Vertebral Columns Summer 2023

Use of Artificial Intelligence and Machine Learning in Spine Surgery Current State of Artificial Intelligence in Spine Imaging

Vertebral

COLUMNS International Society for the Advancement of Spine Surgery

Lumbar Bone Density Measurements: Using CT and MRI as Alternatives to DEXA Lumbar Muscle Health: Importance in Spine Surgery Annular Repair The Rise of Sacroiliac Joint Fusions Informed Consent in Spine Surgery

PLUS

Advancements in Biomaterials and Implications For Spine Surgery

SUMMER 2023

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EDITORIAL Advancements in Biomaterials and Implications For Spine Surgery

NEW TECHNOLOGY Use of Artificial Intelligence and Machine Learning in Spine Surgery

Editor in Chief Kern Singh, MD Editorial Board

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NEW TECHNOLOGY Current State of Artificial Intelligence in Spine Imaging

Peter Derman, MD, MBA Brandon Hirsch, MD Sravisht Iyer, MD

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BONE QUALITY Lumbar Bone Density Measurements: Using CT and MRI as Alternatives to DEXA

PATIENT FITNESS Lumbar Muscle Health: Importance in Spine Surgery

Yu-Po Lee, MD Sheeraz Qureshi, MD, MBA Managing Editor Audrey Lusher Designer CavedwellerStudio.com

PATIENT OUTCOMES Annular Repair

THE SI JOINT The Rise of Sacroiliac Joint Fusions

LEGAL MATTERS Informed Consent in Spine Surgery

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Vertebral Columns is published quarterly by the International Society for the Advancement of Spine Surgery. ©2023 ISASS. All rights reserved.

Opinions of authors and editors do not necessarily reflect positions taken by the Society.

This publication is available digitally at www.isass.org/news/vertebralcolumns-Summer-2023

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Summer 2023 Vertebral Columns

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From the Chicago Medical School at Rosalind Franklin University of Medicine and Science in North Chicago, Illinois (Mr Kaul and Mr. Wolf), and Rush University Medical Center in Chicago, Illinois (Mss Roca, Anwar, Loya, Medakkar and Drs Federico, Massel, Sayari, Lopez, and Singh).

EDITORIAL

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Aayush Kaul, BS

Advancements in Biomaterials and Implications For Spine Surgery During the 19th century, Dutch surgeon Job Van Merren reported the first autologous graft success with subsequent reports of allogeneic grafts, which are still in use today.1 In the modern era, more than 2 million bone graft procedures are reported annually worldwide.2 Autogenic harvesting from the iliac bone remains the gold standard, with allogeneic grafts being accepted as an alternative. Despite its popularit y, autologous bone grafts still pose considerable problems, such as a lack of neovascularization and the requirement of multiple surgical sites during harvesting procedures, thus increasing risk of infection.1 These limitations stem from challenges in creating biocompatible materials that successfully substitute human bone in vivo. There is a current need for osteogenic, osteoconductive, and osteoinductive methods that

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are equally effective compared to established standards. 2,3 The progression of biomater ia ls was ma rked by cha nges i n use f rom sta i n less steel to lighter materials such as t itan iu m. 4 Newer mater ia ls have been associated with improved postoperative patient-reported outcome measures (PROMs) such as visual analog scale (VAS) pain scores and Oswestry Disability Index (ODI) scores, among others.

Presently, polymers with higher levels of biocompat ibi lit y a re used because of improved longterm patient outcomes. Looking for ward, technologies such as 3-dimensional (3D) printing will enhance the current use of biomaterials by allowing personalized medicine at lower costs. The goal of this article is to review advancements in biomaterials used in spine surgery and discuss their implications.

Jacob C. Wolf, BS

Andrea M. Roca, MS

Fatima N. Anwar, BA

Alexandra C. Loya, BS

Srinath S. Medakkar, BS

Vincent P. Federico, MD

Dustin H. Massel, MD

Arash J. Sayari, MD

Gregory D. Lopez, MD

Kern Singh, MD

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Stainless Steel/Titanium Stainless steel is an affordable material with a high mechanical strength that is able to withstand biomechanical forces. Originally one of the first biomaterials established in orthopedic surgery, stainless steel is still utilized in medical device implants today.

However, associated nickel and chromium toxicity caused the industry to shift toward other metal compounds and alloys. 5

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The emergence of titanium marked the new age of metals in spine surger y and quick ly gained popularit y for its low weight, resistance to corrosion, and ability to positively interact with bones. Research has demonstrated that titanium in saline solution was capable of forming a protective coating of TiO2, which further enhances its use. It is now being approached as a mater ia l for protec t ive coating. 5 A study conducted by Hasegawa et al compared polyetheretherketone (PEEK) cages with titanium-coated PEEK (TiPEEK) cages in posterior lumbar interbody fusion (PLIF) surgery and found that the TiPEEK cages had significantly better fusion rates at 6 months.6 This study’s findings suggest that adding titanium can significantly decrease postoperative recovery times and allow faster return to work . W h i le t h is may be exciting, titanium is limited in use due to its high costs. 5 Future research should explore additional benefits with the use of titanium in addition to decreased toxicity and enhanced fusion rates to provide patients with adequate expectations following spine surgeries.

Calcium Sulfate Used as early as 1892, calcium sulfate, also known as “gypsum” or “plaster of Paris,” acts

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EDITORIAL

as a synthetic bone substitute. It offers many advantages due to its bone-like structure, affordability, and availability in different forms, such as pellets and injectable fluids.

It is also non-allergenic and promotes bone capillary infiltration, enhancing osteoconductivity.1 A study by Hoffman et al found that injectable calcium sulfate cement was noninferior to autologous bone graft when repairing tibial fractures.7 Furthermore, there were no statistically significant differences seen in scores on the 12-item Short Form physical component summary (PCS) or the VAS between the autologous and calcium sulfate cement groups, and the calcium sulfate cement group showed significantly less blood loss. This study serves as a significant indication of calcium sulfate’s role as a synthetic bone replacement.

Calcium sulfate can also be used in bead forms for the treatment of spondylodiscitis.

Due to its flexibility, it can be used to carry antibiotics to help treat osteomyelitis of the spine and reduce infection rates that could result from the associated surgery. 8 Future research should focus on improving the osteoconductivity of the material. Because this material is already cheaper than autologous grafting, improving its osteoconductivity would allow it to become the new gold standard and would increase the accessibility of associated surgeries and lead to further improved postoperative PROMs.

Calcium Phosphate Because of the morbidity associated with autologous bone grafting, calcium phosphate modalities have remained a popular syn-

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The latest progress in the field of spine surgery is 3D printing, which has paved the way for groundbreaking possibilities in biomaterials…. Lador et al demonstrated that patients with custom 3D-printed implants achieved favorable stability. The need for future studies comparing outcomes of 3D-printed implants to established implants grows as the utilization of 3D technology extends across various spinal procedures that are impacting clinical practice in new ways.

thetic alternative to established methods.9 Hydroxyapatite (HA) stood out due to its osteoconductive properties, as its crystallographic scaffold provided structural support similar to that of bone while being porous enough to facilitate neovascularization.10,11 Yoshii et al demonstrated the effectiveness of HA in their study comparing outcomes of patients who underwent anterior cervical corpectomy and fusion with HA treatment versus autologous grafting.12 The study showed similar effectiveness between groups, with the autologous group showing more blood loss and a higher incidence of donor site pain. Another calcium phosphate alternative is β-tricalcium phosphate (TCP), which has gained a considerable reputation for being both osteoconductive and osteoinductive.13

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Established literature has highlighted both the benefits and weaknesses of its surgical use. A study by Thaler et al demonstrated favorable clinical outcomes with TCP use in posterior lumbar interbody fusion; however, an increased incidence of pseudoarthrosis was noted.14 Multiple studies have reported the benefit of using HA and TCP in a composite material termed biphasic calcium phosphate (BCP). Prior research has shown this composite material outperforms both HA and TCP used in isolation; it also promotes TCP degradation, thus mitigating its adverse effects.15-17

Future Directions: 3D Printing The latest progress in the field of spine surgery is 3D printing, which has paved the way for groundbreaking possibilities for biomaterials.18 In addition to personalized pedicle screw guides, Sheha et al explored the creation of patient-specific 3D-printed vertebrae with the potential to replicate the complex anatomy of the spine.19 Zhu et al demonstrated that 3D-printed vertebrae were equivalent in strength to natural vertebral discs with acceptable cytocompatibility in rat studies. 20 The discs maintained statistically comparable heights at 2, 3, and 6 months postoperatively with favorable proteoglycan and collagen deposition in the scaffold.

Experimental studies have also highlighted the possibility of 3D-printing technology as a solution to common problems in bonegraft surgery. Plantz et al explored the ability to use a 3D-printed HA-demineralized mone matrix (DBM) to reduce host inflammatory

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responses.21 Rats undergoing lumbar fusion in a HA-DBM group demonstrated significantly less edema postoperatively than rats in a bone morphogenic protein (rhBMP-2) control group. 21 Interested in optimizing production, Lai et al demonstrated the benefits of using magnesium in combination with a porous poly lactide-co-glycolide (PLGA) and TCP biodegradable 3D-printed implant in rats, showing favorable osteogenesis and angiogenesis with reduced inf lammatory reactions. 22 The involvement of 3D printing is in its nascent stages of transitioning from animal trials to human clinical cases. Lador et al reviewed several case studies in which 3D-printed patient-specific implants were used in complex spine surgeries, all being oncology cases. 23 The patients with custom implants achieved favorable stability, and the study calls for future studies comparing outcomes of 3D-printing implants to other established implants. As this ongoing transition persists, the utilization of 3D printing looks to extend its breadth to various spinal procedures in the realm of clinical practice.

Conclusion The field of spine surgery has developed significant advancements in the use of biomaterials, specifically in bone grafting. Since the successes of autologous and allogeneic grafts, advancements in biomaterials have improved patient outcomes and reduced associated risks. Limitations in gold standard autologous grafts, such as lack of vascularization and the need for multiple donor sites, have spurred interest in alternative options. The emergence of biomaterials with

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higher biocompatibility has provided novel avenues for applications in spine surgery that will continue. 3D-printing technology presents an opportunity for personalized implants, better patient outcomes, and

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fewer complications. With significant advancements in biomaterials thus far and a promising potential for expansion, there is a bright future for the involvement of novel biomaterials in spine surgery. l

References 1. Fernandez de Grado G, Keller L, Idoux-Gillet Y, et al. Bone substitutes: a review of their characteristics, clinical use, and perspectives for large bone defects management. J Tissue Eng. 2018;9:2041731418776819.

2. Gillman CE, Jayasuriya AC. FDA-approved bone grafts and bone graft substitute devices in bone regeneration. Mater Sci Eng C Mater Biol Appl. 2021;130:112466.

3. Lementowski PW, Lucas P, Taddonio RF. Acute and chronic complications of intracortical iliac crest bone grafting versus the traditional corticocancellous technique for spinal fusion surgery.

Orthopedics. 2010;33(4):240-247.

4. Hofmann A, Gorbulev S, Guehring T, et al; CERTiFy Study Group. Autologous iliac bone graft compared with biphasic hydroxyapatite and calcium sulfate cement for the treatment of bone defects in tibial plateau fractures: a prospective, randomized, open-label, multicenter study. J Bone Joint Surg Am. 2020;102(3):179-193.

5. Choi SR, Kwon JW, Suk KS, et al.

The clinical use of osteobiologic and metallic biomaterials in orthopedic surgery: the present and the future.

Materials (Basel). 2023;16(10):3633.

6. Hasegawa T, Ushirozako H, Shigeto E, et al. The titanium-coated PEEK cage maintains better bone fusion with the endplate than the PEEK cage 6 months after PLIF surgery: a multicenter, prospective, randomized study. Spine (Phila Pa 1976). 2020;45(15):E892-E902.

7. Hofmann A, Gorbulev S, Guehring T, et al; CERTiFy Study Group. Autologous iliac bone graft compared with biphasic hydroxyapatite and calcium sulfate cement for the treatment of

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bone defects in tibial plateau fractures: a prospective, randomized, open-label, multicenter study. J Bone Joint Surg Am. 2020;102(3):179-193.

8. Tang X, Li J, Wang C, et al. Antibiotic-loaded calcium sulfate beads in spinal surgery for patients with spondylodiscitis: a clinical retrospective study. BMC Musculoskelet Disord. 2022;23(1):270.

9. Dimitriou R, Mataliotakis GI, Angoules AG, Kanakaris NK, Giannoudis PV. Complications following autologous bone graft harvesting from the iliac crest and using the RIA: a systematic review. Injury. 2011;42 Suppl 2:S3-S15.

10. Litak J, Czyzewski W, Szymoniuk M, et al. Hydroxyapatite use in spine surgery-molecular and clinical aspect.

Materials (Basel). 2022;15(8):2906.

11. Spivak JM, Hasharoni A. Use of hydroxyapatite in spine surgery. Eur Spine J. 2001;10(Suppl 2):S197-S204.

12. Yoshii T, Hirai T, Sakai K, et al.

Anterior cervical corpectomy and fusion using a synthetic hydroxyapatite graft for ossification of the posterior longitudinal ligament.

Orthopedics. 2017;40(2):e334-e339.

13. Bohner M, Santoni BLG, Döbelin N. β-tricalcium phosphate for bone substitution: synthesis and properties. Acta Biomater. 2020;113:23-41.

14. Thaler M, Lechner R, Gstöttner M, Kobel C, Bach C. The use of beta-tricalcium phosphate and bone marrow aspirate as a bone graft substitute in posterior lumbar interbody fusion.

Eur Spine J. 2013;22(5):1173-1182.

15. Toth JM, An HS, Lim TH, et al. Evaluation of porous biphasic calcium phosphate ceramics for anterior cervical in-

terbody fusion in a caprine model. Spine (Phila Pa 1976). 1995;20(20):2203-2210.

16. Ng AM, Tan KK, Phang MY, et al.

Differential osteogenic activity of osteoprogenitor cells on HA and TCP/HA scaffold of tissue engineered bone. J Biomed Mater Res A. 2008;85:301- 312.

17. Garrido CA, Lobo SE, Turíbio FM, Legeros RZ. Biphasic calcium phosphate bioceramics for orthopaedic reconstructions: clinical outcomes.

Int J Biomater. 2011;2011:129727.

18. Lo WC, Tsai LW, Yang YS, Chan RWY. Understanding the future prospects of synergizing minimally invasive transforaminal lumbar interbody fusion surgery with ceramics and regenerative cellular therapies.

Int J Mol Sci. 2021;22(7):3638.

19. Sheha ED, Gandhi SD, Colman MW. 3D printing in spine surgery. Ann Transl Med. 2019;7(Suppl 5):S164.

20. Zhu M, Tan J, Liu L, et al. Construction of biomimetic artificial intervertebral disc scaffold via 3D printing and electrospinning. Mater Sci Eng C Mater Biol Appl. 2021;128:112310.

21 Plantz M, Lyons J, Yamaguchi JT, et al. Preclinical safety of a 3D-printed hydroxyapatite-demineralized bone matrix scaffold for spinal fusion. Spine (Phila Pa 1976). 2022;47(1):82-89.

22. Lai Y, Li Y, Cao H, et al. Osteogenic magnesium incorporated into PLGA/ TCP porous scaffold by 3D printing for repairing challenging bone defect.

Biomaterials. 2019;197:207-219.

23. Lador R, Regev G, Salame K, Khashan M, Lidar Z. Use of 3-dimensional printing technology in complex spine surgeries. World Neurosurg. 2020;133:e327-e341.

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Yu-Po Lee, MD

NEW TECHNOLOGY

From UCI Health in Orange County, California

Use of Artificial Intelligence and Machine Learning in Spine Surgery The rapid development of artificial intelligence (AI) is reaching all aspects of our world. Artificial intelligence is being used to make our web searches more focused, make the ads that we see more personalized, and even assist us in the workplace.

Artificial intelligence has already made inroads into spine surgery. Consider that the basis of science is to generate data, and the practice of medicine is to synthesize the vast amount of data produced in bench research, animal studies, and clinical trials into a well-organized plan to treat patients.

One area in which AI can assist in spine surger y is machine learning, a process in which a computer program evaluates datasets to identif y relationships in the data and then assist in surgical planning.

Spinal Deformity Spinal deformity is a field of spine surgery in which a significant amount of research has been done. There are certain parameters that can improve surgical outcomes in spinal deformity surgery. For example, studies have shown that obtaining sagittal balance can lead to improved outcomes.1-3 Surgical techniques that can help achieve sagittal balance include the use of Smith-Peterson osteotomies and pedicle subtraction osteotomies.1-3 The selection

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of levels to fuse can also determine sagittal balance.1-3 Data are currently being gat hered compa r i ng preoperat ive a nd postoperative radiographs and correlating them with outcomes. Based on a growing database of scoliosis cases, programs now exist that can help generate a surgical plan that can give surgeons and patients the best possible outcome. Such programs can analyze a vast amount of data that would be too tedious and repetitive for humans.

One unfortunate aspect of medicine is that physicians work their entire careers to accrue knowledge, but when physicians retire, that knowledge often is not passed on. The benefit of machine learning programs is that this knowledge can be stored in t he programs, and by ingesting t hat information, the programs will continue to improve and be able to assist surgeons well on into the future, such as helping surgeons synthesize a plan for complex scoliosis cases.

As a case example, R.O. is a 65-year-old man with a history of congenital scoliosis.

Over the years, the scoliosis has worsened, a nd he wou ld li ke to consider su rger y (Figure 1). The planning for surgery in this case would be very challenging. However, the preoperative films were sent to one of the programs, which sent back a surgical

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plan (Figure 2). Currently, the program is only able to give recommendations in the sagittal plane, but in time, the program’s capabilities will expand and become more refined with each new case that is added.

Disc Degeneration and Stenosis Another area of spine surgery in which a computer program may be able to assist surgeons is in the selection patients with

Figure 1. Lateral imaging of a 65-year-old man with a history of congenital scoliosis prior to surgical intervention.

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Figure 2. Preoperative surgical plan created using artificial intelligence/machine learning.

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lumbar disc degeneration and stenosis for surgery. The surgical indications in these cases are commonly recognized to be worsening radicular pain, loss of sensation or motor weakness, and loss of bowel or bladder control. While surgeons try to adhere to these principles, surgical results still remain highly variable.4-6 Many patients do well after surgery, but some patients are not satisfied with the surgery they had. This can be due to many reasons, such as patients experiencing instability after decompression4-6 or not having adequate decompression.

Spine surgeons of ten rely on clinica l judgment and experience, in addition to scientific studies, to determine which individuals are good candidates for surgery and which surgical procedure is most appropriate. Nevertheless, the field of medicine is still an art and not entirely a science at this point. As long as there is some intuition involved in surgical decision-making, t here w ill be variabilit y in patient selection methods and surgical technique ut ilizat ion. For example, a 65-year-old

woman diagnosed with lumbar stenosis and a grade 1 spondylolisthesis at L4-5 will most likely have a decompression and fusion at L4-5. In contrast, an 85-year-old woman with lumbar stenosis and a grade 1 spondylolisthesis may have many different treatment recommendations because of her age and possible medical comorbidities.

In situations like this, having a program t hat could predict outcomes and assist spine surgeons in decision-making would be a huge benefit to surgeons and would improve patient satisfaction.

Conclusion New technology improves the field of medicine. The study of human anatomy made surgery possible. The discovery of penicillin and antibiotics made surger y safer. The discover y of radiographs and advanced imaging made surgery more precise. The use of instrumentation made corrections more powerful. It w ill be interesting to see what gains spine surgery will see from artificial intelligence. l

References 1. de Kleuver M, Faraj SSA, Haanstra TM, et al; COSSCO study group. The Scoliosis Research Society adult spinal deformity standard outcome set.

Spine Deform. 2021;9(5):1211-1221.

2. Ilharreborde B. Sagittal balance and idiopathic scoliosis: does final sagittal alignment influence outcomes, degeneration rate or failure rate? Eur Spine J. 2018;27(Suppl 1):48-58.

3. Koller H, Pfanz C, Meier O, et al. Factors influencing radiographic and clinical

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outcomes in adult scoliosis surgery: a study of 448 European patients.

Eur Spine J. 2016;25(2):532-548.

4. Phillips FM, Slosar PJ, Youssef JA, Andersson G, Papatheofanis F. Lumbar spine fusion for chronic low back pain due to degenerative disc disease: a systematic review. Spine (Phila Pa 1976). 2013;38(7):E409-E422.

5. Furunes H, Storheim K, Brox JI, et al.

Total disc replacement versus multidisciplinary rehabilitation in patients

with chronic low back pain and degenerative discs: 8-year follow-up of a randomized controlled multicenter trial. Spine J. 2017;17(10):1480-1488.

6. Mannion AF, Brox JI, Fairbank JC. Comparison of spinal fusion and nonoperative treatment in patients with chronic low back pain: long-term follow-up of three randomized controlled trials. Spine J. 2013;13(11):1438-48.

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From the Hospital For Special Surgery in New York, New York.

NEW TECHNOLOGY

Current State of Artificial Intelligence in Spine Imaging Recent advances in artificial intelligence (AI) and machine learning (ML) such as ChatGPT have brought the power of these technologies to households across the world. This relatively new technology is incredibly powerful, and its applications seem endless. As it continues to mature and applications become more tangible, we will continue to see these technologies become increasingly ubiquitous within our daily lives. Thus, understanding the current state of AI is significant.

Here, we aim to understand the current state of AI in spine imaging specifically regarding training data, clinical use, and research applications.

AI and ML Models and Training Data Three training methods currently exist for ML algorithms: (1) supervised learning, (2) unsupervised learning, and (3) reinforced learning.1–3 Within the current literature of spine imaging, supervised learning models are the most used. Linear, labeled data are inputted into a model and output is validated by a human for model training. Unsupervised and reinforced learning allow the algorithm to find data relationships on its ow n and

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can handle nonlinear data, making them more powerful for developing increasingly advanced models such as artificial neural networks and convolutional neural networks (CNNs).

These neural networks imitate biological neuronal processing. CNNs in particular focus on image processing1 and as such, they hold great promise for potential applications in spine imaging. These models can handle complex and nonlinear data, require less intensive direct involvement, and can be optimized with smaller data quantities.

Fundamental to the field, AI and ML are only as good as the data and models they are trained on.4 However, building high quality datasets is a challenge. Poor data can suffer from confounding factors, poor image resolution, inconsistent imaging protocols and standards, lack of uniformit y, and demographic biases. As a result, even the most promising models may not confer clinical utility to users if pitfalls in data quality were not mitigated prior to training. Unfortunately, labeling data is time consuming and labor intensive. 5 For unlabeled data, gathering high quality data from specific pathologies for large databases

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Tejas Subramanian, BE

Chad Simon, BS

Marcel Dupont, BA

Joshua Zhang, BS

Tomoyuki Asada, MD

Sravisht Iyer, MD

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is oftentimes not feasible. Even with the currently established multicenter databases, concerns regarding multi-source data quality, sufficient data quantity, storage and transmission, anonymization, and patient rights do exist.1,3,5 It has been previously shown that AI can even be used to generate these data sets. These “bootstrapping” methods allow for AI to utilize image processing techniques to artificially multiply the training set.6 In the literature, there have been attempts to develop larger data sets. For example, Jamaluddin et al trained a CNN model to classify disc degeneration in 12,018 discs.7 In a systematic review, Langerhuizen et al described the current state of AI in fracture detection describing data sets of up to 256,000 patients.8 However, these data sets are not publicly available and it is difficult to reproduce and verify the data quality. Ultimately, a large, multi-center, standardized dataset

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would be an enormous asset to advancing AI in spine imaging. Hurdles in reaching this goal begin with ensuring consistent image acquisition and pretreatment procedures to balance image discrepancies. 5 If trained well, these models have enormous potential in clinical and research applications.

AI in Radiographic Analysis Much of how we understand, plan, and treat a patient’s pathology stems from imaging in the forms of radiography, computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI). Improving diagnostic accuracy and efficiency in interpreting these imaging findings using AI is another clinical avenue for which researchers are beginning to investigate. AI models have demonstrated efficacy in CT, PET, and MRI image enhancement while reducing imaging time. For example,

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given the long scan times, MRI is especially prone to significant motion artifact. Jiang et al have used AI to reduce noise and motion artifact in MR images. 9 AI has also been used to decrease Gibbs artifacts on MRIs caused by reconstitution of the finite acquisition fields. 5 Similar studies utilizing AI techniques to eliminate noise in CT and PET images have demonstrated promising results.10–12 The additional benefit of these image enhancing models is shorter scans with lower radiation dose. Longer scans improve spatial resolution; however, with the power of AI, images with poorer resolution can be improved with post-acquisition enhancement.13,14 Measurements w ithin the spine ser ve great importance both in clinical practice and research but are often incredibly labor intensive. Research into utilizing AI to aid in this process is still in its early stages, but preliminary results show promising utility. Zhou et al developed a CNN capable of measuring four lumbosacral anatomical parameters from lateral lumbar radiographs: lumbosacral lordosis angle, lumbosacral angle, sacral horizontal angle, and sacral inclination angle.15 Their model was found to be clinically equivalent to that of attending radiologists and even superior in terms of reliability and reproducibility. Cho et al calculated lumbar lordosis angles from lateral radiographs and found no differences between AI-generated and manual measurements.16 In other studies, utilizing CNNs, Watanabe et al, Sardjono et al, Galbusera et al, and Weber et al all demonstrated high precision and accuracy in measuring sagittal parameters such as

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T4-12 kyphosis, L1-5 lordosis, pelvic tilt, Cobb angles, and vertebral rotation as well as muscle health via muscle segmentation and sarcopenia.17–20 Kim et al’s CNN model was developed to generate segmented images of the spine from axial CT images. 21 Their model demonstrated a similarity coefficient, precision, and accuracy ratings all eclipsing 90%. Similarly, Bae et al developed a CNN capable of identifying superior and inferior vertebrae in a single CT slice of the cervical spine and performed postprocessing for separating and segmenting vertebrae in 3 dimensions (3D). 22 This method achieved comparable accuracy to manual segmentation by human experts. These sagittal and muscle health parameters that AI models are capable of measuring accurately are greatly important, and as such, this technology has the potential to enhance our understanding of spinal pathologies in research and in clinical practice for the treatment and follow-up of patients.

These AI analysis techniques have also demonstrated efficacy in both interpretation and diagnosis. In a systematic review of AI data in fracture detection, Langerhuizen et al found accuracies ranging from 77% to 90%, which is comparable to orthopedic readers.8 Kim et al trained a model to evaluate spine MR images differentiating between pyogenic spondylitis and tuberculous spondylitis.

In comparison with 3 radiologists, their AI model was more accurate in interpreting the imaging. Lewandrowski et al recently trained a CNN model to detect spinal pathologies on axial MRI.23 Preliminary results utilizing their model demonstrated 86% ac-

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curacy in detecting foraminal stenosis and 85% accuracy in detecting disc herniation.

Wang et al similarly demonstrated accuracy of 95% in detecting cervical spondylosis. 24 Other studies utilizing CNN models have demonstrated high sensitivity and specificity in detecting scoliosis, 25,26 spinal tumors, 27 multiple sclerosis,28 and osteoporosis.29 As AI becomes increasingly capable of detecting pathologies on radiographic imaging, its use in clinical practice may improve accuracy, reduce medical errors, and streamline the process of radiographic data acquisition and analysis. 3

AI in the Operating Room AI-powered imaging technologies have gradually been introduced to the operating room. Previous studies have harnessed AI technolog y for augmented reality for intraoperative use. 30 Navigation and robotic technologies in spine surgery are especially interesting avenues of AI disruption, as image processing is a clear strength of AI techniques. Computer-assisted navigation systems use CT imaging w ith reference clamps to produce 3D renderings of the spine in real-time. 31 AI-powered systems are beginning to be used in operating rooms across the country and have demonstrated better safety profiles, such as increased accuracy in pedicle screw fixation, compared to more traditional technologies. 32 However, these computer-assisted navigation systems and robotic technologies are still relativity new, and the use of AI in improving surgical safety has not been fully explored. Additionally, AI is being used for surgical planning. For

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example, Lafage et al automated vertebrae selection for ASD surgery. Utilizing preoperative imaging, their model was almost 90% accurate in identifying the upper treated vertebra. 33 Medicrea, a French startup, is also harnessing the power of AI in surgical planning. They developed a modeling tool that similarity utilizes preoperative imaging to create custom 3D-printed rods to eliminate the guesswork of manual manipulation.30 AI holds great potential in revolutionizing intraoperative imaging, planning, and treatment.

Future Perspectives AI and its applications within spine imaging, while relatively new, have been well studied.

Reports largely demonstrate its efficacy in assisting with radiographic analysis both preoperatively and intraoperatively. However, its actual adoption clinically has been slow.

The major obstacle is lack of trust in the technology. More advanced artificial neural networks are “black box” programs, meaning even the developers often do not understand why a specific output was generated from the given inputs. This also introduces ethical and legal concerns with its application clinically. While this is a difficult-to-understand notion, we believe the limitation is not with the technology itself; rather, it is with the data to which it has access to. Therefore, before AI can be trusted in applications that may directly impact patients, large-scale, high-quality data sets should be generated for model training. This next step is close, and as the AI field continues to mature, we will likely see its utility in spine imaging continue to rise. l

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References 1. Charles YP, Lamas V, Ntilikina Y. Artificial intelligence and treatment algorithms in spine surgery. Orthop Traumatol Surg Res. 2023;109(1S):103456.

2. Hornung AL, Hornung CM, Mallow GM, et al. Artificial intelligence in spine care: current applications and future utility.

Eur Spine J. 2022;31(8):2057-2081.

3. Huber FA, Guggenberger R. AI MSK clinical applications: spine imaging.

Skeletal Radiol. 2022;51(2):279-291.

4. Gutman MJ, Schroeder GD, Murphy H, Flanders AE, Vaccaro AR. Artificial intelligence in spine care. Clin Spine Surg. 2021;34(4):121-124.

5. Cui Y, Zhu J, Duan Z, Liao Z, Wang S, Liu W. Artificial intelligence in spinal imaging: current status and future directions. Int J Environ Res Public Health. 2022;19(18):11708.

6. Martín-Noguerol T, Oñate Miranda M, Amrhein TJ, et al. The role of artificial intelligence in the assessment of the spine and spinal cord. Eur J Radiol. 2023;161:110726.

7. Jamaludin A, Lootus M, Kadir T, et al.

ISSLS prize in bioengineering science 2017: automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist.

Eur Spine J. 2017;26(5):1374-1383.

J, Zaharchuk G. Ultra-low-dose PET reconstruction using generative adversarial network with feature matching and task-specific perceptual loss.

Med Phys. 2019;46(8):3555-3564.

13. Plenge E, Poot DHJ, Bernsen M, et al.

Super-resolution methods in MRI: can they improve the trade-off between resolution, signal-to-noise ratio, and acquisition time?

Magn Reson Med. 2012;68(6):1983-1993.

24. Wang S, Hu Y, Shen Y, Li H. Classification of diffusion tensor metrics for the diagnosis of a myelopathic cord using machine learning. Int J Neural Syst. 2018;28(2):1750036.

14. McCollough CH, Leng S. Use of artificial intelligence in computed tomography dose optimisation. Ann ICRP. 2020;49(1_suppl):113-125.

25. Jamaludin A, Fairbank J, Harding I, et al. Identifying scoliosis in population-based cohorts: automation of a validated method based on total body dual energy x-ray absorptiometry scans.

Calcif Tissue Int. 2020;106(4):378-385.

15. Zhou S, Yao H, Ma C, et al. Artificial intelligence x-ray measurement technology of anatomical parameters related to lumbosacral stability.

Eur J Radiol. 2022;146:110071.

16. Cho BH, Kaji D, Cheung ZB, et al. Automated measurement of lumbar lordosis on radiographs using machine learning and computer vision.

Global Spine J. 2020;10(5):611-618.

17. Watanabe K, Aoki Y, Matsumoto M. An application of artificial intelligence to diagnostic imaging of spine disease: estimating spinal alignment from moiré images. Neurospine. 2019;16(4):697-702.

18. Galbusera F, Niemeyer F, Wilke HJ, et al. Fully automated radiological analysis of spinal disorders and deformities: a deep learning approach.

Eur Spine J. 2019;28(5):951-960.

8. Langerhuizen DWG, Janssen SJ, Mallee WH, et al. What are the applications and limitations of artificial intelligence for fracture detection and classification in orthopaedic trauma imaging?

A systematic review. Clin Orthop Relat Res. 2019;477(11):2482-2491.

19. Weber KA, Smith AC, Wasielewski M, et al. Deep learning convolutional neural networks for the automatic quantification of muscle fat infiltration following whiplash injury. Sci Rep. 2019;9(1):7973.

9. Jiang D, Dou W, Vosters L, Xu X, Sun Y, Tan T. Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network.

Jpn J Radiol. 2018;36(9):566-574.

20. Sardjono TA, Wilkinson MHF, Veldhuizen AG, van Ooijen PMA, Purnama KE, Verkerke GJ. Automatic Cobb angle determination from radiographic images. Spine (Phila Pa 1976). 2013;38(20):E1256-1262.

10. Yang Q, Yan P, Zhang Y, et al. Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans Med Imaging. 2018;37(6):1348-1357.

21. Kim YJ, Ganbold B, Kim KG. Web-based spine segmentation using deep learning in computed tomography images.

Healthc Inform Res. 2020;26(1):61-67.

11. Higaki T, Nakamura Y, Zhou J, et al.

Deep learning reconstruction at CT: phantom study of the image characteristics. Acad Radiol. 2020;27(1):82-87.

12. Ouyang J, Chen KT, Gong E, Pauly

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23. LewandrowskI KU, Muraleedharan N, Eddy SA, et al. Feasibility of deep learning algorithms for reporting in routine spine magnetic resonance imaging. Int J Spine Surg. 2020;14(s3):S86-S97.

22. Bae HJ, Hyun H, Byeon Y, et al. Fully automated 3D segmentation and separation of multiple cervical vertebrae in CT images using a 2D convolutional neural network. Comput Methods Programs Biomed. 2020;184:105119.

26. Vergari C, Skalli W, Gajny L. A convolutional neural network to detect scoliosis treatment in radiographs. Int J Comput Assist Radiol Surg. 2020;15(6):1069-1074.

27. Wang J, Fang Z, Lang N, Yuan H, Su MY, Baldi P. A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks.

Comput Biol Med. 2017;84:137-146.

28. Wang SH, Tang C, Sun J, et al. Multiple sclerosis identification by 14-Llyer convolutional neural network with batch normalization, dropout, and stochastic pooling. Front Neurosci. 2018;12:818.

29. Muehlematter UJ, Mannil M, Becker AS, et al. Vertebral body insufficiency fractures: detection of vertebrae at risk on standard CT images using texture analysis and machine learning.

Eur Radiol. 2019;29(5):2207-2217.

30. Akosman I, Lovecchio F, Lyons K, et al.

The emerging role of artificial intelligence in adult spinal deformity. Semin Spine Surg. 2022;34(4):100986.

31. Rasouli JJ, Shao J, Neifert S, et al. Artificial intelligence and robotics in spine surgery. Global Spine J. 2021;11(4):556-564.

32. Luther N, Iorgulescu JB, Geannette C, et al.

Comparison of navigated versus non-navigated pedicle screw placement in 260 patients and 1434 screws: screw accuracy, screw size, and the complexity of surgery.

J Spinal Disord Tech. 2015;28(5):E298-303.

33. Lafage R, Ang B, Alshabab BS, et al. Predictive model for selection of upper treated vertebra using a machine learning approach. World Neurosurg. 2021;146:e225-e232.

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From the Texas Back Institute in Plano, Texas.

Lumbar Bone Density Measurements Emily C. Courtois, MS

Using CT and MRI Scans as Alternatives to DEXA

Evaluating and assessing vertebral bone quality prior to spinal surgery is important to increase the long-term success of instrumentation and limit the risk of complications. Subsidence, device Alexander M. Satin, MD migration, and pedicle screw pullout are among the possible complications of instrumentation in patients with poor bone quality.1-5 Dual-energy x-ray absorptiometry (DEXA) is a well-established tool for assessing bone mineral Mary P. RogersLaVanne, PhD density (BMD) and has served as the reference standard for classifying bone as normal, osteopenic, or osteoporotic.6,7 However, DEXA has shortcomings for spinal applications due to the inclusion of osteophytes and cortical bone in bone measurements, which may Peter B. Derman, MD, MBA lead to falsely elevated BMD results.8,9 In light of this, there has been recent interest in alternative methods to measure patients’ bone quality using existing imaging techniques: computed tomography (CT) or magnetic resonance imaging (MRI).3,5,10,11 Spinal surgery candidates almost universally undergo preoperative CT and/or MRI.

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As a result, use of these images to assess bone quality may eliminate the need for a DEXA, thus reducing radiation exposure to patients, cost of care, and patient inconvenience. Further research in various spine surgery populations is needed to determine how well CT or MRI correlate with either DEXA or bone quality outcomes like implant subsidence and screw pullout. Population composition, such as age or sex, may affect how well CT or MRI bone quality measurement methodologies perform.12,13 This is a growing area of research that may provide alternatives to DEXA for future spinal surgery screenings and may yield greater benefits to patients in reducing postoperative complications.

In the present article, we introduce recent methodologies that utilize CT and MRI o measure bone quality.

Measuring Bone Quality with CT Tools in the Picture Archiving and Communication System (PACS) or similar programs are used to evaluate bone quality using CT or MRI. Measuring Hounsfield units (HU) from CT scans can provide a direct assessment of bone quality.

CT scan measurements for bone quality are most often performed by first locating a midsagittal cut through the center of the vertebral body (Figure 1A). Then, on the axial

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view, a region of interest (ROI) is drawn on the cancellous bone of the vertebral body, avoiding cortical regions (Figure 1B).14 The measurements should avoid the vascular area of the venous plexus, which is just anterior to the thecal sac.15 A composite score is calculated by averaging each of the middle section measurements from each vertebral body from L1 to L4.

Measuring Bone Quality With MRI Bone quality in MRI is measured by calculating the vertebral bone quality (VBQ) score based on signal intensity (SI) of the vertebral bodies.16 T1-weighted scans are used to measure VBQ based on SI of the images.17 Using the midsagittal view of the lumbar spine, an ROI is drawn over the center of each vertebral body from L1 to L4 (Figure 2). In addition, an ROI is drawn over the thecal sac immediately posterior to the L3 vertebra to measure the SI of the cerebral spinal fluid (CSF).

Using the equation derived from Ehresman et al, the median of the vertebral measurements is divided by the CSF to yield an MRI lumbar composite value (Equation 1).18 VBQ score =

Figure 1. Example of how to measure bone quality on a computed tomography image.

(A) Using the sagittal view, the midvertebral body is located. (B) Using the axial view of the chosen vertebra, a region of interest is drawn in the middle of vertebral body consisting only of cancellous bone and avoiding the posterior vascular zone.

Figure 2.

Example of how to measure bone quality on a T1-weighted magnetic resonance image. Regions of interest are drawn in the midsagittal region of the vertebral bodies from L1 to L4.

A region of interest is drawn immediately posterior to the L3 vertebra to measure the cerebral spinal fluid.

Median SIL1–L4 SICSF

Alternate Measurement Methods A major challenge for current research is that the measurement methodologies are not yet completely standardized. Deliberation on the most efficient or effective methods for measuring bone quality from CT images or MRIs are seldom published. Table 1 displays the cutoff values for these imaging techniques based on current literature.

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However, some studies have reported on various aspects of methodology. For example, the comparison between using the sagittal vs axial view of the vertebral body to capture bone quality using CT has been compared in a few studies.12,19,20 These studies have concluded that the sagittal view was more efficient while maintaining the effectiveness of the measurements.

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Variations for calculating MRI VBQ have been tested; one study found that the VBQ’s ability to classify bone quality did not considerably change regardless of incorporating the CSF in Equation 1.12 In addition, using means verses medians in the MRI VBQ calculations was compared, and, regardless of method, the correlations did not appreciably change.12 Other proposed changes to MRI methodology have been published in the literature. Huang et al utilized a calculation similar to Equation 1; however, the numerator was simply the SI of the S1 vertebral body, which was measured using the midsagittal view.21 This equation yielded moderate to strong correlations with DEXA T-scores, with an accuracy of 82% in predictive ability. Other methods are still being explored and produced for both CT and MRI bone quality measurements.

In a study by Roch et al, the VBQ measurements were derived from three versions of MRI images (T1, T2, and Short Tau Inversion Recovery, or STIR) and combined to improve the accuracy of the bone quality assessment.22 While these results are intriguing, completing these imaging techniques, measuring each one, and combining them in a specific equation may be difficult to adopt for uses in the clinical setting.

Study population is an important factor to consider when evaluating CT or MRI bone quality assessments. As discussed by Courtois et al, who found a poor relationship between MRI and DEXA, the findings may not be as sensitive in younger patients undergoing treatment for painful disc degeneration as compared to older patients in whom bone quality may be more compromised.12 While this study population of younger patients with symptomatic disc degeneration had a moderate relationship between CT and DEXA, Kohan et al found a poor correlation between CT and DEXA in an older adult spinal deformity patient population.23 Research is still underway to understand which measurements are most appropriate for specific populations and applications.

Conclusion While DEXA remains the gold standard for evaluating BMD prior to spine surgery, there is increased interest in CT- and MRI-based methods. CT and MRI show potential for becoming platforms to estimate bone quality. However, results may vary depending on population composition, such as age or sex. More research should be encouraged to expand knowledge in this field of study. l

Table 1. Imaging techniques and their cutoff values for classification of bone quality.

Classification

DEXA 24

CT25

MRI26,27

Normal

> –1.0

>160

<2.41

Osteopenic

–2.5 to –1.0

100 to 160

2.41 to 3.0

Osteoporotic

< –2.5

100

>3.0

Note: DEXA is given in T-scores; CT is given in Hounsfield units; and MRI is given in VBQ score. There is not yet consensus in the literature regarding MRI VBQ cutoff values—these may vary by study.

Abbreviations: CT = computed tomography; DEXA = dual-energy x-ray absorptiometry; MRI = magnetic resonance imaging; VBQ = vertebral bone quality.

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References 1. Bertagnoli R, Zigler J, Karg A, Voigt S. Complications and strategies for revision surgery in total disc replacement. Orthop Clin North Am. 2005;36(3):389-395.

2. Ouyang H, Hu Y, Hu W, et al. Incidences, causes and risk factors of unplanned reoperations within 30 days of spine surgery: a single-center study based on 35,246 patients.

Spine J. 2022;22(11):1811-1819.

3. Jones C, Okano I, Arzani A, et al. The predictive value of a novel site-specific MRI-based bone quality assessment, endplate bone quality (EBQ), for severe cage subsidence among patients undergoing standalone lateral lumbar interbody fusion.

Spine J. 2022;22(11):1875-1883.

4. Soliman MAR, Aguirre AO, Kuo CC, et al.

Vertebral bone quality score independently predicts cage subsidence following transforaminal lumbar interbody fusion. Spine J. 2022;22(12):2017-2023 5. Sakai Y, Takenaka S, Matsuo Y, et al.

Hounsfield unit of screw trajectory as a predictor of pedicle screw loosening after single level lumbar interbody fusion. J Orthop Sci. 2018;23(5):734-738.

6. Link TM. Osteoporosis imaging: state of the art and advanced imaging.

Radiology. 2012;263(1):3-17.

7. Krugh M, Langaker MD. Dual-energy x-ray absorptiometry. StatPearls.

StatPearls Publishing LLC; 2023.

8. Wang Y, Videman T, Boyd SK, Battie MC. The distribution of bone mass in the lumbar vertebrae: are we measuring the right target?

Spine J. 2015;15(11):2412-2416.

9. Masud T, Langley S, Wiltshire P, Doyle DV, Spector TD. Effect of spinal osteophytosis on bone mineral density measurements in vertebral osteoporosis. BMJ. 1993;307(6897):172-173.

10. Kim AYE, Lyons K, Sarmiento M, Lafage V, Iyer S. MRI-based score for assessment of bone mineral density in operative spine patients.

Spine. 2023;48(2):107-112.

11. Ahmad A, Crawford CH, Glassman SD, Dimar JR, Gum JL, Carreon LY. Correlation between bone density measurements on CT or MRI versus

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DEXA scan: a systematic review. N Am Spine Soc J. 2023;14:100204.

12. Courtois EC, Ohnmeiss DD, Guyer RD. Assessing lumbar vertebral bone quality: a methodological evaluation of CT and MRI as alternatives to traditional DEXA [published online July 13, 2023]. Eur Spine J. doi:10.1007/s00586-023-07855-6 13. Aynaszyan S, Devia LG, Udoeyo IF, Badve SA, DelSole EM. Patient physiology influences the MRIbased vertebral bone quality score.

Spine J. 2022;22(11):1866-1874.

14. Choi MK, Kim SM, Lim JK. Diagnostic efficacy of Hounsfield units in spine CT for the assessment of real bone mineral density of degenerative spine: correlation study between T-scores determined by DEXA scan and Hounsfield units from CT. Acta Neurochir (Wien). 2016;158(7):1421-1427.

15. Brett AD, Brown JK. Quantitative computed tomography and opportunistic bone density screening by dual use of computed tomography scans.

J Orthop Translat. 2015;3(4):178-184.

16. Pennington Z, Ehresman J, Lubelski D, et al. Assessing underlying bone quality in spine surgery patients: a narrative review of dual-energy X-ray absorptiometry (DXA) and alternatives. Spine J. 2021;21(2):321-331.

17. Ehresman J, Pennington Z, Schilling A, et al. Novel MRI-based score for assessment of bone density in operative spine patients.

Spine J. 2020;20(4):556-562.

18. Ehresman J, Ahmed AK, Lubelski D, et al. Vertebral bone quality score and postoperative lumbar lordosis associated with need for reoperation after lumbar fusion. World Neurosurg. 2020;140:e247-e252.

ment of vertebral fractures and density.

Osteoporos Int. 2016;27(3):1131-1136.

21. Huang W, Gong Z, Wang H, et al. Use of MRI-based vertebral bone quality score (VBQ) of S1 body in bone mineral density assessment for patients with lumbar degenerative diseases.

Eur Spine J. 2023;23:1553-1560.

22. Roch PJ, Çelik B, Jäckle K, et al. Combination of vertebral bone quality scores from different magnetic resonance imaging sequences improves prognostic value for the estimation of osteoporosis. Spine J. 2023;23(2):305-311.

23. Kohan EM, Nemani VM, Hershman S, Kang DG, Kelly MP. Lumbar computed tomography scans are not appropriate surrogates for bone mineral density scans in primary adult spinal deformity. Neurosurg Focus. 2017;43(6):E4.

24. Organization WH. Prevention and management of osteoporosis. World Health Organization; 2003.

25. Yaprak G, Gemici C, Seseogullari OO, Karabag IS, Cini N. CT derived Hounsfield unit: an easy way to determine osteoporosis and radiation related fracture risk in irradiated patients. Front Oncol. 2020;10:742.

26. Kadri A, Binkley N, Hernando D, Anderson PA. Opportunistic use of lumbar magnetic resonance imaging for osteoporosis screening. Osteoporos Int. 2022;33(4):861-869.

27. Ehresman J, Schilling A, Yang X, et al. Vertebral bone quality score predicts fragility fractures independently of bone mineral density. Spine J. 2021;21(1):20-27.

19. Zaidi Q, Danisa OA, Cheng W. Measurement techniques and utility of Hounsfield unit values for assessment of bone quality prior to spinal instrumentation: a review of current literature. Spine. 2019;44(4):E239-E244.

20. Lee SJ, Binkley N, Lubner MG, Bruce RJ, Ziemlewicz TJ, Pickhardt PJ. Opportunistic screening for osteoporosis using the sagittal reconstruction from routine abdominal CT for combined assess-

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From the Hospital for Special Surgery in New York, New York (all authors), and from the University of Tsukuba, Institute of Medicine, Department of Orthopedic Sugery in Tsukuba, Japan (Dr Asada).

Lumbar Muscle Health Importance in Spine Surgery Nishtha Singh, MBBS

Amy Z. Lu, BS

Kasra Araghi, BS

Maximilian K. Korsun, BS

Tomoyuki Asada, MD

Sheeraz A. Qureshi, MD, MBA

Skeletal muscle plays an important role in movement, perfusion, nutrient storage, and stability.1 Additionally, muscle health has been indicated as a potential marker of how fitness increases the quality of life, including better surgical outcomes and recovery.2,3 Postoperatively, skeletal muscle plays an important role in bone healing by promoting revascularization with the provision of stem cells.4 Like other skeletal muscles, lumbar muscle groups play an important role in movement, stability, perfusion, and nutrient storage.1 Additionally, good muscle health, when coupled with regular resistance training and exercise, is generally associated with reduced injury and better overall health. 5-7 Hence, it is crucial to explore and understand the role of lumbar muscle health in patients undergoing spine surgery.

Lumbar Muscle Groups: Functional Anatomy The lumbar spine is surrounded by numerous muscles that are typically grouped based on position and function. 8,9 The major functional muscle groups consist of the extensors, flexors, lateral flexors, and rotators.10 The extensors are posterior to the lumbar spine, which consists of the erector

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spinae and multifidus muscles.8,9 The f lexors arise from the anterolateral aspect of the lumbar spine, and this includes the psoas major and the abdominal muscles (internal/external oblique, transversus abdominis). 9,11 Coordination by several muscles, including the quadratus lumborum, psoas major, multifidi, and the abdominal musculature, create trunk rotation and lateral flexion and provide support for the low back.10,12,13

Radiological Assessment and Validation Recent evidence suggests that evaluating the psoas and paralumbar muscles provides a comprehensive understanding of muscle health and is supported by 2 validated methods: the Goutallier classification (qualitative assessment) and t he total cross-sectional area (TCSA; quantitative assessment), with or without considering the fat infiltration.14-16 The Goutallier classification based on the qualitative assessment of fatt y degeneration is defined as follows: 0 = no intramuscular fat, 1 = minimal to no fatty streaks, 2 = fat evident but less than muscle tissue, 3 = equal amounts of fat and muscle, and 4 = higher quantity of fat than muscle.17,18 On the other hand, TCSA

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is commonly measured on a magnetic resonance imaging slice at the upper endplate level of L4 vertebrae quantitatively, which has shown higher reliability compared to measuring at the other levels.19,20 It is taken as an average of left and right muscles, measured meticulously by manually outlining the innermost fascial border surrounding them.16,17,18 The literature suggests that fat infiltration (FI) is associated with axial back pain and disability.16,21 For the assessment of the psoas muscle, the Goutallier classification is indicated for the qualitative assessment of the muscle mass. Additionally, for the qualitative measurement, the normalized total psoas area (NTPA), is calculated as TCSA divided by the square of patient height as defined in the literature.17,19 On the other hand, the paralumbar muscle assessment consists of Goutallier grading for the qualitative measurement along with the normalized muscle area defined as TCSA divided by the body mass index (quantitative assessment).16-18,22 Furthermore, the volumetric analysis of the paralumbar muscles is performed using the lumbar indentation value, which is defined as the minimum distance between the coordinates connecting the bilateral paralumbar muscle bulges and the top of the spinous process.17,18

From Bench to Bedside Importance in Lumbar Decompression Surgery The relationship between lumbar muscle health and the outcomes of decompression surgeries has been the subject of several

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studies. Song et al conducted a study to evaluate the impact of muscle health on the outcomes of lumbar microdiscectomy. The study included 163 patients, and the results indicated that patients with poorer muscle health took longer to achieve minimal clinically important differences, although they eventually achieved them at similar rates.

The study also found that a lower psoas TCSA normalized by height was weakly correlated with greater improvements in pain scores, while the paralumbar TCSA normalized by body mass index positively correlated with changes in physical function. These findings suggest that preoperative lumbar muscle health may influence the recovery and time to achieve functional improvements after lumbar microdiscectomy.17 In another study by Zotti et al, it was discovered that a reduced cross-sectional area of the lumbar multifidus muscle and muscle atrophy was associated with less favorable outcomes after lumbar spinal decompression surgery. 23 However, not all studies have found a significant relationship between preoperative muscle health and surgical outcomes.

Chen et al explored gene expression profiles in paralumbar tissues from patients with lumbar disc herniation who underwent microdiscectomy surgery. The study revealed that patients with poor surgical outcomes had a lower expression of brain-derived neurotrophic factor in the deep multifidus muscle and higher expression of interleukin-1β in subcutaneous fat. These findings suggest a potential relationship between impaired muscle regeneration and inflammatory dysregulation in subcutaneous fat

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with poor surgical outcomes following microdiscectomy for lumbar disc herniation.24

Importance in Lumbar Fusion Surgery Recent advances have explored the effect of lumbar muscle healt h on functional outcomes in patients undergoing spinal fusion procedures. Urakawa et al conducted a retrospective study on a group of patients undergoing posterior lumbar surgery with transforaminal lumbar interbody fusion and posterolateral fusion. In a total of 212 patients, it was found that a low NTPA was associated with worse postoperative clinical outcomes. In addition, low NTPA was reported as an independent predictor of failure to reach minimal clinically important differences in the Oswestry Disability Index and visual analog scale for leg pain.19 In another study, Bokshan et al investigated a subset of patients undergoing thoracolumbar fusion surgery and reported that patients with a low psoas cross-sectional area at the L4 vertebrae have a significantly increased risk of longer length of stay, in-hospital complications, referral to rehabilitation facilities, and mortality.25 Akin to this, Zakaria et al demonstrated psoas muscle size to be a sensitive predictor for postoperative complications in patients undergoing posterior lumbar surgeries, including fusions. On the other hand, the paralumbar muscle group was not found to predict postoperative morbidity in patients undergoing fusion surgeries. 20 However, not all studies have found a significant association between lumbar muscle health and postoperative outcomes. Wang et al investigated the association between

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preoperative paralumbar muscle degeneration and the development of adjacent segment disease (ASD) following posterior decompression and instrumented fusion for degenerative lumbar disorders. They did not identify preoperative paralumbar muscle degeneration as a statistically significant risk factor for ASD. These findings suggest that while poor lumbar muscle health may impact certain outcomes, it may not play a significant role in the development of ASD following decompression surgery. 26 Barile et al conducted a study in a cohort of 308 patients undergoing posterior spinal fusion surgery and compared the influence of osteopenia and sarcopenia on the postoperative infection rate. It was found that osteopenia (MRI-based M-score) rather than sarcopenia (validated psoas to lumbar vertebral index) was an independent risk factor for postoperative surgical site infection in patients after spinal fusion surgeries. 27 Additionally, in older patients undergoing elective lumbar spine surgeries, sarcopenia (as measured by NTPA) was not found to be a reliable predictor of acute perioperative complications. 28

Summary The existing research suggests that there is a positive relationship between lumbar muscle health and postoperative outcomes in certain contexts. Poor muscle health, characterized by reduced cross-sectional area, muscle atrophy, and altered gene expression, may lead to slower recovery, delayed achievement of functional improvements, and less favorable outcomes following lumbar decompression

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and fusion surgeries. However, the impact of muscle health on specific outcomes may vary, and more research is needed to further elucidate the complex relationship between

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bench (preoperative muscle health) and bedside (postoperative outcomes) in the context of lumbar muscle health and spine surgery. l

References 1. McCuller C, Jessu R, Callahan AL. Physiology, skeletal muscle. StatPearls. https://www.ncbi.nlm.nih.gov/books/ NBK537139/. Published April 28, 2023. Accessed June 28, 2023.

12. Andersson EA, Grundström H, Thorstensson A. Diverging intramuscular activity patterns in back and abdominal muscles during trunk rotation.

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13. Akuthota V, Nadler SF. Core strengthening. Arch Phys Med Rehabil.

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3. Myers JN, Fonda H. The impact of fitness on surgical outcomes. Curr Sports Med Rep. 2016;15(4):282-289.

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StatPearls Publishing; 2023.

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14. Touban BM, Pavlesen S, Smoak JB, et al. Decreased lean psoas cross-sectional area is associated with increased 1-year all-cause mortality in male elderly orthopaedic trauma patients.

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15. Morrell GR, Ikizler TA, Chen X, et al. Psoas muscle cross-sectional area as a measure of whole-body lean muscle mass in maintenance hemodialysis patients.

J Ren Nutr. 2016;26(4):258-264.

16. Gibbons D, McDonnell JM, Ahern DP, et al. The relationship between radiological paralumbar lumbar measures and clinical measures of sarcopenia in older patients with chronic lower back pain. J Frailty Sarcopenia Falls. 2022;07(02):52-59.

17. Song J, Araghi K, Dupont MM, et al.

Association between muscle health and patient-reported outcomes after lumbar microdiscectomy: early results.

Spine J. 2022;22(10):1677-1686.

18. Tamai K, Chen J, Stone M, et al. The evaluation of lumbar paralumbar muscle quantity and quality using the Goutallier classification and lumbar indentation value. Eur Spine J. 2018;27(5):1005-1012.

19. Urakawa H, Sato K, Vaishnav AS, et al.

Preoperative cross-sectional area of psoas muscle correlates with short-term functional outcomes after posterior lumbar surgery. Eur Spine J. 2023;32(7):2326-2335.

20. Zakaria HM, Schultz L, Mossa-Basha F, Griffith B, Chang V. Morphometrics as a predictor of perioperative morbidity after lumbar spine surgery.

Neurosurg Focus. 2015;39(4):E5.

21. Teichtahl AJ, Urquhart DM, Wang Y, et al. Fat infiltration of paralumbar muscles is associated with low back pain, disability, and structural abnormalities in community-based adults.

Spine J. 2015;15(7):1593-1601.

22. Virk S, Wright-Chisem J, Sandhu M, et al. A novel magnetic resonance imaging-based lumbar muscle grade to predict health-related quality of life scores among patients requiring surgery. Spine. 2020;46(4):259-267.

23. Zotti MGT, Boas FV, Clifton T, Piche M, Yoon WW, Freeman BJC. Does pre-operative magnetic resonance imaging of the lumbar multifidus muscle predict clinical outcomes following lumbar spinal decompression for symptomatic spinal stenosis?

Eur Spine J. 2017;26(10):2589-2597.

24. Chen X, Hodges PW, James G, Diwan AD. Do markers of inflammation and/or muscle regeneration in lumbar multifidus muscle and fat differ between individuals with good or poor outcome following microdiscectomy for lumbar disc herniation? Spine. 2020;46(10):678-686.

25. Bokshan SL, Han AL, DePasse JM, et al.

Effect of sarcopenia on postoperative morbidity and mortality after thoracolumbar spine surgery. Orthopedics. 2016;39(6).

26. Wang H, Ma L, Yang D, et al. Incidence and risk factors of adjacent segment disease following posterior decompression and instrumented fusion for degenerative lumbar disorders. Medicine. 2017;96(5):e6032.

27. Barile F, Ruffilli A, Fiore M, et al. Is sarcopenia a risk factor for postoperative surgical site infection after posterior lumbar spinal fusion? Int J Spine Surg. 2022;16(4):735-739.

28. Charest-Morin R, Street J, Zhang H, et al. Frailty and sarcopenia do not predict adverse events in an elderly population undergoing non-complex primary elective surgery for degenerative conditions of the lumbar spine. Spine J. 2018;18(2):245-254.

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From Midwest Orthopaedics at Rush in Chicago, Illinois.

Annular Repair The annulus fibrosis is an integral part of the intervertebral segment, as its degenerat ion Cameron Kia, MD can have signif icant clinical implications.1 Weakening of the annulus fibrosis from age-related changes is one of the most com mon rea son s of lu mba r disc herniations. W hile most s y mptoms may resolve w it h Gregory Lopez, MD conservative treatment, lumbar discectomy is still one of the most commonly performed spine surgeries in the United States. 2 Despite overall good surgical success rates, recurrent symptomatic herniations have been reported in 7% to 18% of patients. 3,4 Recurrent disc herniations also remain a significant clinical dilemma because they carry the potential for greater complications and are being more technically demanding. 3 Over the past several years, more emphasis has been placed on understanding the pathophysiology of the annulus as well as its healing potential. 5,6 Regardless of biologic or mechanical repair, the potential for annular healing could prevent patients from requiring subsequent surgeries. The focus of this review will be on the annulus anatomy and current repair techniques.

Annulus Fibrosus Anatomy and Healing Potential The structural anatomy plays an important role in understanding how these herni-

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ations occur along with ways to prevent recurrence. The annulus surrounds the inner nucleus pulposis, acting as a laminate structure.7 It is composed of mainly water but includes type 1 collagen, forming layers surrounding proteoglycans.7 Its inner and outer layers are organized differently due to the different mechanical environments it faces.7 For instance, cells of the outer annulus produce mainly type 1 collagen due to the compression it undergoes from axial loading.7 W hile several factors play a role in its degenerat ion, ag ing causes sig nif icant changes that alter its structure.1 With time, both proteoglycan and collagen densit y decrease, leading to weakening and eventually the formation of fissures or cracks within the annulus. 8 Self-healing of these tears are limited due the avascularity of the annulus.

In an animal study, Smith et al found that with only a stab incision of the annulus, the defect healed with fibrous in-growth within a 1-year period. 9 Even despite this healing potential, biomechanical studies have demonst rated mechanical consequences of this fibrous in-grow th, such as loss of load absorption over time.10 This inf lammator y environment and limited healing capability can lead to reherniation and the need for subsequent surgical intervention. Several risk factors have been found when examining for recurrent herniations, including disc degeneration, age,

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© 2019 USA Spine Care & Orthopedics

sex, and obesity.11-14 Miller et al found that an annular defect of greater than 6 mm was associated with a significantly higher likelihood of symptomatic recurrence.11 This has led to the investigation of several biologic and mechanical devices to aid in annular healing.15

Biologic Repair Although limited clinical data are available, several synthetic and natural biomaterials have been examined for annular repair.16,17 The majority of these studies involve gene or cell therapy being delivered via a scaffold to provide a mechanical environment for collagen synthesis.18-20 In an animal study by Fuller et al, the authors created slit puncture defects within the annulus and repaired them w ith

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collagen sponges soaked with hyaluronan oligosaccharides to facilitate extracellular matrix remodeling.15 This induced regrowth of annular lamellae around the puncture site, with reorganization of collagen and increased proteoglycan.15 One common agreement in most of the biologic literature has been the importance of a mechanical scaffold to allow for host cells to integrate into the defect. 20 While the data are promising, the current literature is based on animal models only, with the majority of defect size not always mimicking those seen clinically.

In addition, a majorit y of commercially available scaffolds are comprised of type 2 collagen, resulting in a higher production compared to the native type 1 collagen. 20 Bron et al proposed several criteria for

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PATIENT OUTCOMES

scaffolds, which included mimicking the native biomechanical properties of the disc, one that promotes native cells to survive and secrete extracellular matrix, and a scaffold t hat also contains t he nucleus puloposus. 5 Despite severa l successf ul in-vivo biologic repairs, no ideal scaffold or biologic integration has been identified for clinical use. 21,22

Surgical Repair Given the limited healing capacity of the annulus, the more recent focus has been surgical repair at the time of discectomy.23-25 With data supporting that large annular defects play a role in reherniation,11 theoretically reducing this gap could prevent f urt her ex trusion of t he disc. This was biomechanically supported by Bartlett et al, who found that disc load to failure was significantly lower in annular defects that were not repaired with suture. 26 Ba i ley et a l conducted a 2-yea r, prospect ively randomized cont rolled t rial comparing annular defect repaired with a suture anchor device (Xclose Tissue Repair; Anulex Technologies, Minnetonka, MN) to controls. 27 Although the authors found an overall reduction in reherniation at 2 years, the study did not reach statistical significance. 27 Thome et al performed a similar randomized cont rolled study using another suture anchor device and found the rate of symptomatic herniation was 50% lower w it h repa i r at 2 yea rs, w it h a sig n i f ica nt ly lower reoperat ion rate. 28 W hile no signif icant increase in complication rates has been found using

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a suture device, its actual effectiveness remains in question.

More recently, a bone mesh anchor dev ice (Barricaid; Int rinsic Therapeut ics Inc., Woburn, MA) has been developed for larger defects. This dev ice anchors into the vertebral body and has a mesh plug designed to fit into the sized annular defect as a mechanical plug. A recent US study presented a case series of 55 patients with 3-month follow up after bone anchor closure and found a reoperation rate of 1.8% at f inal follow up, w ith an overall significant improvement in leg pain and Oswestr y Disabilit y Index scores. 29 In a recent meta-analysis combining 15 studies and more than 2100 patients, the Barricaid annular device was found to be effective in reducing reoperation compared to a suture device. 30 W hile this bone closure repair provides promising results, longer term results, as well as a further cost analysis, is needed to know its true benefit.

Conclusions Annular tears remain a significant clinical dilemma, with larger annular tears (>6 mm) being a major risk factor for reherniation.

The ideal repair involves restoring the biomechanics of the annulus and healing with type 1 collagen. Current repair devices on the market have had mixed results, with suture anchors demonstrating no significant difference and moderate evidence to support the use of a bone anchor device.

Longer-term data, in addition to cost analysis, are needed to support the use of any repair method for large annular defects. l

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References 1. Singh K, Masuda K, Thonar EJ-M, An HS, Cs-Szabo G. Age-related changes in the extracellular matrix of mucleus pulposus and annulus fibrosus of human intervertebral disc. Spine. 2008;34:10-16.

2. Gray DT, Deyo RA, Kreuter W, et al. Population-based trends in volumes and rates of ambulatory lumbar spine surgery.

Spine (Phila Pa 1976). 2006;31:1957-1963.

3. McGirt MJ, Eustacchio S, Varga P, et al. A prospective cohort study of close interval computed tomography and magnetic resonance imaging after primary lumbar discectomy: factors associated with recurrent disc herniation and disc height loss. Spine (Phila Pa 1976). 2009;34:2044-2051.

4. Ambrossi GL, McGirt MJ, Sciubba DM, et al. Recurrent lumbar disc herniation after single-level lumbar discectomy: incidence and health care cost analysis. Neurosurgery. 2009;65:574–578.

5. Bron JL, Heider MN, Meisel H-J, Van Royen BJ, Smit TH. Repair, regenerative and supportive therapies of the annulus fibrosus: achievements and challenges. Eur Spine J. 2009;18:301-313.

6. Bailey A, Araghi A, Blumenthal S, Huffmon GV; Anular Repair Clinical Study Group. Prospective, multicenter, randomized, controlled study of anular repair in lumbar discectomy: two-year follow-up. Spine. 2013;38(14):1161-1169 7. Pezowicz CA, Robertson PA, Broom ND. The structural basis of interlamellar cohesion in the intervertebral disc well. J Anat. 2006;208(3):317-330.

8. Osti OL, Vernon-Roberts B, Moore R, Fraser RD. Annular tears and disc degeneration in the lumbar spine. A post-mortem study of 135 discs. J Bone Joint Surg Br. 1992;74(5):678-682.

review and meta-analysis of comparative studies. Spine. 2018;43(5):E308-e315.

12. Kim KT, Lee DH, Cho DC, et al. Preoperative risk factors for recurrent lumbar disk herniation in L5-S1. J Spinal Disord Tech. 2015;28:E571-E577.

13. Leven D, Passias PG, Errico TJ, et al.

Risk factors for reoperation in patients treated surgically for intervertebral disc herniation: a subanalysis of eight-year SPORT data. J Bone Joint Surg Am. 2015;97:1316-1325.

14. Moliterno JA, Knopman J, Parikh K, et al. Results and risk factors for recurrence following single-level tubular lumbar microdiscectomy. J Neurosurg Spine. 2010;12:680-686.

15. Fuller ES, Shu C, Smith MM, Little CB, Melrose J. Hyaluronan oligosaccharides stimulate matrix metalloproteinase and anabolic gene expression in vitro by intervertebral disc cells and annular repair in vivo. J Tissue Eng Regen Med. 2018;12(1):e216-e226.

16. Grunert P, Borde, BH, Towne SB, et al.

Riboflavin crosslinked high‐density collagen gel for the repair of annular defects in intervertebral discs: an in vivo study.

Acta Biomaterialia. 2015;26:215-224.

17. McGuire R., Borem R, Mercuri J. The fabrication and characterization of a multi‐ laminate, angle‐ply collagen patch for annulus fibrosus repair. J Tissue Eng Regen Med. 2017;11(12):3488-3493.

18. Liang H, Ma SY, Feng G, Shen FH, Joshua LX. Therapeutic effects of adenovirus-mediated growth and differentiation factor-5 in a mice disc degeneration model induced by annulus needle puncture. Spine J. 2010;10:32-41.

9. Smith JW, Walmsley R. Experimental incision of the intervertebral disc. J Bone Joint Surg Br. 1951;33-B(4):612-625.

19. Yoon ST, Park JS, Kim KS, et al.

ISSLS prize winner: LMP-1 upregulates intervertebral disc cell production of proteoglycans and BMPs in vitro and in vivo. Spine. 2004;29:2603-2611.

10. Fazzalari NL, Costi JJ, Hearn TC, et al. Mechanical and pathologic consequences of induced concentric anular tears in an ovine model.

Spine. 2001;26(23):2572-2581.

20. Saad L, Spector M. Effects of collagen type on the behavior of adult canine annulus fibrosus cells in collagen-glycosaminoglycan scaffolds. J Biomed Mater Res A. 2004;71:233-241.

11. Miller LE, McGirt MJ, Garfin SR, Bono CM. Association of annular defect width after lumbar discectomy with risk of symptom recurrence and reoperation: systematic

21. Sato M, Asazuma T, Ishihara M, et al.

An experimental study of the regeneration of the intervertebral disc with an allograft of cultured annulus fibro-

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sus cells using a tissue-engineering method. Spine. 2003;28(6):548-553.

22. Sato M, Kikuchi M, Ishihara M, et al. Tissue engineering of the intervertebral disc with cultured annulus fibrosus cells using atelocollagen honeycomb-shaped scaffold with a membrane seal (ACHMS scaffold). Med Bio Eng Comput. 2003;41(3):365-371.

23. Thome C, Klassen PD, Bouma GJ, et al. Annular closure in lumbar microdiscectomy for prevention of reherniation: a randomized clinical trial. Spine J. 2018;18:2278-2287.

24. Ardeshiri A, Miller LE, Thome C. Two- year real-world results of lumbar discectomy with bone-anchored annular closure in patients at high risk of reherniation. Eur Spine J. 2019;28:2572-2578.

25. Bailey A, Araghi A, Blumenthal S, Huffmon GV. Prospective, multicenter, randomized, controlled study of anular repair in lumbar discectomy two-year follow-up. Spine. 2013;38:1161-1169.

26. Bartlett A, Wales L, Houfburg R, et al. Optimizing the effectiveness of a mechanical suture-based anulus fibrosus repair construct in an acute failure laboratory simulation. J Spinal Disord Tech. 2013;26(7):393-399.

27. Bailey A, Araghi A, Blumenthal S, Huffmon GV; Anular Repair Clinical Study Group. Prospective, multicenter, randomized, controlled study of anular repair in lumbar discectomy: two-year follow-up [erratum in Spine (Phila Pa 1976). 2013;38(17):1527]. Spine (Phila Pa 1976). 2013;38(14):1161-1169.

28. Thome C, Klassen PD, Bouma GJ et al. Annular closure in lumbar miscrodiscectomy for prevention of reherniation: a randomized clinical trial.

Spine J. 2018;18(12):2278-2287.

29. Nunley P, Strenge KB, Huntsman K, et al.

Lumbar discectomy with barricaid device implantation in patients at high risk of reherniation: initial results from a postmark study. Cureus. 2021;13(12):e20274.

30. Wang Y, He X, Chen S, et al. Annulus fibrosus repair for lumbar disc herniation: a meta-analysis of clinical outcomes from controlled studies [published online ahead of print April 17, 2023]. Global Spine J. doi:10.1177/21925682231169963

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THE SI JOINT

From Rush University Medical Center in Chicago, Illinois.

The Rise of Sacroiliac Joint Fusions Arash J. Sayari, MD

With more than $85 billion in annual expenditures, low back pain remains the fifth most common reason for a medical visit.1 Significant efforts have been made toward better understanding the various etiologies of low back pain.

The sacroiliac (SI) joint has been cited as a source of pain in up to 15% of patients seen by physicians for low back pain, but it is often misdiagnosed as lumbar pathology. 2 Therefore, it is no surprise that healthcare expenditure has steadily increased in both diagnosing and treating SI dysfunction.

The SI joint connects the spine to the pelvis, transferring loads between the lower extremities and the spine. However, the SI joint functions poorly in resisting shear loads, predisposing patients to increased rates of degeneration. 3 Increased rates of degeneration exist in cases of repetitive st ress, preg na nc y, seronegat ive spondyloarthropathies, obesity, trauma, and degeneration, leading to asymmetric pain that can mimic disc degeneration and radiculopathy. A diagnosis of SI joint dysfunction has demonstrated a moderate to high sensitivity and specificity in patients who demonstrate a positive response to at least three provocative examination maneuvers.4 Fluoroscopic- and ultrasound-guided SI joint injections offer both diagnostic and therapeutic utilit y. Patients who report

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a significant improvement in pain after an injection may be further diagnosed as having SI joint dysfunction.

Why the Increase in Utilization?

Similar to the attention paid to improving our understanding of the SI joint and low back pain, there has been an increase in the utilization of SI joint fusion procedures.

Since its inception in 2015, the minimally invasive surger y (MIS) SI joint fusion Current Procedural Terminolog y (CPT) code, 27279, has seen increased volume and reimbursement rates. 5 Furthermore, both open and MIS SI joint fusions have seen an increase in procedure volume of 2,351% in the decade spanning from 2010 to 2020, though MIS SI joint fusions have rapidly outgrown their open counterparts.

Whether this relates to an increase in diagnosis or increase in reimbursement is up for debate.

Additionally, open SI joint fusions are becoming more commonplace in long-construct deformity procedures. The current literature has suggested widely var ying rates of pseudarthrosis from the single digits to as high as 70%. 6 In addition to alternative techniques such as multiple rod constructs, surgeons addressing spinal deformities have incorporated S2-alar-iliac (S2AI) screws instead of iliac screws to fur-

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THE SI JOINT

ther minimize the risks of pseudarthrosis and reoperation.7,8 However, literature is lacking as to whether these patients have been evaluated for dysfunction prior to u ndergoi ng f usion of t he SI joi nt. T he longer-term effects of fusing a previously asymptomatic SI joint has yet to be clearly demonstrated.

Various medical technology companies have offered more advanced procedural solutions to addressing SI joint dysfunction, instilling confidence in surgeons hoping to successfully treat SI joint pain. Training laboratories, demonstrations, and surgeon support teams have attempted to unif y our understanding of how to successfully perform an SI joint fusion. Industr y-led grow t h has been well demonst rated in the pharmaceutical arena and is becoming more apparent in spine surgery. This often leads surgeons toward trialing new techniques and implants with the hopes of improving outcomes. Together, this has led to a rapid growth in SI joint fusions.

Is the Increase in Utilization Warranted?

Early outcome studies following MIS SI joint fusions demonstrated excellent medium- and long-term outcomes. However, these studies were inherently biased. 9 A systematic review of 16 articles suggested excellent outcomes ranging from 18% to 100%.10 Much of the meaningful literature w it h long-term follow-up, however, involved authors with significant conflicts of interest. In patients correctly diagnosed as having SI joint dysfunction, outcome stud-

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© 2023 Globus Medical

ies continue to demonstrate good results at 24 and 60 months. 9,11 Currently, there is a double-blinded multicenter randomized controlled trial with ongoing enrollment comparing MIS SI joint fusion with a sham operation, but the results have yet to be published.12 Furthermore, no argument can be made for the utility of an MIS technique for SI fusion. Transiliac and MIS dorsal approaches minimize the dissection and tissue disruption required to perform a successful arthrodesis, and techniques have been described using both fluoroscopy and navigation with excellent results.13 As robotics and navigation increase its reach beyond pedicle screws, placement of SI screws from various approaches may even allow for improved biomechanics. “Crossed” tech-

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THE SI JOINT

niques of placing implants from a lateral, posterolateral, or even posterior approach allow for improved biomechanical stability across the SI joint and may become more reproducible with the use of robotics and navigation.

Final Thoughts The SI joint is prone to biomechanical loads that can increase degeneration and is a site of wear in various medical conditions. Together, the SI joint plays a role in low back pain and can mimic lumbar pathology, often going underappreciated as a diagnosis and thus delaying appro-

priate medical care. Recent advancements in diagnosis include correlating physical examination findings with injections and have allowed surgeons to better appreciate the SI joint as a pain generator, though confirming that the SI joint is indeed the primar y cause of low back pain has remained a challenge. While no surgery is perfect, MIS techniques can mitigate many of the potential complications and improve outcomes. Patients who are successfully diagnosed as having SI joint dysfunction may benefit from an MIS SI joint fusion across a minimally mobile joint that is often a significant source of pain. l

References 1. Martin BI, Deyo RA, Mirza SK, et al.

Expenditures and health status among adults with back and neck problems.

JAMA. 2008;299(6):656-664.

2. Dreyfuss P, Dreyer SJ, Cole A, Mayo K. Sacroiliac joint pain. J Am Acad Orthop Surg. 2004;12(4):255-265.

3. Kiapour A, Joukar A, Elgafy H, Erbulut DU, Agarwal AK, Goel VK. Biomechanics of the sacroiliac joint: anatomy, function, biomechanics, sexual dimorphism, and causes of pain. Int J Spine Surg. 2020;14(Suppl 1):3-13.

4. Falowski S, Sayed D, Pope J, et al. A review and algorithm in the diagnosis and treatment of sacroiliac joint pain.

J Pain Res. 2020;13:3337-3348.

5. Federico VP, Zavras AG, Butler A, Nolte MT, Munim MA, Lopez GD, et al.

Medicare reimbursement rates and utilization trends in sacroiliac joint fusion [published online ahead of print May 16, 2023]. J Am Acad Orthop Surg. doi:10.5435/JAAOS-D-22-00800

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6. Kim YJ, Bridwell KH, Lenke LG, Rhim S, Cheh G. Pseudarthrosis in long adult spinal deformity instrumentation and fusion to the sacrum: prevalence and risk factor analysis of 144 cases.

Spine. 2006;31(20):2329-2336.

11. Duhon BS, Bitan F, Lockstadt H, et al. Triangular titanium implants for minimally invasive sacroiliac joint fusion: 2-year follow-up from a prospective multicenter trial.

Int J Spine Surg. 2016;10:13.

7. Elder BD, Ishida W, Lo SFL, et al. Use of S2-Alar-iliac screws associated with less complications than iliac screws in adult lumbosacropelvic fixation. Spine. 2017;42(3):E142-E149.

12. Randers EM, Gerdhem P, Dahl J, Stuge B, Kibsgård TJ. The effect of minimally invasive sacroiliac joint fusion compared with sham operation: study protocol of a prospective double-blinded multicenter randomized controlled trial. Acta Orthop. 2022;93:75-81.

8. Ishida W, Elder BD, Holmes C, et al.

Comparison between S2-alar-iliac screw fixation and iliac screw fixation in adult deformity surgery: reoperation rates and spinopelvic parameters.

Global Spine J. 2017;7(7):672-680.

9. Rudolf L, Capobianco R. Five-year clinical and radiographic outcomes after minimally invasive sacroiliac joint fusion using triangular implants.

Open Orthop J. 2014;8:375-383.

13. Polly DW Jr, Holton KJ. Minimally invasive sacroiliac joint fusion: a lateral approach using triangular titanium implants and navigation. JBJS Essent Surg Tech. 2020;10(4):e19.00067.

10. Zaidi HA, Montoure AJ, Dickman CA. Surgical and clinical efficacy of sacroiliac joint fusion: a systematic review of the literature. J Neurosurg Spine. 2015;23(1):59-66.

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LEGAL MATTERS

From The CORE Institute in Phoenix, Arizona

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Informed Consent in Spine Surgery The doctrine of informed consent refers to the requirement that a patient have sufficient information and understanding prior to making decisions about their medical care. The process of obtaining a patient’s informed consent plays a critical role in ensuring ethical and legal standards are met prior to the decision to undergo spine surgery. In general, important components of informed consent include the following: (1) information about the patient’s diagnosis, (2) the treatment recommendation, (3) alternative treatment options, (4) the risks and benefits of the recommended treatment, and (5) the expected result of the treatment options being considered.1 T he precise lega l def i n it ion of what constitutes adequate informed consent has evolved over time and varies between jurisdictions. In fact, t he seminal case credited w ith shaping the current legal standard in the United States relates to spine surgery. Canterbury v Spence, which was decided on appeal in 1972, redefined the legal concepts of the doctor-patient relationship and informed consent. 2 The case involved George Canterbury, a 19-yearold man who underwent a thoracic laminectomy in Washington, DC, in 1959 by Dr. William Spence. After a postoperative fa l l f rom his hospita l bed, Ca nterbur y developed paraparesis and incontinence.

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He u nder went emergent reBrandon P. Hirsch MD vision surger y after which he did not completely recover. Canterbur y filed a lawsuit, claiming that he had not been adequately informed about the risks associated with the procedure.

Although incomprehensible to physicians today, conventional medical doctrine at the time held that physicians not disclose upsetting or negative information to patients in order to prevent emotional harm.

This meant that during the pre-Canterbury era, pat ients were of ten not told about terminal diagnoses or serious risks associated with treatment. Consistent with this practice, Dr. Spence admitted he did not discuss paralysis as a risk of thoracic laminectomy because he was concerned this would deter the patient from proceeding with surger y. The jur y eventually ruled that Dr. Spence did not meet his duty to disclose the risks of surger y. The ruling transformed the standard for disclosure of risk from what another surgeon would deem appropriate to share with a patient to that which a patient would expect to k now before elec t i ng to proceed w it h surgery. Today, failure to obtain adequate informed consent is a frequent complaint in malpractice litigation involving spine surgeons. A 2017 review of the Westlaw Next database found that among 233 cases

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LEGAL MATTERS

of spine surgery litigation, 153 cases (66%) cited an allegation of inadequate informed consent. 3 A 2011 study of litigation related to cervical spine surgery found that lack of informed consent was involved in 44 (56%) of the 78 cases studied.4 Despite its importance, the process of obta i n i ng adequate i n for med consent from patients prior to spine surgery faces many challenges. Spine conditions and their associated surgical treatments are complex and involve significant detail.

Expected outcomes after surgery can be uncertain and the reported risks of many types of spine surgery vary greatly within the literature. Patients undergoing spine surger y may also be of advanced age or may have limited health literacy making it difficult for them to comprehend and retain the information they are given. 5 Chronic pain and the use of psychoactive medications are common in patients undergoing spine surgery and affect patients’ ability to understand complex information. The current structure of the healthcare system

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places significant time constraints on office visits, further limiting opportunities for discussion. Despite these challenges, surgeons remain obligated to ensure a patient’s informed consent.

It is i mpor ta nt to u ndersta nd t hat a signed consent form does not mean that the requirements for informed consent have been met. Rather than thinking of consent as a signed document or a single event, surgeons should consider informed consent as a process involving discussion and assessment of patient understanding.

There are several steps that spine surgeons can take to improve the process: • Emphasize the importance of informed consent. Ensure that the patient understands that they play an important role in the decisionmaking process. Reinforce their autonomy and the fact that the decision to proceed with surgery is theirs.

• Use clear and understandable language. Avoid using complex medical terminology when discussing the procedure with patients. Instead, use plain and simple language to ensure that patients can comprehend the information being provided.

• Provide written materials. Along with the verbal discussion, provide written materials that outline the procedure, potential risks, expected

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outcomes, and alternative treatment options. These materials can serve as references for patients to review at their own pace and reinforce their understanding.

• Use visual aids. Visual aids such as diagrams, models, or even computer simulations can be utilized to help patients visualize the spine, the surgical procedure, and the potential impact of the surgery. These aids can make complex concepts more accessible and improve patient comprehension.

• Encourage involvement of family members or support persons.

Patients may find it helpful to have a family member or a trusted support person present during the informed consent process. This person can provide emotional support and help the patient process the information more effectively.

• Assess understanding. Ask patients to recall the risks and alternatives to surgery that were discussed. Ask

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them if they fully understand the information they were given and encourage them to ask questions to fill in any gaps in knowledge.

• Document the consent process.

Record the details of the discussion, any questions asked, the information provided, and the patient’s agreement to proceed with the surgery. Documentation serves as evidence that the process was conducted appropriately and that the patient was well informed.

Informed consent remains a critical part of patient care in spine surgery. While many obstacles to providing adequate informed consent exist, a variety of strategies are available to help surgeons ensure that a patient has an adequate understanding of their treatment options.

Prov iding effective informed consent strengthens the doctor-patient relationship and enhances patient’s trust. In doing so, spine surgeons can reduce the likelihood of adverse outcomes in malpractice litigation and improve the overall quality of care they provide. l

References 1. Walston-Dunham B. Medical Malpractice: Law and Litigation. Thomson/Delmar Learning; 2006.

2. Murphy WJ. Canterbury v. Spence—the case and a few comments.

Forum. 1976;11:716-726.

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3. Grauberger J, Kerezoudis P, Choudhry AJ, et al. Allegations of failure to obtain informed consent in spinal surgery medical malpractice claims.

JAMA Surg. 2017;152:e170544.

4. Epstein NE. A review of medicolegal malpractice suits involving cervical spine. J Spinal Disord Tech. 2011;24:15-19.

5. Lo WB, McAuley CP, Gillies MJ, et al. Consent: an event or a memory in lumbar spinal surgery? A multi-centre, multi-specialty prospective study of documentation and patient recall of consent content.

Eur Spine J. 2017;26:2789-2796.

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