for optimal image quality. LV leads should also be targeted to areas of viable myocardium. CRT patients with scar tissue in the posterolateral left ventricle assessed by cardiac MRI have minimal response to CRT.25 Further data consistently show that LV leads placed in areas of viable myocardium – especially in viable segments with dyssynchrony – have high response rates to CRT versus LV leads placed in an area of scar and no dyssynchrony.14 Late-activating segments of the left ventricle can be targeted electrically. LV pacing from areas of late activation defined by either LV lead electrical delay (QLV is defined as the time between onset of the QRS on the surface ECG and the sensed signal on the LV lead) or interventricular delay (time of onset of large positive or negative peaks of the right ventricular to LV electrogram) have been shown to correlate with favourable acute haemodynamic and clinical responses.26,27 Even patients with a lead placed in the LV apex had a good response to CRT if the lead showed late LV activation.28 Leads placed in an electrically late-activating segment predicted by ECGI or vectorcardiography have also been shown to enhance acute response to CRT.28–30 However, preliminary results from the CRT Implant Strategy Using the Longest Electrical Delay for Non-left Bundle Branch Block Patients (ENHANCE–CRT) pilot study, which randomised 248 patients to LV lead placement guided by QLV versus standard LV lead placement in non-LBBB subjects, showed no statistical difference.31 Applicability of CRT therapy can also be limited by the anatomic constraints of the coronary sinus (CS). In 5–10% of patients, LV lead placement is unsuccessful due to either CS inaccessibility, high LV pacing thresholds, or phrenic nerve stimulation.1,2,6 In addition, >50% of patients have only one CS branch that is suitable for lead placement, making it difficult to target LV lead placement to areas of late activation or dyssynchrony in all subjects.32
New Catheter Approaches to LV Synchronisation Endocardial LV pacing offers many potential benefits over epicardial LV pacing. LV endocardial leads can be targeted to any area of the left ventricle due to the lack of anatomic constraints from the CS. In addition, LV endocardial pacing thresholds are lower than epicardial leads and phrenic nerve stimulation can be more readily avoided. Animal and human studies have shown that the LV endocardium provides a favourable acute haemodynamic response to LV
Drugs and Devices Figure 1: Decision Tree for CRT Non-responders
Labs and work-up for non-cardiac causes of congestive heart failure
Treat metabolic disorder or anaemia
Chest X-ray for lead position CRT non-responder
ECG with right ventricle only, left ventricle only and CRT on/off to assess percentage BIV pacing Device check to settings and assess BIV pacing %
Atrioventricular nodal ablation or increase rate control to achieve near-100% BIV pacing
Ablation or drug therapy for low BIV pacing from ventricular arrhythmias to achieve near-100% BIV pacing
Lead revision if poor lead position or ineffective LV pacing. Consider imaging to target dyssynchronous segments and avoid scar
Good lead position and CRT pacing morphology
Optimise atrioventricular and ventriculoventricular timing
• Echo-based optimisation • Device-based with IEGMs • ECG-based using QRS, ECGI or vectorcardiography • Non-invasive haemodynamic with continuous blood pressure monitoring (Finapres) or impedance cardiography, finger plethysmography or acoustic cardiography
Consider assessment of residual dyssynchrony via echo, MRI, vectorcardiography, ECGI or nuclear imaging or empirically consider advanced CRT lead options
• Turn on multipoint pacing from quadripolar lead • His bundle pacing • Second coronary sinus lead for dual-siteLV pacing • LV endocardial free wall lead via either transseptal arterial or ventricular approach, LV septal pacing or leadless approach
Consider turning off CRT if negative responder, especially if at baseline the patient had relatively narrow QRS, non-left bundle branch block, lacked dyssynchrony and/or had significant lateral wall scarring
BIV = biventricular; ECGI = non-invasive high-resolution electrocardiographic mapping; IEGM = intracardiac electrogram; LV = left ventricular.
pacing. This is likely to due to more rapid LV endocardial impulse conduction leading to shorter LV activation times as compared with epicardial pacing.33 The most common technique for achieving LV endocardial pacing involves transseptal access across the intra-atrial septum to deliver a LV lead across the mitral annulus into the left ventricle. The largest trial of LV endocardial pacing with transseptal atrial placement, the ALternate Site Cardiac ResYNChronization (ALSYNC) trial, enrolled 138 patients and had an 89.4% procedural success rate in patients who were CRT non-responders or who had failed implants. The clinical response rate was 59%. However, despite anticoagulation there was a high rate of thromboembolic complications (stroke rate was 2.6 per 100 patient years and there were 14 transient ischaemic attacks in nine patients), but no cases of lead-related mitral regurgitation were seen.34 Another technique for LV endocardial pacing involves passing the lead through the intraventricular septum and into the lateral LV myocardium. In a preliminary study, all patients were successfully implanted with this technique and eight out of nine were considered responders. All patients were on anticoagulation and no cerebrovascular accidents or transient ischaemic attacks were reported during a mean follow-up of 8.7 months.35 The larger Pilot Study of Interventricular Septal Puncture for Cardiac Resynchronization Therapy to Treat Heart Failure (LV-CONSEPT) trial (NCT01818765), designed to evaluate outcomes for this approach to LV lead placement, has completed enrolment. Preliminary data from LV endocardial pacing via the LV septum has also been shown to improve haemodynamics versus right ventricular pacing and to have similar haemodynamic response to lateral LV endocardial biventricular pacing. This technique utilises a specialised pacing lead with a fixed 4 mm helix. The helix has a thin coating on the proximal portion, with only the distal 1.27 mm electrode exposed. The lead is screwed into the intraventricular septum until LV septal
capture is confirmed. The advantage of this technique is that the lack of hardware in the left ventricle eliminates the need for anticoagulation.36 The final option for LV endocardial pacing is leadless pacing. The WiSE™ CRT System (EBR Systems) uses a leadless 9 mm pacing electrode directly implanted into the LV endocardium via retrograde or transseptal access. The electrode is powered by a generator implanted near the left ventricle in the intercostal space that delivers ultrasound to the electrode, which is then converted to electricity for pacing. The LV electrode will endothelialise, so long-term anticoagulation is not required. In the preliminary Safety and Performance of Electrodes Implanted in the Left Ventricle (SELECT-LV) study of 35 patients, there was a 97% success rate for implantation, with an 85% clinical response rate to CRT in patients that failed CRT implants or were non-responders. However, 23% of participants in this trial experienced significant adverse events.37 The larger Simulation Of the Left Ventricular Endocardium for CRT (SOLVE-CRT) trial (NCT02922036) will look at 350 patients who are non-responders or who have failed CRT implants to assess the utility of this system. Another option to improve CRT response is to pace the LV from two different sites. A study looking at 40 patients with permanent AF with a slow ventricular response randomised patients to conventional CRT versus CRT with two LV leads and showed higher LV ejection fraction (27% versus 35%) and smaller LV end-systolic volume (157 cm3 versus 134 cm3) with dual site LV pacing.38 A second study compared 34 patients who had received CRT with two LV leads to a propensity score-matched population that had received conventional CRT.39 The conventional CRT group had more frequent ventricular tachycardia and a higher all-cause mortality and heart transplant rate. Preliminary data from the 100-patient Triple-site versus Standard Cardiac Resynchronization Therapy (TRUST-CRT) study showed similar rates of adverse events and implantation success with triple-site versus standard CRT and an improved rate of clinical response, with only 12.5% of patients reporting New York Heart Association (NYHA)
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Cardiac Resynchronisation Therapy Class III symptoms versus 30% in the standard CRT group. However, triple-site CRT was associated with higher LV lead thresholds, lower pacing impedance and greater battery drain.40 No further data have been published, although the trial completed enrolment in 2015. The recently published the triple-site CRT(V3) study showed that in 84 CRT non-responders randomised to a second LV lead versus control, there was no improvement in clinical response to CRT in the treatment group at 24 months, but there was a high rate (20%) of procedural complications.41 Multiple randomised controlled trials of dual-site LV pacing have been completed but have not yet released their results. Multisite LV pacing can also be delivered via a quadripolar LV lead. Multiple point pacing (MPP) is an option in most commercially available CRT systems and allows for dual-site LV pacing by using two separate bipoles from a quadripolar lead. By covering a larger area of LV myocardium, MPP can increase the speed of impulse propagation and reduce LV activation times. MPP has shown improvement over single-site LV pacing as assessed by pressure volume loops, LV dP/dtmax, global peak LV radial strain and LV outflow tract velocity time integral in selected patients.42 In the MultiPoint Pacing IDE (MPP-IDE) study, MPP delivered from anatomically-separate bipoles resulted in an 87% clinical response rate and 100% response rate in patients who were non-responders at 3 months. MPP may be a good option to use in patients that do not initially respond to standard CRT therapy.43 Figure 1 outlines a strategy for improving the clinical response of CRT non-responders. His bundle pacing is emerging as a first-line option for optimal CRT response in non-responders and in those with unfavourable CS anatomy. It is based on the premise that longitudinal dissociation exists in the proximal His bundle and disease within this bundle causes bundle branch block. Stimulation of the distal His bundle can narrow and potentially normalise a widened QRS. Animal and human studies from >40 years ago showed that temporary pacing of the distal His bundle resulted in QRS normalisation.44,45 More recent work has shown that it is feasible to permanently pace the His with standard pacing leads and that His bundle pacing can result in narrowing of the QRS in 70–90% of patients.46–48 In initial work, Lustagarten et al. noted similar outcomes between His bundle pacing and CS lead pacing for CRT, with significantly shorter procedure times with the former therapy.49 A larger series reported by Sharma et al. showed a 90% success rate in narrowing the QRS with His bundle pacing.50 The clinical response rate was 70% and mean ejection fraction increased from 30% to 44%. There was also a significant improvement in NYHA class, with participants’ symptoms improving by one NYHA class on average. Another recent series of 39 patients with RBBB from the same group showed that His bundle pacing was successful in 95% of patients and there was a favourable clinical response in 76% of patients.50 His bundle pacing offers many potential advantages: • Faster impulse propagation due to endocardial and His–Purkinje system recruitment;46,47 • Direct access to the anatomic area of interest;46 • Lack of need for optimisation of CRT lead placement or ventriculoventricular (VV) timing, since recruitment of the distal His-Purkinje system and fascicles should result in normalisation of LV electrical activation;49,50 • Endocardial pacing with QRS narrowing should avoid the negative CRT response that is sometimes associated with LV
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epicardial pacing due to ventricular proarrhythmia or unchanged dyssynchrony pattern.51 While His bundle pacing offers promise as a first-line therapy, current drawbacks include: • lack of randomised trials showing clinical improvement or mortality benefit; • high lead revision rates; • higher thresholds and current battery drain associated than for CRT; and • lack of significant long-term data of lead performance in the His position. The on-going His Bundle Pacing Versus Coronary Sinus Pacing for CRT (His-SYNC) pilot trial (NCT02700425) will randomise 40 patients to CRT with His bundle pacing versus a LV lead. Further large clinical trials are required before this form of pacing can be considered a first-line method for CRT.
Narrow QRS Narrow QRS patients are generally excluded from CRT due to the lack of benefit demonstrated in multiple large randomised controlled trials. However, selected patients with narrow QRS do benefit from CRT. Many studies have shown the CRT pacing coupled with atrioventricular (AV) node ablation, either with a LV lead or His bundle pacing, have reported improved heart failure symptoms and LV ejection fraction in patients with AF, LV dysfunction and congestive heart failure.52,53 The recently published Abate and Place in AF plus CRT (APAF-CRT) trial randomised 109 patients with a narrow QRS and permanent AF to AV node ablation and CRT versus rate control. The CRT–AV node ablation group had a significant reduction in death from any cause or hospitalisation for heart failure (12% versus 33%) and a trend towards decreased mortality (4% versus 12%).54 While CRT for narrow QRS patients in sinus rhythm is currently contraindicated, some studies have suggested that in selected patients the majority of acute haemodynamic improvement from CRT is due to AV optimisation, thus improving LV preload rather than correcting dyssynchrony.55 The His Optimized Pacing Evaluated for Heart Failure (HOPE-HF) trial (NCT02671903) will randomise 160 patients with a LV ejection fraction <40% with narrow QRS or RBBB and PR interval >200 msec to His bundle pacing with AV optimisation versus standard congestive heart failure therapy, testing the hypothesis that AV optimisation improves outcome in this group of patients.56
Atrioventricular and Ventriculoventricular Timing Two initial large trials evaluated the use of AV and VV timing optimisation to improve CRT outcomes – SmartDelay determined AV Optimization: A Comparison of AV Optimization Methods Used in CRT (SMART-AV) and Frequent Optimization Study Using the QuickOpt Method (FREEDOM), but these showed no additional benefit over nominal settings.57 As a consequence, guidelines currently only recommend AV and VV timing optimisation for CRT non-responders. Dynamic algorithms that change programmed settings based on frequent automatic assessments, thereby optimising AV and VV timing, have performed better. Adaptive CRT (aCRT) uses intrinsic conduction to determine timings and results in LV-only pacing with a sensed AV delay <220 msec and biventricular pacing otherwise. The
Drugs and Devices Figure 2: Optimisation of CRT with Adaptive CRT and Speckle Tracking Strain Imaging in Non-responders
A 75-year old male CRT non-responder with an ejection fraction of 30%, left bundle branch block with QRS duration of 160 msec, and New York Heart Association Class III heart failure symptoms due to cardiac amyloidosis. (A) RV–LV=0 msec. Bull’s eye plot and endocardial GLS graph demonstrate impaired GLS (−10.53%) in an apical sparing pattern typical of cardiac amyloidosis. Segmental peak systolic strain curves illustrate a wide range in the timing of peak systolic strain, with early systolic shortening of the septum (red arrows) and late peak contraction of the lateral wall (blue arrow). (B) LV only. Note the similarly impaired GLS (−11.68%), though with time to peak strain occurring over a narrower range. (C) RV–LV=40 msec. Peak systolic deformation is markedly improved, particularly in the septal and inferior walls, with a significant increment in GLS (−15.11%). Note the narrow range of peak strain values. (D) Velocity vector tracking demonstrates severe septal–lateral wall dyssynchrony and severely impaired longitudinal strain with RV–LV=0 msec. (E) Some improvement with LV-only pacing. (F) Near-restoration of the synchrony of velocity, with convergence toward the centre of the LV, with RV–LV=40 msec. GLS = global longitudinal strain; LV = left ventricle; RV = right ventricle.
aCRT algorithm was associated with a reduced 30-day readmission rate of 14.8% versus 24.8% in controls. Figure 2 shows optimisation of CRT with echocardiographic strain imaging using aCRT. Patients in the aCRT group with >50% LV-only pacing had an 82% clinical response rate versus 68% in the optimised biventricular pacing group.58 The Clinical Trial of the SonRtip Lead and Automatic AV–VV Optimization Algorithm in the PARADYM RF SonR CRT-D (RESPOND-CRT) trial assessed the clinical utility of the SonR™ (LinaNova) contractility sensor in CRT optimisation. The SonR sensor measures mechanical vibrations, which correlate with LV dP/dTmax. The study randomised 998 patients in a 2:1 fashion to receive weekly automatic CRT optimisation
with SonR versus echocardiographic optimisation.59 There was a 75% response rate in the SonR group versus 70% in the control group, with a 35% relative reduction in the risk of hospitalisation for heart failure. The SyncAV™ algorithm (Abbott) regularly calculates the PR interval from device electrograms and automatically adjusts the AV delay to allow for intrinsic septal activation before biventricular pacing. This algorithm shortens QRS duration during biventricular pacing to a greater extent than statically optimised AV and VV delays and LV-only pacing.60 A recent randomised study showed that programming AV delays and VV timing to promote fusion with intrinsic septal activation and targeting the shortest QRS duration with CRT pacing resulted in a higher rate of reverse remodelling than nominal
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Cardiac Resynchronisation Therapy Table 1: Options for Optimisation of Atrioventricular Delays and Ventriculoventricular Timing in CRT Pacing Echocardiographic Methods Ritter: pulsed wave Doppler of mitral inflow AV optimisation only
Doppler echocardiographic measurement of the time of MVC. AV delay [QRSonset−MVCSAVD–QRSonse− MVCLAVD]+ SAVD, where SAVD and LAVD are short (50–60 msec) and long AV delays (160–200 msec), respectively, and −MVC is the time interval between QRS onset (QRSonset) and MVC at short and long AV delay
Iterative: pulsed wave Doppler of mitral inflow AV optimisation only
AV delay is programmed by assessing mitral inflow pattern to allow for biventricular capture and separation of E and A waves without A wave truncation
Simplified (Meluzin): pulsed wave mitral inflow AV optimisation only
Longest AV delay with full biventricular capture – (5–10 msec) – (the time from the end of the A wave to onset of systolic MR)
Diastolic MR method (Ishikawa) AV optimisation only
Long AV delay is set to observe diastolic MR, and the LAVD – duration of diastolic MR is the optimal AV delay
Aortic or LVOT VTI: continuous wave Doppler of aortic flow AV and VV optimisation
AV delay and VV timing are serially programmed to achieve maximum aortic or LVOT VTI
Mitral VTI AV and VV optimisation
AV delay and VV timing are serially programmed to maximise diastolic mitral inflow of both E and A wave
MR jet AV and VV optimisation
The slope of continuous wave Doppler of the MR jet is measured as a marker of LV contractility. The AV and VV delays are serially programmed to maximise dP/dt
Tissue Doppler imaging AV and VV optimisation
VV timings are optimised to the maximum tissue Doppler velocity sum of all 16 segments of the LV
Speckle tracking strain imaging VV optimisation only
VV timings are optimised to peak global longitudinal strain of the LV
Device-based Methods SmartDelay™ (Boston Scientific) AV optimisation only
IEGM-based method that uses sensed atrial and paced atrial AV intervals and intrinsic RV to LV conduction time to calculate AV delay to allow for fusion between native septal activation and biventricular pacing
QuickOpt™ (Abbott) AV and VV optimisation
IEGM-based method that calculates AV interval based on length of RA lead IEGM duration to allow for ventricular pacing to occur after atrial depolarisation is complete. VV interval is calculated by comparing instrinic conduction between the RV and LV IEGMs and conduction time between RV and LV during RV and LV pacing
AdaptiveCRT™ (Medtronic) AV and VV optimisation
IEGM-based method that dynamically calculates AV delay every minute. LV-only pacing is delivered for native AV interval <220 msec and AV delay is time from RA sense or RA pace to RV sense – 40 msec. If instrinic AV interval >220 msec, then biventricular pacing is delivered after the end of the atrial IEGM and >50 msec before RV sense. VV interval is based on AV interval and time between RV sense and end of the ventricular IEGM on the far field signal
SyncAV™ (Abbott) AV optimisation only
IEGM-based method that calculates and dynamically sets AV delay by assessing instrinic AV delay every 256 beats and subtracting a programmed offset (50 msec nominally, but can be set to 10–60 msec)
SonR™ (LivaNova) AV and VV optimisation
Using a lead-based micro-accelerometer to detect mechanical vibrations (endocardial acceleration signal), AV and VV delays are dynamically optimised weekly during rest and exercise to maximise the peak endocardial acceleration signal, which is a surrogate for LV contractility
CRT AutoAdapt™ (Biotronic) AV and VV optimisation
IEGM-based method similar to AdaptiveCRT. AV interval to RV and LV is measured based on sensed and paced atrial beats. LV-only pacing is delivered if A-paced AV interval is <250 msec and A to LV interval is longer than A to RV interval, otherwise biventricular pacing is delivered. AV delay is dynamically set at 70% of AV interval or AV interval – 40 msec, depending on which is shorter
Other Methods Invasive haemodynamic AV and VV optimisation
An open-lumen micromanometer catheter or pressure wire directly placed in the LV is used to target maximum rate of increase of LV pressure (dP/dtmax) to optimise AV and VV timing
Impedance cardiography (Task Force® Monitor Systems, CNSystems) AV and VV optimisation
Multiple electrodes placed on the chest, neck, and abdomen measure transthoracic impedance. Increased aortic blood flow and cardiac output are associated with lower transthoracic impedance. AV and VV timings are optimised to target the lowest impedance value, which corresponds to maximum cardiac output
Acoustic cardiography (Audicor™, Inovise Medical) AV and VV optimisation
Using an ECG electrodes in V3 and V4 positions to detect the first, second and third heart sounds and the QRS, time from onset of the Q wave to the mitral component of S1 is measured (electromechanical activation time) and the strength of S3 is assessed. AV and VV timings are optimised for the shortest electromechanical activation time and strongest S3
Finger plethysmography AV and VV optimisation
AV and VV delays are optimised using finger oximetry to target the maximum pulse amplitude of the finger plethysmogram wave form
Noninvasive blood pressure measurement AV and VV optimisation
AV and VV delays optimised with serial blood pressure measurements targeting the peak mean systolic blood pressure over multiple measurements
Atrioventricular = AV; IEGM = intracardiac electrogram; LAVD = long atrioventricular delay; LVOT = left ventricular outflow tract; MR = mitral regurgitation; MVC = mitral valve closure; RA = right atrial; SAVD = short atrioventricular delay; VTI = velocity time integral; VV = ventriculoventricular. Adapted from Rowe and Kaye 201870 and Gorcsan et al. 2008.71
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Drugs and Devices settings (74% versus 53%).61 Table 1 summarises the various methods of AV and VV timing optimisation.
CRT with an ICD There has been significant debate as to whether the addition of ICD therapy to CRT improves outcomes. The majority of patients who meet the CRT criteria are also suitable for an ICD for the primary prevention of sudden cardiac death. Furthermore, most patients enrolled in the pivotal trials demonstrating CRT benefit also received an ICD.62
benefit from ICD therapy in addition to CRT.68 The CeRtiTuDe study showed that while patients receiving CRT-P had a higher mortality, 95% of this excess mortality was not due to sudden cardiac death.69 The guidelines currently recommend CRT-P for NYHA Class IV heart failure and favour CRT-D for younger patients with milder heart failure, with ischaemic heart disease and life expectancy >1 year.65 CRT-P is recommended for older patients with more advanced congestive heart failure, severe renal dysfunction, frailty and lower life expectancy.65
Conclusion Multiple retrospective studies have shown better survival in patients who received a CRT-D rather than a CRT pacemaker (CRT-P).63,64 Additionally, in patients with ischaemic cardiomyopathy, CRT-D implantation was associated with improved mortality over CRT-P.63,64 This may be because the patients who received CRT-P were generally older, more likely female and more often had non-ischaemic cardiomyopathy as well as a greater number of comorbidities, which likely contributed to their lower survival.63,64 In its favour, CRT-P is costs less and has been associated with fewer complications than CRT-D.65 It should be noted that large cohort studies have shown that in patients with non-ischaemic cardiomyopathy, the addition of ICD therapy to CRT is not associated with improved outcomes;66 however, in non-ischaemic cardiomyopathy with scarring, specifically LV mid-wall fibrosis seen with cardiac MRI, CRT-D improves mortality and reduces major adverse cardiac events.67 The Prospective Observational Study of the ICD in Sudden Cardiac Death Prevention (PROSe-ICD) study developed a clinical decision tool using biomarkers and clinical variables to help guide those who would
ristow MR, Saxon LA, Boehmer J, et al. Cardiac B resynchronization therapy with or without an implantable defibrillator in advanced chronic heart failure. N Engl J Med 2004;350:2140–250. https://doi.org/10.1056/NEJMoa032423; PMID: 15152059. Cleland JG, Daubert JC, Erdmann E, et al.; Cardiac Resynchronization‐Heart Failure (CARE‐HF) Study Investigators. The effect of cardiac resynchronization on morbidity and mortality in heart failure. N Engl J Med 2005;352:1539–49. https://doi.org/10.1056/NEJMoa050496; PMID: 15753115. Daubert JC, Saxon L, Adamson PB, et al. 2012 EHRA/HRS expert consensus statement on cardiac resynchronization therapy in heart failure: implant and follow-up recommendations and management. Europace 2012;14:1236–86. https://doi. org/10.1093/europace/eus222; PMID: 22930717. Zareba W, Klein H, Cygankiewicz I, et al. Effectiveness of cardiac resynchronization therapy by QRS morphology in the Multicenter Automatic Defibrillator Implantation Trial – Cardiac Resynchronization Therapy (MADIT-CRT). Circulation 2011;123:1061–72. https://doi.org/10.1161/ CIRCULATIONAHA.110.960898; PMID: 21357819. Birnie DH, Ha A, Higginson L, et al. Impact of QRS morphology and duration on outcomes after cardiac resynchronization therapy: results from the Resynchronization-Defibrillation for Ambulatory Heart Failure Trial (RAFT). Circ Heart Fail 2013;6:1190– 98. https://doi.org/10.1161/CIRCHEARTFAILURE.113.000380; PMID: 23995437. Nery PB, Ha AC, Keren A, et al. Cardiac resynchronization therapy in patients with left ventricular systolic dysfunction and right bundle branch block: A systematic review. Heart Rhythm 2011;8:1083–7. https://doi.org/10.1016/j. hrthm.2011.01.041; PMID: 21300176. Kandala J, Upadhyay GA, Altman RK, et al. QRS morphology, left ventricular lead location, and clinical outcome in patients receiving cardiac resynchronization therapy. Eur Heart J 2013;34:2252–62. https://doi.org/10.1093/eurheartj/eht123; PMID: 23571836. Varma N. Left ventricular conduction delays and relation to QRS configuration in patients with left ventricular dysfunction. Am J Cardiol 2009;103:1578–85. https://doi. org/10.1016/j.amjcard.2009.01.379; PMID: 19463519. Kutyifa V, Stockburger M, Daubert JP, et al. PR interval identifies clinical response in patients with nonleft bundle branch block: a Multicenter Automatic Defibrillator Implantation Trial-Cardiac Resynchronization Therapy substudy. Circ Arrhythm Electrophysiol 2014;7:645–51. https://doi.org/10.1161/CIRCEP.113.001299; PMID: 24963007.
CRT is a potent therapy for improving outcomes and reducing mortality in heart failure. Despite being an established first-line therapy, significant issues remain in patient selection and the proper delivery of CRT. On-going research into new tools and methods to improve CRT therapy will allow for further improvements in outcomes in what has already proven an innovative therapy for the treatment of symptomatic LV systolic dysfunction.
Clinical Perspective • To improve CRT, more accurate patient selection is needed to include new populations that will respond, and limit implantation in patient groups with poor outcomes. • Better coronary sinus lead placement location and device programming is needed to improve CRT outcomes with existing technology. • New pacing approaches are required to improve left ventricular synchronisation.
10. D aubert C, Gold MR, Abraham WT, et al. Prevention of disease progression by cardiac resynchronization therapy in patients with asymptomatic or mildly symptomatic left ventricular dysfunction: insights from the European cohort of the REVERSE (Resynchronization Reverses Remodeling in Systolic Left Ventricular Dysfunction) trial. J Am Coll Cardiol 2009;54:1837–46. https://doi.org/10.1016/j.jacc.2019.08.011; PMID: 19800193. 11. Ruschitza F, Abraham WT, Singh JP, et al. Cardiacresynchronization therapy in heart failure with a narrow QRS complex. N Engl J Med 2013;369:1395–405. https://doi. org/10.1056/NEJMoa1306687; PMID: 23998714. 12. Cleland JG, Abraham WT, Linde C, et al. An individual patient meta-analysis of five randomized trials assessing the effects of cardiac resynchronization therapy on morbidity and mortality in patients with symptomatic heart failure. Eur Heart J 2013;34:3547–56. https://doi.org/10.1093/eurheartj/eht290; PMID: 23900696. 13. Stankovic I, Prinz C, Ciarka A, et al. Relationship of visually assessed apical rocking and septal flash to response and long-term survival following cardiac resynchronization therapy (PREDICT-CRT). Eur Heart J Cardiovasc Imaging 2016;17:262–9. https://doi.org/10.1093/ehjci/jev288; PMID: 26588984. 14. Bilchick K, Kuruvilla S, Hamirani Y, et al. Impact of mechanical activation, scar, and electrical timing on cardiac resynchronization therapy response and clinical outcomes. J Am Coll Cardiol 2014;63:1657–66. https://doi. org/10.1016/j.jacc.2014.02.533; PMID: 24583155. 15. Chung ES, Leon AR, Tavazzi L, et al. Results of the Predictors of Response to CRT (PROSPECT) trial. Circulation 2008;117:2608–16. https://doi.org/10.1161/ CIRCULATIONAHA.107.743120; PMID: 18458170. 16. Sohal M, Shetty A, Duckett S, et al. Noninvasive assessment of LV contraction patterns using CMR to identify responders to CRT. JACC Cardiovasc Imaging 2013;6:864–73. https://doi. org/10.1016/j.jcmg.2012.11.019; PMID: 23735442. 17. Maass AH, Vernooy K, Wijers SC, et al. Refining success of cardiac resynchronization therapy using a simple score predicting the amount of reverse ventricular remodelling: results from the Markers and Response to CRT (MARC) study. Europace 2018;20:e1–10. https://doi.org/10.1093/europace/ eux169; PMID: 28582522. 18. Ploux S, Lumens J, Whinnett Z, et al. Noninvasive electrocardiographic mapping to improve patient selection for cardiac resynchronization therapy: Beyond QRS duration and left bundle branch block morphology. J Am Coll Cardiol 2013;61:2435–43. https://doi.org/10.1016/j.jacc.2013.01.093;
PMID: 23602768. 19. J ohnson WB, Vatterott PJ, Peterson MA, et al. Body surface mapping using an ECG belt to characterize electrical heterogeneity for different left ventricular pacing sites during cardiac resynchronization: Relationship with acute hemodynamic improvement Heart Rhythm 2017;14:385–91. https://doi.org/10.1016/j.hrthm.2016.11.017; PMID: 27871987. 20. Dong YX, Powell BD, Asirvatham SJ, et al. Left ventricular lead position for cardiac resynchronization: a comprehensive cinegraphic, echocardiographic, clinical, and survival analysis. Europace 2012;14:1139–47. https://doi.org/10.1093/europace/ eus045; PMID: 22467754. 21. Saxon LA, Olshansky B, Volosin K, et al. Influence of left ventricular lead location on outcomes in the COMPANION study. J Cardiovasc Electrophysiol 2009;20:764–8. https://doi. org/10.1111/j.1540-8167.2009.01444.x; PMID: 19298563. 22. Singh JP, Klein HU, Huang DT, et al. Left ventricular lead position and clinical outcome in the multicenter automatic defibrillator implantation trial-cardiac resynchronization therapy (MADIT-CRT) trial. Circulation 2011;123:1159–66. https://doi.org/10.1161/CIRCULATIONAHA.110.000646; PMID: 21382893. 23. Khan FZ, Virdee MS, Palmer CR, et al. Targeted left ventricular lead placement to guide cardiac resynchronization therapy: the TARGET study: a randomized, controlled trial. J Am Coll Cardiol 2012;59:1509–18. https://doi.org/10.1016/j. jacc.2011.12.030; PMID: 22405632. 24. Saba S, Marek J, Schwartzman D, et al. Echocardiographyguided left ventricular lead placement for cardiac resynchronization therapy: results of the Speckle Tracking Assisted Resynchronization Therapy for Electrode Region trial. Circ Heart Fail 2013;6:427–34. https://doi.org/10.1161/ CIRCHEARTFAILURE.112.000078; PMID: 23476053. 25. Bleeker GB, Kaandorp TA, Lamb HJ, et al. Effect of posterolateral scar tissue on clinical and echocardiographic improvement after cardiac resynchronization therapy. Circulation 2006;113:969–76. https://doi.org/10.1161/ CIRCULATIONAHA.105.543678; PMID: 16476852. 26. Singh JP, Fan D, Heist EK, et al. Left ventricular lead electrical delay predicts response to cardiac resynchronization therapy. Heart Rhythm 2006;3:1285–92. https://doi.org/10.1016/j. hrthm.2006.07.034; PMID: 17074633. 27. Gold MR, Singh JP, Ellenbogen KA, et al. Interventricular electrical delay Is predictive of response to cardiac resynchronization therapy. J Am Coll Cardiol Electrophysiol 2016;2:438–47. https://doi.org/10.1016/j.jacep.2016.02.018; PMID: 29759863. 28. Kandala J, Upadhyay GA, Altman RK, et al. Electrical delay
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Cardiac Resynchronisation Therapy
in apically positioned left ventricular leads and clinical outcome after cardiac resynchronization therapy. J Cardiovasc Electrophysiol 2013;24:182–7. https://doi.org/10.1111/j.15408167.2012.02428.x; PMID: 22966852. Silva JN, S. Ghosh S, Bowman TM, et al. Cardiac resynchronization therapy in pediatric congenital heart disease: Insights from noninvasive electrocardiographic imaging. Heart Rhythm 2009;6:1178–85. https://doi. org/10.1016/j.hrthm.2009.04.017; PMID: 19632630. Engels EB, Strik M, van Middendorp LB, et al. Prediction of optimal cardiac resynchronization by vectors extracted from electrograms in dyssynchronous canine hearts. J Cardiovasc Electrophysiol 2017;28:944–51. https://doi.org/10.1111/ jce.13241; PMID: 28467647. Singh JP, Berger RD, Doshi RN, et al. B-LBCT01-03 – targeted left ventricular lead implantation in non-left bundle branch block patients: primary results of the enhance CRT pilot study. Heart Rhythm Society Meeting, May 2018. Khan FZ, Virdee MS, Gopalan D, et al. Characterization of the suitability of coronary venous anatomy for targeting left ventricular lead placement in patients undergoing cardiac resynchronization therapy. Europace 2009;11:1491–5. https:// doi.org/10.1093/europace/eup292; PMID: 19880411. Hyde ER; Behar JM, Claridge S, et al. Beneficial effect on cardiac resynchronization from left ventricular endocardial pacing Is mediated by early access to high conduction velocity tissue: Electrophysiological Simulation Study. Circ Arrhythm Electrophysiol 2015;8:1164–72. https://doi.org/10.1161/ CIRCEP.115.002677; PMID: 26136400. Morgan JM, Biffi M, Geller L, et al. ALternate Site Cardiac ResYNChronization (ALSYNC): A prospective and multicentre study of left ventricular endocardial pacing for cardiac resynchronization therapy. Eur Heart J 2016;37:2118–27. https:// doi.org/10.1093/eurheartj/ehv723; PMID: 26787437. Betts TR, Gamble JH, Khiani R, et al. Development of a technique for left ventricular endocardial pacing via puncture of the interventricular septum. Circ Arrhythm Electrophysiol 2014;7:17–22. https://doi.org/10.1161/CIRCEP.113.001110; PMID: 24425419. Rademakers LM, van Hunnik A, Kuiper M, et al. A possible role for pacing the LV septum in cardiac resynchronization therapy. J Am Coll Cardiol Electrophysiol 2016;2:413–22. https://doi. org/10.1016/j.jacep.2016.01.010; PMID: 29759859. Reddy VY, Miller MA, Neuzil R, et al. Cardiac Resynchronization Therapy with Wireless Left Ventricular Endocardial Pacing: The SELECT-LV Study. J Am Coll Cardiol 2017;69:2119–29. https://doi.org/10.1016/j.jacc.2017.02.059; PMID: 28449772. Leclercq C, Gadler F, Kranig W, et al. A randomized comparison of triple-site versus dual-site ventricular stimulation in patients with congestive heart failure. J Am Coll Cardiol 2008;51:1455–62. https://doi.org/10.1016/j. jacc.2007.11.074; PMID: 18402900. Providencia R, Rogers D, Papageorgiou N, et al. Long-term results of tri-ventricular versus bi-ventricular pacing in heart failure: a propensity-matched comparison. J Am Coll Cardiol Electrophysiol 2016;2:825–35. https://doi.org/10.1016/ j.jacep.2016.05.015; PMID: 29759767. Enarczyk R, Kowalski O, Sredniawa D, et al. Implantation feasibility, procedure‐related adverse events and lead performance during 1-year follow-up in patients undergoing triple‐site cardiac resynchronization therapy: A substudy of TRUST CRT Randomized Trial. J Cardiovasc Electrophysiol 2012;23:1228–36. https://doi.org/10.1111/j.15408167.2012.02375.x; PMID: 22651239. Bordachar P, Gras D, Clementy N, et al. Clinical impact of an additional left ventricular lead in cardiac resynchronization therapy nonresponders: The V3 trial. Heart Rhythm 2018;6:870–6. https://doi.org/10.1016/j.hrthm.2017.12.028; PMID: 29288035. Zanon F, Baracca E, Pastore G, et al. Multipoint pacing by a left ventricular quadripolar lead improves the acute hemodynamic response to CRT compared with conventional
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biventricular pacing at any site. Heart Rhythm 2015;12:975–81. https://doi.org/10.1016/j.hrthm.2015.01.034; PMID: 25625721. Niazi I, Baker J, Corbisiero R, et al. Safety and efficacy of multipoint pacing in cardiac resynchronization therapy: The MultiPoint Pacing Trial. J Am Coll Cardiol Electrophysiol 2017;3:1510–8. https://doi.org/10.1016/j.jacep.2017.06.022; PMID: 29759832. Narula OS. Longitudinal dissociation in the His bundle. Bundle branch block due to asynchronous conduction within the His bundle in man. Circulation 1977;56:996–1006. https://doi. org/10.1161/01.CIR.56.6.996; PMID: 923070. El-Sherif N, Amay YLF, Schonfield C, et al. Normalization of bundle branch block patterns by distal His bundle pacing. Clinical and experimental evidence of longitudinal dissociation in the pathologic his bundle. Circulation 1978;57:473–83. https://doi.org/10.1161/01.CIR.57.3.473; PMID: 624157. Deshmukh P, Casavant DA, Romanyshyn M, et al. Permanent, direct His-bundle pacing: a novel approach to cardiac pacing in patients with normal His-Purkinje activation. Circulation 2000;101:869–77. https://doi.org/10.1161/01.CIR.101.8.869; PMID: 10694526. Lustgarten DL, Crespo EM, Arkhipova-Jenkins I, et al. His-bundle pacing versus biventricular pacing in cardiac resynchronization therapy patients: a crossover design comparison. Heart Rhythm 2015;12:1548–57. https://doi. org/10.1016/j.hrthm.2015.03.048; PMID: 25828601. Sharma PS, Dandamudi G, Herweg B, et al. Permanent His bundle pacing as an alternative to biventricular pacing for cardiac resynchronization therapy: a multicenter experience. Heart Rhythm 2018;15:413–20. https://doi.org/10.1016/j. hrthm.2017.10.014; PMID: 29031929. Lustgarten DL, Calame S, Crespo EM, et al. Electrical resynchronization induced by direct His-bundle pacing. Heart Rhythm 2010;7:15–21. https://doi.org/10.1016/j. hrthm.2009.09.066; PMID: 19914142. Sharma PS, Naperkowski A, Bauch TD, et al. Permanent His bundle pacing for cardiac resynchronization therapy in patients with heart failure and right bundle Branch block. Circ Arrhythm Electrophysiol 2018;11:e006613. https://doi. org/10.1161/CIRCEP.118.006613; PMID: 30354292. Scott PA, Yue AM, Watts E, et al. Transseptal left ventricular endocardial pacing reduces dispersion of ventricular repolarization. Pacing Clin Electrophysiol 2011;34:1258–66. https:// doi.org/10.1111/j.1540-8159.2011.03138.x; PMID: 21615758. Doshi RN, Daoud EG, Fellows C, et al. Left ventricular-based cardiac stimulation post AV nodal ablation evaluation (the PAVE study). J Cardiovasc Electrophysiol 2005;16:1160–5. https:// doi.org/10.1111/j.1540-8167.2005.50062.x; PMID: 16302897. Deshmukh PM, Romanyshyn M. Direct His-bundle pacing: present and future. Pacing Clin Electrophysiol 2004;27:862–70. https://doi.org/10.1111/j.1540-8159.2004.00548.x; PMID: 15189517. Brignole M, Pokushalov E, Pentimalli F, et al.; APAF-CRT Investigators. A randomized controlled trial of atrioventricular junction ablation and cardiac resynchronization therapy in patients with permanent atrial fibrillation and narrow QRS. Eur Heart J 2018;39:3999–4008. https://doi.org/10.1093/eurheartj/ ehy555; PMID: 30165479. Jones S, Lumens J, Sohaib SM, et al. Cardiac resynchronization therapy: mechanisms of action and scope for further improvement in cardiac function. Europace 2017;19:1178–86. https://doi.org/10.1093/europace/euw136; PMID: 27411361. Keene D, Arnold A, Shun-Shin, M, et al. Rationale and design of the randomized multicenter His Optimized Pacing Evaluated for Heart Failure (HOPE-HF) trial. ESC Heart Failure 2018:5:966–77. https://doi.org/10.1002/ehf2.12315; PMID: 29984912. Ellenbogen KA, Gold MR, Meyer TE, et al. Primary results from the SmartDelay determined AV optimization: a comparison to other AV delay methods used in cardiac resynchronization therapy (SMART-AV) trial: A randomized
trial comparing empirical, echocardiography-guided, and algorithmic atrioventricular delay programming in cardiac resynchronization therapy. Circulation 2010;122:2660–8. https:// doi.org/10.1161/CIRCULATIONAHA.110.992552; PMID: 21098426. Starling RC, Krum H, Bril S, et al. Impact of a novel adaptive optimization algorithm on 30-day readmissions: Evidence from the Adaptive CRT Trial. JACC Heart Fail 2015;3:565–72. https://doi.org/10.1016/j.jchf.2015.03.001; PMID: 26071616. Brugada J, Delnoy PP, Brachmann J, et al. Contractility sensor guided optimization of cardiac resynchronization therapy: results from the RESPOND-CRT trial. Eur Heart J 2017;38:730–8. https://doi.org/10.1093/eurheartj/ehw526; PMID: 27941020. Varma N, O’Donnell D, Bassiouny M, et al. Programming cardiac resynchronization therapy for electrical synchrony: Reaching beyond left bundle branch block and left ventricular activation delay. J Am Heart Assoc 2018;7:e007489. https://doi. org/10.1161/JAHA.117.007489; PIMID: 29432133. Trucco E, Tolosana JM, Arbelo E, et al. Improvement of reverse remodeling using electrocardiogram fusion-optimized intervals in cardiac resynchronization therapy: A randomized study. J Am Coll Cardiol Electrophysiol 2018;4:181–9. https://doi. org/10.1016/j.jacep.2017.11.020; PMID: 29749935. Katritsis DG, Auricchio A. Do we need an implantable cardioverter-defibrillator for primary prevention in cardiac resynchronisation therapy patients? Arrhythm Electrophysiol Rev 2018;7:157–8. https://doi.org/10.15420/aer.2018.7.3.EO1; PMID: 30416727. Bogale N, Priori S, Cleland JGF, et al. The European CRT Survey: 1 year (9–15 months) follow-up results. Eur J Heart Fail 2012;14:61–73. https://doi.org/10.1093/eurjhf/hfr158; PMID: 22179034. Barra S, Providência R, Tang A, et al. Importance of implantable cardioverter-defibrillator back-up in cardiac resynchronization therapy recipients: A systematic review and meta-analysis. J Am Heart Assoc 2015;4:e002539. https:// doi.org/10.1161/JAHA.115.002539; PMID: 26546574. Brignole M, Auricchio A, Baron-Esquivias G, et al. 2013 guidelines on cardiac pacing and cardiac resynchronization therapy. Eur Heart J 2013;34:2281–329. https://doi.org/10.1093/ eurheartj/eht150; PMID: 23801822. Barra S, Boveda S, Providencia R, et al. Adding defibrillation therapy to cardiac resynchronization on the basis of the myocardial substrate. J Am Coll Cardiol 2017;69:1669–78. https:// doi.org/10.1016/j.jacc.2017.01.042; PMID: 28359511. Leyva F, Zegard A, Acquaye E, et al. Outcomes of cardiac resynchronization therapy with or without defibrillation in patients with nonischemic cardiomyopathy. J Am Coll Cardiol 2017;70:1216–27. https://doi.org/10.1016/j.jacc.2017.07.712; PMID: 28859784. Nauffal V, Zhang Y, Tanawuttiwat T, et al. Clinical decision tool for CRT-P vs. CRT-D implantation: Findings from PROSE-ICD. PLoS ONE 2017;12:e0175205. https://doi.org/10.1371/journal. pone.0175205; PMID: 28388657. Marijon E, Leclercq C, Narayanan K, et al. Causes-of-death analysis of patients with cardiac resynchronization therapy: an analysis of the CeRtiTuDe cohort study. Eur Heart J 2015;36:2767–76. https://doi.org/10.1093/eurheartj/ehv455; PMID: 26330420. Rowe MK, Kaye GC. Advances in atrioventricular and interventricular optimization of cardiac resynchronization therapy – what’s the gold standard? Expert Rev Cardiovasc Ther 2018;16:183–96. https://doi.org/10.1080/14779072.2018.1427 582; PMID: 29338475. Gorcsan J 3rd,, Abraham T, Agler DA, et al. Echocardiography for cardiac resynchronization therapy: recommendations for performance and reporting – a report from the American Society of Echocardiography Dyssynchrony Writing Group endorsed by the Heart Rhythm Society. J Am Soc Echocardiogr 2008;21:191–213. https://doi.org/10.1016/j.echo.2008.01.003; PMID: 18314047.
Drugs and Devices
Update in Cardiac Pacing Nishant Verma and Bradley P Knight Division of Cardiology, Department of Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, US
Abstract Initial efforts to artificially stimulate the heart were borne out of a necessity to prevent catastrophic bradycardic events. The initial pacemaker systems were large, bulky external devices. However, advancements in technology allowed for the development of internally powered, fully implantable devices. Further advancements resulted in more complex, programmable devices, but the overall systems have remained largely unchanged for more than 50 years. The most recent advancements in the field have represented fundamental paradigm shifts in both pacemaker design and the approach to cardiac pacing. These efforts have focused on reducing and eliminating hardware to reduce the risk of complications and to focus on improving cardiac efficiency to improve clinical outcomes. In this article, the authors explore these advances including leadless pacemaker systems, permanent His bundle pacing and advances in the field of cardiac resynchronisation therapy.
Keywords Pacemakers, bradycardia, leadless pacemakers, permanent His bundle pacing, cardiac resynchronisation therapy Disclosure: NV receives honoraria from Medtronic and Biotronik; BPK is a consultant for and receives honoraria from Biotronik, Boston Scientific, Medtronic and Abbott. Received: 14 January 2019 Accepted: 11 April 2019 Citation: Arrhythmia & Electrophysiology Review 2019;8(3):228–33. DOI: https://doi.org/10.15420/aer.2019.15.3 Correspondence: Nishant Verma, 251 East Huron Street, Feinberg 8-503, Chicago, IL 60611, US. E: email@example.com Open Access: This work is open access under the CC-BY-NC 4.0 License which allows users to copy, redistribute and make derivative works for non-commercial purposes, provided the original work is cited correctly.
A Brief History of Cardiac Pacing Electrical stimulation of the heart was used sporadically throughout the 19th century, generating a set of case reports, largely related to attempts to resuscitate people.1 The contemporary field of cardiac pacing emerged in the 20th century (Figure 1). The first use of pacemakers in a modern sense was in the late 1920s when Australian anaesthetist Mark Lidwell and American physiologist Albert Hyman, working independently of each other, developed the first cardiac pacemaker machines. Dr Hyman created the first device to artificially pace the heart in 1932 and coined the term ‘artificial pacemaker’.2 At this time, however, the invention was regarded with scepticism and was not widely adopted. In the 1950s, significant breakthroughs in the field of cardiac pacing occurred. During this time, Paul Zoll created a completely external system for transcutaneous pacing, a system that is still in use today in emergencies.3 While effective, this technique was limited due to the use of high voltages and the resultant painful stimulation from external thoracic pacing. At this time, the field of cardiac surgery was rapidly advancing and a frequently encountered complication was damage to the His bundle with resultant atrioventricular (AV) block. Electrodes directly attached to the heart could be connected to an external generator to stimulate the heart and allow for recovery of the conduction system. C Walton Lillehei and Earl Bakken, from the University of Minnesota, developed a battery-powered external pacemaker, obviating the need for an external power source and making the system more reliable and portable.4 In 1958, Swedish physicians Ake Senning and Rune Elmqvist implanted the first fully internal pacemaker.4 The pulse generator was placed
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in the epigastrium and was attached to epicardial leads. The first patient to receive this device, Arne Larsson, underwent 26 pacemaker procedures over the course of his lifetime and died at the age of 86, outliving his original physicians.5 Subsequent improvements in the battery technology, the size and programmability of the pulse generators, and the use of transvenous components have led to our modern pacemaker systems. However, the overall concept and design has not changed significantly since the mid-1960s.
Limitations of Current Pacemaker Systems Current transvenous pacemakers consist of a pulse generator, which contains the battery and electronics, and the leads that travel from the pulse generator and contact the myocardium to sense cardiac activity and deliver electrical impulses (Figure 2). These systems are effective for the treatment of symptomatic bradycardia; however, the overall design has not significantly changed in more than 50 years. Many of the limitations and complications of these devices are related to their overall design and construction, particularly the leads. The leads consist of an insulation-encapsulated conductor that is repeatedly subjected to cardiac and shoulder motion, which may result in mechanical stresses that can cause them to fracture over time. The pulse generator sits in an extravascular space and can serve as a nidus for bacterial infection with the leads serving as a portal of entry into the bloodstream. In addition, the fundamental approach to transvenous pacing, that the native His-Purkinje system can be bypassed and replaced with non-physiological electrical stimulation, has been shown to result in reduced cardiac synchrony and efficiency with worse clinical outcomes in some situations.
© RADCLIFFE CARDIOLOGY 2019
Update in Cardiac Pacing Figure 1: Early Advancements in Cardiac Pacing
Figure 3: Early Complications of Pacemaker Implantation
Albert Hyman creates first cardiac “artificial pacemaker”
AC-powered pacemaker units attached to an extension cord
Earl Bakken and C. Walton Lillehei create a batterypowered external pacemaker
Rune Elmqvist and Ake Senning create the first fully implantable cardiac pacemaker
A: In 1932, Albert Hyman created an ‘artificial pacemaker’. B: In the 1950s, external AC-powered units were used to treat transient atrioventricular block, especially after cardiac surgery. C: In the late 1950s, Earl Bakken, founder of Medtronic, developed the first batterypowered external pacemaker, increasing the reliability and portability of these external systems. D: The first self-contained, implantable pacemaker was placed in 1958 by Ake Senning and Rune Elmqvist in Sweden. Sources: Technical Museum Vienna; Dr Seymour Furman; Bakken Library and Museum; and Siemens Healthcare GmbH.
Figure 2: Modern Dual-chamber Pacemaker
A: This patient developed a pneumothorax after the implantation of a dual chamber pacemaker. Due to an unusual variant in this patient’s anatomy – a single pleural cavity or ‘buffalo chest’ – he developed bilateral pneumothoraces which required a chest tube. B: This patient developed a severe haematoma after pacemaker implantation. The presence of haematoma significantly increases the risk of pocket infection and this patient went on to develop bacteraemia and a systemic infection requiring device extraction. C: This patient developed right atrial lead perforation noted on the chest X-ray the next morning. The atrial lead travelled through the lateral wall of the right atrium and into the right lung, also creating a large right-sided pneumothorax. The patient required chest tube placement as well as repositioning of the right atrial lead.
Recent Advancements in Cardiac Pacing
Though the devices have become smaller and increased in complexity and programmability, the overall design of transvenous pacemakers has not fundamentally changed for more than 50 years. The device consists of a pulse generator (red rectangle), which contains the battery and electronics of the device and the leads (red arrows) which sense cardiac activity and depolarise the myocardial tissue on demand.
The basic design of cardiac pacing devices has not significantly changed since the mid-1960s and their limitations are well known. Recent advancements have attempted to address these limitations by reducing hardware and improving cardiac efficiency. These attempts include the advent of leadless pacemaker systems as well as attempts to improve cardiac efficiency with permanent His bundle pacing (PHBP), algorithms designed to mimic normal physiology and new technologies for cardiac resynchronisation therapy (CRT).
Complications from pacemaker implantation can be divided into immediate, intermediate and late-term complications. The rates of complications range from <1% to 6% and require prompt recognition and management.6
Leadless pacemakers represent a fundamental paradigm shift in the design of pacemaker systems with the goal of creating small, completely intracardiac units without transvenous leads and disconnected from any extravascular components. Two designs have been explored for leadless pacemakers – single and multicomponent systems.
Immediate, procedure-related complications are related to the implant process and can include pneumothorax and haemothorax, pocket haematoma, cardiac perforation and lead dislodgements (Figure 3). Intermediate complications include device infection, venous thrombosis or stenosis, pain or discomfort at the pocket site, mechanical disruption of the tricuspid valve with resultant tricuspid regurgitation and, rarely, discomfort with ventricular pacing. Late complications can include lead fracture or insulation breaks due to mechanical stresses, increases in pacing threshold or impedances due to tissue ingrowth, and device infections, often with systemic bloodstream infection. Treatment of the late-term complications of device implantation is often complicated and may require lead extraction, which can be technically challenging and carries a risk of central venous or cardiac perforation, haemothorax and death.
ARRHYTHMIA & ELECTROPHYSIOLOGY REVIEW
A single-component system has an individual, small unit which contains the entire pacemaker (battery, electronics and electrodes) which is implanted in the heart using a deflectable delivery sheath (Figure 4). This simple design allows easy implantation of an energy-efficient system and eliminates the need for extravascular components and leads. The commercially available leadless pacemakers that are currently used are of this design. However, there are limitations of this system, most notably the difficulty with device retrieval for infection, premature device failure, or battery depletion. There are also uncertain risks such as thrombus formation and risk of infection. In addition, the currently available systems can only be used for single chamber ventricular pacing (VVI or VVIR), limiting their widespread applicability. For most patients with sinus node dysfunction and AV block, a single chamber leadless pacemakers is a suboptimal choice compared with a dual chamber pacemaker; therefore, they are largely limited to patients
Drugs and Devices Figure 4: Transcatheter Delivery of a Leadless Pacemaker
Figure 5: Unipolar and Bipolar Recordings from a Permanent His Bundle Pacing Lead
A H V
A leadless pacemaker (red circle) is inserted into the right ventricle via a catheter delivery system (red arrows). A large sheath is placed in the right femoral vein and advanced to the inferior vena cava. Through this sheath, the catheter delivery system is advanced into the right atrium and across the tricuspid valve. The delivery system is used to position and deliver the leadless pacemaker in a septal location within the right ventricle.
with permanent AF and slow ventricular response or those with paroxysmal, infrequent AV block.7 Efforts are currently underway to develop a dual chamber design, though challenges with device-todevice communication and active fixation in the thin-walled right atrium will need to be overcome. Two leadless pacing devices were available in the US, including the Nanostim™ leadless cardiac pacemaker (Abbott) and the Micra™ transcatheter pacing system (Medtronic). However, the Nanostim device was recalled in 2016 due to issues of premature battery depletion and is not currently available. In clinical trials, both systems demonstrated a high rate of successful implantation (>95%).8,9 However, there was a 4–6.5% rate of major complications, including perforations or pericardial effusions in 1.5–1.6% of cases.8,9 Pacemaker measurements were stable at 6 months.8,9 In analyses comparing patients with leadless pacemakers to a cohort of patients with transvenous pacemakers, there were fewer short and intermediate-term complications with leadless pacing, largely driven by an absence of lead and pocket complications and a low risk of infection.9,10 Dual chamber leadless pacing systems are under development. There are also efforts to provide AV synchronous pacing using a ventricular leadless pacemaker that senses atrial contraction to provide VDD pacing. Multicomponent pacemaker systems are of a different design and are not commercially available. Rather than relying on a single unit containing all electrical components of the device, a smaller endocardial ‘seed’ is used and functions as an energy transducer. A second, extrathoracic device communicates with the endocardial component using energy to induce a pacing pulse. This design could allow for dual-chamber pacing as well as cardiac resynchronisation therapy. In addition, a system that
A: The fluoroscopic image shows an octapolar His catheter (black, dashed arrow) that was placed via the femoral vein as a fluoroscopic marker. The delivery sheath (C315HIS, Medtronic) was been positioned at the atrioventricular septum and the tip of the permanent His bundle pacing lead (3830 SelectSecure™, Medtronic) is exposed to obtain unipolar recordings on the pacing system analyser (PSA) or on the electrophysiology lab recording system. Three distinct signals are seen: atrial (A), His bundle electrogram (H) and a ventricular electrogram (V). B: Once a satisfactory location has been found, the lead is manually rotated into the septal myocardium and the sheath is pulled back to expose the ring electrode (red circle). Bipolar recordings on the PSA are shown with an unusually distinct A, H and V signal demonstrated. The octapolar His catheter (black, dashed arrow) has moved from its previous position at the His bundle.
integrates a subcutaneous ICD (S-ICD) as the extrathoracic component would allow for bradycardia pacing and anti-tachycardia pacing.
Permanent His Bundle Pacing Chronic RV apical pacing is non-physiological and has been associated with an increased risk of heart failure, AF and death. This has prompted a search for alternative sites for pacing including the high septum, right ventricular outflow tract (RVOT), moderator band and His bundle.Capture of the His bundle allows rapid, efficient activation of the ventricles by using the Purkinje network. While the concept of His bundle pacing has been around since the 1960s, there was limited clinical experience with it as a pacing technique until a small study by Pramod Deshmukh in 2000.11,12 At that time, there were no specific tools to help accomplish the task and PHBP has only recently become widespread. A small-calibre, lumenless pacing lead (Medtronic’s 3830 SelectSecure™) is delivered via a specially designed sheath (C315HIS, Medtronic) to map the AV septum. Unipolar recording and pacing can be used to locate a His bundle electrogram near the membranous septum (Figure 5). When an appropriate location is found, the lead is manually rotated to fix it to the myocardium in a location where the cardiac conduction system can be captured. If His bundle capture is confirmed at this location and pacing thresholds are acceptable, the delivery sheath is slit and final capture thresholds are measured. A number of different QRS morphologies can be seen with His bundle pacing because the tissues that can be captured in this area include the His bundle, RV septum and right atrium. Standardised nomenclature
ARRHYTHMIA & ELECTROPHYSIOLOGY REVIEW
Update in Cardiac Pacing have been developed to describe the various electrocardiographic findings including selective His bundle capture (His bundle capture alone; Figure 6A) and non-selective His bundle capture (His bundle capture along with RV septal capture; Figure 6B). Data have shown that the implant success rate for PHBP ranges from 70â€“90%.13,14 Lead thresholds tend to be higher than traditional RV apical leads; however, the thresholds appear to remain stable over time. PHBP was found to have a statistically significant reduction in a combined endpoint of hospitalisation for heart failure, death or upgrade to biventricular pacing when compared with patients undergoing traditional RV apical pacing (HR 0.71; p<0.02). This effect was most pronounced when the RV pacing burden is more than 20% and was largely driven by a reduction in heart failure events.13 This technique has also been shown to be effective in instances of complete AV block and in patients with right and left bundle branch block (Figure 7). This reversal of conduction disease is generally explained by the concept of longitudinal dissociation in the His bundle. However, other explanations include differential source-sink relationships during pacing versus intrinsic impulse propagation, virtual electrode polarisation and local capture of conduction tissue fibres that connect downstream from the site of bundle branch block.15,16 The ability to reverse left bundle branch block (LBBB) and normalise the QRS has led to an interest in using PHBP for CRT.17 Additional data will be needed from upcoming trials to compare these two methods of resynchronisation. The recently published His Bundle Pacing Versus Coronary Sinus Pacing for Cardiac Resynchronization Therapy (HisSYNC) trial was the first multi-centre, prospective trial comparing PHBP with coronary sinus (CS) lead implantation for CRT candidates (NCT02700425).18 There was no difference in outcomes seen between the PHBP group and traditional CS group in this pilot study; however, there was a high crossover rate between the two groups. A method involving transseptal, direct left bundle stimulation from the RV has achieved QRS normalisation, even in patients where the LBBB cannot be corrected with PHBP.19
Closed Loop Stimulation In an effort to improve upon the traditional rate-drop response algorithm and create a more physiologic response to the need for pacing, closed loop stimulation has been developed by Biotronik. The algorithm measures RV impedance, a surrogate for cardiac contractility, and uses this information to adjust the pacing rate before a sudden drop in heart rate. There has been some success with the treatment of vasovagal syncope with this algorithm although a large, multicentre, randomised clinical trial, Benefit of Dual Chamber Pacing with Closed Loop Stimulation (CLS) in Tilt-induced Cardioinhibitory Reflex Syncope (BIOSync CLS) study (NCT02324920), is still ongoing.20
Cardiac Resynchronisation Therapy Ventricular cardiac dyssynchrony arises when segments of the left ventricle (LV) contract late due to delayed electrical activation. Most commonly, this occurs in the lateral wall of the LV due to LBBB. CRT uses the concept of targeted electrical stimulation to treat a delayed segment of the ventricle in order to improve electrical and mechanical synchrony and efficiency. This is accomplished through near simultaneous activation of an RV apical lead and an epicardial LV lead placed via the CS. CRT is not always effective and up to 30% of patients do not respond to this therapy. However, efforts to maximise the response from CRT include appropriate patient selection as
ARRHYTHMIA & ELECTROPHYSIOLOGY REVIEW
Figure 6A: Selective His Bundle Capture
VI II V5 An example of selective His bundle capture is demonstrated on a 12-lead ECG after permanent His bundle pacemaker implantation. The lead is exclusively capturing His bundle tissue without any involvement of the right ventricular septum. This mimics native conduction and leads to a reproduction of the intrinsic, narrow QRS. The pacing stimuli are highlighted with red arrows. Close inspection reveals an isoelectric segment between the pacing stimulus and the subsequent QRS, replicating the isoelectric HV delay seen during normal cardiac conduction. This delay must be considered when programming the atrioventricular delay.
Figure 6B: Non-selective His Bundle Capture ID
13::5 13:5 54:34 43 1-Se 1 1-S -Se -S S Sep-201 p-20 p-20 2016 20 16 13:54:34 1-Sep-2016
I II IIII III aV a VB VB aVB aVL aV a VL V L
F VF V aV a aVF VI VI V2 V2 V3 V3 V4 V4 V5 V5 V6 V6
An example of non-selective His bundle capture is demonstrated during threshold testing of a permanent His bundle pacing system. At high outputs (left of the tracing), the pacemaker lead captures both the His bundle tissue as well as the right ventricular (RV) septum, leading to a pre-excited QRS morphology. As the output is reduced, a clear change in QRS morphology is evident (red boxes) when there is loss of His bundle capture and only RV septal capture remains. The RV septal capture threshold is lower than the His bundle threshold in these cases.
outlined by existing clinical trial data, CS lead placement in an optimal location for resynchronisation and delivery of CRT therapy with every cardiac cycle.
Multisite and Multipoint Pacing A significant recent development in the field of CRT was the creation of the quadripolar CS lead. Before this, unipolar and bipolar leads for CRT were hampered by limited programmability, resulting in high pacing thresholds or phrenic nerve capture with even minor changes in lead position. Quadripolar leads were more forgiving, offering multiple options for pacing vectors allowing for more targeted cardiac resynchronisation. These leads were found have a lower risk of lead replacement and abandonment and appear to improve mortality when
Drugs and Devices Figure 7: Correction of Left Bundle Branch Block with Permanent His Bundle Pacing
placement relies on the existing coronary venous anatomy, which may be unsuitable for optimal lead placement.
Endocardial LV lead placement has been explored as an alternative to allow for more targeted LV lead placement with a more physiologic ventricular activation and no risk of phrenic nerve capture. This has been accomplished by using conventional pacing leads placed through the interatrial or interventricular septum, as well as a leadless system that involves retrograde aortic implantation of a wireless, endocardial pacing electrode.27–29 Despite the attractive features of this technique, even those studies that have shown some indication of clinical benefit have shown a high rate of adverse events, including systemic thromboembolism and procedural complications.29,30 Additional improvements in the delivery systems, increased familiarity with anticoagulation requirements for left-sided lead implantation and further studies will hopefully help to reduce complication rates and improve outcomes with this technique.
A: This is the 12-lead ECG of a patient who presented with marked exertional dyspnoea. It shows 2:1 AV block with a left bundle branch block. B: The pacing system analyser recordings from a permanent His bundle lead is shown demonstrating that the mechanism of atrioventricular block is infra-Hisian (red arrow). In addition, the preceding two conducted beats show infra-Hisian Wenckebach (red double arrows), a marker of significant infraHisian disease. C: Despite the presence of significant conduction abnormalities at baseline, permanent His bundle pacing results in significant narrowing of the QRS complex and normalisation of the QRS axis. This ability of permanent His bundle pacing to normalise QRS complexes, even in the setting of severe conduction disease, has led to the exploration of permanent His bundle pacing as an alternative method to achieve cardiac resynchronisation.
compared with bipolar leads.21 A natural extension of these benefits is the concept that pacing from more than one LV site may allow for improved resynchronisation and outcomes. Multisite pacing refers to the concept of using two or more epicardial CS leads to improve the resynchronisation response. Implantation of two or more CS leads has been shown to be feasible and safe. Small studies have shown benefits of this technique in haemodynamic response, ejection fraction, LV end systolic volume and heart failure symptoms.22 However, the use of a Y-adaptor to pace between the two leads can result in the use of high outputs and early battery depletion.23 Additional studies are needed for effectiveness of this technique as the results have been mixed.24 Multipoint pacing refers to pacing from multiple LV sites through a single quadripolar lead. By choosing widely spaced electrodes, a large region of LV myocardium can be captured to provide the best haemodynamic response. This option appears safe and feasible and small studies have shown haemodynamic advantages to multipoint pacing.25 However, a recently published study has shown no initial benefit with multipoint pacing in patients who are CRT non-responders.26 Additional phase II data from this study and other ongoing studies will be needed to assess whether there is a significant advantage to this technique.
Endocardial LV pacing Epicardial CS lead placement, while successful, is hampered by multiple mechanisms of non-response. Fundamentally, it is a nonphysiologic form of pacing as cardiac electrical activation normally proceeds from the endocardium to the epicardium. In addition, CS lead
Future developments for pacemakers will see a continued reduction in hardware. In current pacemaker systems, battery depletion and subsequent generator changes present additional risk of complications, especially infection. Preliminary work has been done to look at using flexible sheets of piezoelectric wires to convert cardiac motion to energy to power pacemaker devices in a nearly inexhaustible manner.31 There have also been efforts at creating biologic pacemakers using gene therapy to increase automaticity of existing non-pacemaker cardiac myocytes.32,33 This research is still in its early stages.
Conclusion Pacemakers were initially developed in an effort to prevent catastrophic, bradycardic events. These simple devices have evolved into modern systems with significant complexity and a high degree of programmability. However, the overall design of pacemaker systems has not significantly changed in more than 50 years, until recently. The advent of leadless pacemaker systems and the adoption of permanent His bundle pacing represent paradigm shifts in the field of cardiac pacing, and have brought a renewed sense of excitement and interest to the field of bradycardia pacing. These efforts and future innovations are likely to continue to focus on advancements that allow for further reductions in hardware and improvements in cardiac efficiency with a goal to reduce complications and improve clinical outcomes.
Clinical Perspective • Cardiac pacing design had remained largely unchanged for more than 50 years. • Leadless pacemakers represent a paradigm shift in the design of pacemaker systems, allowing for a significant reduction in hardware and potential reduction in complications. • Permanent His bundle pacing represents a fundamental shift in the approach to cardiac pacing, focusing on physiological stimulation of the cardiac muscle to improve cardiac efficiency. • Multisite and multipoint pacing represent efforts in the field of cardiac resynchronisation therapy to improve cardiac efficiency and improve clinical outcomes.
ARRHYTHMIA & ELECTROPHYSIOLOGY REVIEW
Update in Cardiac Pacing 1.
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PMID: 10694526. 13. A bdelrahman M, Subzposh FA, Beer D, et al. Clinical outcomes of His bundle pacing compared to right ventricular pacing. J Am Coll Cardiol 2018;71:2319–30. https://doi. org/10.1016/j.jacc.2018.02.048; PMID: 29535066. 14. Bhatt AG, Musat DL, Milstein N, et al. The efficacy of His bundle pacing: lessons learned from implementation for the first time at an experienced electrophysiology center. JACC Clin Electrophysiol 2018;4:1397–406. https://doi.org.10.1016/j. jacep.2018.07.013; PMID: 30466843. 15. Teng AE, Massoud L, Ajijola OA. Physiological mechanisms of QRS narrowing in bundle branch block patients undergoing permanent His bundle pacing. J Electrocardiol 2016;49:644–8. https://doi.org/10.1016/j.jelectrocard.2016.07.013; PMID: 27485351. 16. Verma N, Knight BP. Permanent His-Bundle pacing in a patient with advanced conduction system disease: What is the mechanism of QRS narrowing? Clin Case Rep 2018;6:1236–40. https://doi.org/10.1002/ccr3.1569; PMID: 29988671. 17. Ali N, Keene D, Arnold A, et al. His bundle pacing: a new frontier in the treatment of heart failure. Arrhythm Electrophysiol Rev 2018;7:103–10. https://doi.org/10.15420/aer.2018.6.2; PMID: 29967682. 18. Huang W, Su L, Wu S, et al. A novel pacing strategy with low and stable output: pacing the left bundle branch immediately beyond the conduction block. Can J Cardiol 2017;33:1736. https://doi.org/10.1016/j.cjca.2017.09.013; PMID: 29173611. 19. Upadhyay GA, Vijayaraman P, Nayak HM, et al.; His-SYNC Investigators. His corrective pacing or biventricular pacing for cardiac resynchronization in heart failure. J Am Coll Cardiol 2019 https://doi.org/10.1016/j.jacc.2019.04.026; PMID: 31078637; epub ahead of press. 20. Gopinathannair R, Salgado BC, Olshansky B. Pacing for vasovagal syncope. Arrhythm Electrophysiol Rev 2018;7:95–102. https://doi.org/10.15420/aer.2018.22.2; PMID: 29967681. 21. Turakhia MP, Cao M, Fischer A, et al. Reduced mortality associated with quadripolar compared to bipolar left ventricular leads in cardiac resynchronization therapy. JACC Clin Electrophysiol 2016;2:426–33. https://doi.org/10.1016/j. jacep.2016.02.007; PMID: 29759861. 22. Leclercq C, Gadler F, Kranig W, et al. A randomized comparison of triple-site versus dual-site ventricular stimulation in patients with congestive heart failure. J Am Coll Cardiol 2008;51:1455–62. https://doi.org/10.1016/j. jacc.2007.11.074; PMID: 18402900. 23. Behar JM, Bostock J, Ginks M, et al. Limitations of chronic delivery of multi-vein left ventricular stimulation for cardiac resynchronization therapy. J Interv Card Electrophysiol 2015;42:135-42. https://doi.org/10.1007/s10840-014-9971-2; PMID: 25627144.
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