Reading Hospital/Tower Health Student Summer Research Projects

Trends and Outcomes of ST-Segment-Elevation Myocardial Infarction in Hospitalized Patients Without Standard Modifiable Cardiovascular Risk Factors

by Baotram Nguyen; Biraj Shrestha, MD; Amar Suwal, MD; Rebecca DeBoer, DO; Laura Kane; Anthony Donato, MD Department of Internal Medicine, Reading Hospital, Reading, PA

INTRODUCTION: Standard Modifiable Cardiovascular Risk Factors (SMuRFs) include hypertension, diabetes, hyperlipidemia, and smoking, all of which increase the risk of developing heart disease. However, newer research has observed that in patients experiencing their first episode of ST-segment-elevation myocardial infarction (STEMI), patients without SMuRFs tend to have worse in-hospital outcomes for unclear reasons. We sought to better understand the impact of patient and physician preferences that could explain these differences.

METHODS: A retrospective chart review was performed on patients presenting to the Reading Hospital Emergency Department with the first STEMI episode from January 2019 to April 2022. Patients with past medical histories of STEMI, stent placements, or coronary artery bypass grafts were excluded in order to capture only first events. The study population was divided into two comparative cohorts, SMuRFs and SMuRFless STEMI, based on the history of hypertension, diabetes, hyperlipidemia, or smoking in the patients’ past medical records from EPIC. In addition, demographics and inhospital outcomes were assessed.

RESULTS: 356 first-time STEMI patients were identified, of which 36 (10.11%) were SMuRFless. SMuRFless patients were more likely to be younger (58.77 vs. 64.47 years) and male (74 vs. 65%). SMuRFless patients were more likely to present to the emergency department as walk-ins (54.28 versus 39.25%), took longer to arrive after symptom onset (6.70 versus 5.15 hours), have higher initial troponin values (3.90 versus 2.70 ng/mL), had to wait longer for cardiac catheterization from arrival (69.96 versus 47.48 minutes) and were less likely to receive PCI overall (80.00 versus 89.09%). Furthermore, SMuRFless patients were more likely to have higher inhospital mortality (17.14 versus 8.72%), cardiogenic shock (14.70 versus 6.47), acute heart failure (5.88 versus 2.27%), bleeding requiring transfusion (8.82 versus 1.92%) and overall complications (25.71 versus 21.50%). At discharge, SMuRFless patients also received less guideline-directed medical therapy (GDMT) than patients with SMuRFs.

CONCLUSION: SMuRFless patients make up a considerable proportion of first-time STEMI patients at Reading Hospital, have higher mortality and complications, and undergo fewer procedures. Whether there are patient or physician preferences that may have influenced these outcomes is a matter of ongoing study for this project. Sample size in this interim analysis is too small to make statistical conclusions, but the work is ongoing.

The BCMS Pat Sharma President’s Scholarship A Quality Improvement Project to Decrease the Number of No-Show Patients

by Cassiopeia Roychowdhury, MD, Dept. of Family Medicine, Reading Hospital, Reading, PA; Katie Rudzenski, DO, Dept. of Family Medicine PGY-3, Reading Hospital, Reading, PA; Emma Miller, Millersville University, Millersville, PA

INTRODUCTION: Patients’ attendance to their appointments is essential for maintaining their health and for the clinic’s productivity. However, doctors’ offices are noticing anywhere from 10-40% of their patients do not show up for their appointments and they do not cancel ahead of time. There are many different factors that can affect a patient’s ability to attend an appointment, whether it be transportation or forgetfulness. This study attempts to understand why patients are missing their appointments and to form possible interventions that could be used to reduce the number of no-shows. By reducing the percent of patient no-shows, it helps ensure that patients are receiving the care that they need, and the clinic is not losing revenue.

METHODS: Patients from the Family Health Care Center at Reading Hospital that have missed over 3-4 appointments were chosen at random and voluntarily surveyed over the telephone. A translator was used for patients that did not speak English. The survey included questions focusing on transportation, childcare, and if the patient was receiving reminders. The purpose of our survey was to explore common barriers patients face and create a targeted intervention for a future study. However, the data from the survey will be used to create targeted interventions in the future. Seven patients participated in the survey and results were analyzed with pie charts.

RESULTS: Based on the survey, most patients (40%) miss their appointments because they forgot despite 86% indicating they received at least one reminder. Other patients gave reasons such as being sick, needing childcare, being called into work, etc. When asked if the patients knew how to cancel the appointments, 71% reported that they did. 57% reported that they never tried to cancel the appointment. However, it should be noted that most of the patients we attempted to contact, did not answer their phone or the number called was inactive. Indicating patients might not be receiving reminders, especially if the inactive phone number is all the office has. Patients were also asked if they had reliable transportation and 28% reported that they did not. CONCLUSION: This survey provided helpful insights on why patients are not showing to their appointments. For future interventions, patients could be given an appointment reminder on their primary care providers (PCP) business card. This study had planned to try the business cards as the intervention but due to time constrictions it did not get started until this week. The interventions’ effectiveness will not be seen for another week, but patients have already given feedback. Many patients thought seeing the PCP picture with their appointment made a personal connection and it was a smaller, more convenient option to the after-visit summaries. Other ideas would be to limit number of follow-ups and have patients call for appointments as needed. It would be helpful to confirm at check-out if the number on file is accurate. We can also ask patients for their preferred method of reminders such as a phone call, mychart, etc. A future recommendation for completing clinical surveys is to give them to qualifying patients in the office or allow the survey more time and send them in the mail. Most patients that were called did not answer or the phone number was inactive, and it made it not the most effective method. Overall, an intervention should be implemented to help improve the consistency of patient care and productivity in the office. Resources Claveau, J., Authier, M., Rodrigues, I., & Crevier-Tousignant, M. (2020, May). Patients’ missed appointments in academic family practices in Quebec. Canadian family physician Medecin de famille canadien. Retrieved July 14, 2022, from https://www.ncbi.nlm.nih. gov/pmc/articles/PMC7219803/ Jain, S. H. (2021, December 10). Missed appointments, missed opportunities: Tackling the patient no-show problem. Forbes. Retrieved July 14, 2022, from https://www.forbes.com/sites/ sachinjain/2019/10/06/missed-appointments-missed-opportunitiestackling-the-patient-no-show-problem/?sh=145cd2d7573b Marbouh, D., Khaleel, I., Al Shanqiti, K., Al Tamimi, M., Simsekler, M. C., Ellahham, S., Alibazoglu, D., & Alibazoglu, H. (2020). evaluating the impact of patient no-shows on service quality. Risk Management and Healthcare Policy, Volume 13, 509–517. https://doi.org/10.2147/rmhp.s232114

Solutionreach. (n.d.). Everything you need to know about no-shows in your practice. Everything You Need to Know About No-Shows In Your Practice. Retrieved July 14, 2022, from https://www. solutionreach.com/guide/reduce-no-shows-and-cancellations-inyour-practice?source_url=https%3A%2F%2Fwww.solutionreach. com%2Fguide%2Freduce-no-shows-and-cancellations-in-yourpractice%23%3A~%3Atext%3DNo-shows%2520are%252C%2520u nfortunately%252C%2520pretty%2520common.%2520No-how%25 20rates%2520have%2Cspecialty%2520and%2520here%2520to%25 20calculate%2520your%2520no-show%2520rate%2529 Toland, B. (2013, February 24). No-shows cost health care system billions. Gazette. Retrieved July 14, 2022, from https://www.postgazette.com/business/businessnews/2013/02/24/No-shows-cost-healthcare-system-billions/stories/201302240381

Mortality and Comorbidities Associated with COVID-19 Infection in Psychiatric Patients from a State Hospital

by Nicole Villa1, Matthew Driben2, Regina Reed2, Maria Ruiza Yee2, Eduardo D. Espiridion2 Drexel College of Medicine, Tower Health Campus1, Reading Hospital, Tower Health2

ABSTRACT

INTRODUCTION: As dangerous as COVID-19 has been for the general population, it has been even more severe in psychiatric wards. A state hospital is a particularly transmissible location for COVID-19, and the medications that psychiatric patients typically utilize may contribute to the incidence of comorbidities including obesity, hypertension, diabetes, chronic kidney disease, chronic obstructive pulmonary disease, heart failure, and cancer. Studies have found that patients with preexisting mental health disorders and comorbidities tend to have worse COVID-19 outcomes, including death.

METHODS: We performed a retrospective study on 24 patients (13 males, 11 females) in a psychiatric state hospital who were at least 18 years old, tested positive for COVID-19, and experienced symptoms severe enough to be admitted to Reading Hospital between April 1, 2020 to June 30, 2022. Patients with multiple COVID-19 admissions to the hospital during this period were counted only once if the period between admissions was less than two weeks. RESULTS: The patient population had an average (IQR) age of 57.75 (48-64) and 2.12 (2-3) comorbidities (IQR). The most common psychiatric disorders were schizoaffective disorder (70.8%), schizophrenia (29.2%), and delirium (29.2%). For comorbidities, patients most commonly presented with hypertension (54.2%), chronic kidney disease (41.2%), diabetes (33.3%), and obesity (33.3%) upon admission. The most common psychiatric medications patients were taking as of their admission for COVID-19 were antipsychotics (83.3%), mood stabilizers (54.2%), antidepressants (54.2%), and benzodiazepines (41.7%).

CONCLUSION: Though our study was descriptive in nature, it was meant to shed light on what conditions precipitate the worsening of COVID-19 infection in psychiatric patients that results in hospitalization. We recommend additional studies in order to make a conclusion about the extent to which psychiatric medications result in worse COVID-19 outcomes due to worse comorbidities.

Cardiac Arrest Resuscitation Excellence Project

by Adam Sigal, MD; Allison Atkinson, MS-1 Department of Emergency Medicine, Reading Hospital – Tower Health, West Reading, Pennsylvania

INTRODUCTION: Cardiac arrest throughout the country has a significantly low survival rate and is responsible for the death of over 300,000 people in the United States every year.1,2 Therefore, it is of interest to evaluate the resuscitation methods performed within Reading Hospital and the community to determine variables that impact cardiac arrest outcomes. This can promote discovering ways in which we can improve our quality of care.

METHODS: Retrospective chart reviews were performed looking at patients from Reading Hospital starting in January 2020 to May 2022 who experienced an out of hospital cardiac arrest or cardiac arrest within the emergency department. The charts were selected using a reporting system that collects the data directly after a code narrator is started in the emergency department. Additional data was collected manually and stored in REDCap. Variables investigated included, but were not limited to, whether the arrest was witnessed, presence or absence of bystander CPR, location of cardiac arrest, treatment performed by emergency medical services and the emergency department, survival to hospital admission, survival to hospital discharge, and neurological function status post cardiac arrest. Preliminary data analysis was performed on SAP/Webi BI Tool and Social Science Statistics.

RESULTS: 453 patients who experienced cardiac arrest under the conditions stated above were evaluated. Of these patients, 28.9% survived to admission from the emergency department and a total of 6.2% survived to discharge from the hospital. The percentage of patients who received bystander CPR was calculated to be 33% and some preliminary statistical analysis was performed to further evaluate components within this variable. Chi-square testing was used to compare the survival rates of patients who had witnessed cardiac arrests and received bystander CPR to those who had unwitnessed arrests and did not obtain CPR until the arrival of EMS. Chi-square calculations comparing these variables showed X2 (1, N=143) = 3.2149, p=.07297 (p>.05).3 Lastly, while the etiology of cardiac arrest can be broad, over 50% of the cardiac arrests that occurred within our data collection had cardiac etiology. CONCLUSION: Given the ongoing nature of this project, we will continue to expand our registry to improve statistical power and will look at individual variables to determine if there are system or process changes that we can make to increase survival to hospital discharge. Potential areas of improvement include incorporating the latest research into our resuscitation research and methods in the emergency department as well as conducting community outreach on the importance of bystander-initiated CPR and AED use.

1. Tsao CW, Aday AW, Almarzooq ZI, et al. Heart disease and stroke statistics—2022 update: A report from the American Heart Association. Circulation. 2022;145(8). doi:10.1161/ cir.0000000000001052 2. Sudden cardiac arrest: Causes & symptoms. Cleveland Clinic. https://my.clevelandclinic.org/health/diseases/17522-sudden-cardiacdeath-sudden-cardiac-arrest. Published June 6, 2022. Accessed July 10, 2022. 3. Chi-Square Test Calculator. Social Science Statistics. https://www. socscistatistics.com/tests/chisquare2/default2.aspx. Published 2022. Accessed July 10, 2022.

The Prospective Association Between Breast Arterial Calcifications on Routine Mammography Screening with Coronary Artery Disease and Stroke, A 17-Year Follow-Up

by Jenna Ehlert (MD Candidate, 2025); Matthew Nudy, M.D., Xuezhi Jiang, M.D., and Peter F. Schnatz, DO Drexel University College of Medicine, West Reading, PA Department of OBGYN, Reading Hospital, Reading, PA

INTRODUCTION: The leading cause of death in women in 2018 was cardiovascular disease (CVD).1 However, the current clinical tools used to assess if a woman is high risk underestimate this true value. Incidental findings of breast arterial calcifications (BAC) on mammography screening may be utilized as a tool to routinely assess for risk of CVD. This study aims to determine if this tool is adequate in evaluating the risk after 17 years of prospective follow-up.

METHODS: The prospective cohort consists of women recruited during routine mammography screening between June and August 2004 at one of four radiology practices in Hartford, CT. The only exclusion criterium is male gender. Baseline data was collected about history of CVD and various risk factors including hypertension, hyperlipidemia, diabetes, tobacco use, exercise habits, and family history of CVD. Participants were contacted 17 years after this initial screening for follow-up with a similar risk questionnaire and to determine if they had developed coronary artery disease (CAD) or stroke. This data is currently being collected via mail, email, and phone calls. It will subsequently be correlated with the baseline mammograms that were analyzed for breast arterial calcifications (BAC) in 2004 by one of 21 radiologists who were standardized and blinded to the study. RESULTS: Of the 1,995 participants who were enrolled at baseline, 334 have completed the 17-year follow-up survey, 57 are deceased, and 22 have declined to or were excluded from participating (male gender, transgender). Results are therefore still pending. The previously reported 10-year prospective study collected follow-up data on 1,039 individuals, 10.1% being BAC positive and 89.9% were BAC negative at baseline. Controlling for age and risk factors, BAC positive women were more likely to develop CAD after 10 years compared to BAC negative women (Odds Ratio [OR] 3.76, 95% Confidence Interval [CI] 1.94-7.28, p < 0.001). Additionally, controlling only for age, BAC positive women were more likely to have a stroke after 10 years (OR 5.10, 95% CI 1.82-14.30).

CONCLUSION: The 10-year prospective study showed a significant association between the presence of BAC on routine mammography with an increased risk of developing CAD and stroke. This data was consistent with the 5-year follow-up data from the same cohort. Continued collection and subsequent analyzation of data for the 17-year follow-up will allow us to determine if this correlation has continued.

1 Heron, M., Ph.D. (2021). Deaths: Leading Causes for 2018. National Vital Statistics Reports, 70(4). <https://www.cdc.gov/nchs/data/ nvsr/nvsr70/nvsr70-04-508.pdf>