Benjamin S Abella
· Assistant ProfessorVerifiedUniversity of Pennsylvania · Rehabilitation Medicine
Active 1990–2026
About
Benjamin S Abella, MD, MPhil, is an Adjunct Professor of Emergency Medicine at the Perelman School of Medicine at the University of Pennsylvania. He is an attending physician in the Department of Emergency Medicine and serves as Vice Chair for Research in the same department. Dr. Abella is also an Associate Scholar at the Center for Clinical Epidemiology and Biostatistics and the Medical Director of the Penn Acute Research Collaboration (PARC). His research focuses on sudden cardiac arrest, a leading cause of death in the United States, with projects including the evaluation of CPR and resuscitation performance, testing new community CPR teaching methods, prognostication of neurologic outcomes after cardiac arrest, and improving post-arrest care and outcomes. He is the developer and Medical Director of the Penn TTM Academy, a training course for post-arrest care and targeted temperature management. Dr. Abella has published extensively in professional journals, contributed to textbook chapters, and participated in developing international CPR guidelines. His work is supported by funding from NIH, PCORI, and industry sources, and he is actively involved in global resuscitation science initiatives.
Research topics
- Medicine
- Cardiology
- Internal medicine
- Political Science
- Virology
- Intensive care medicine
- Chemistry
- Biology
- Psychology
- Molecular biology
- Emergency medicine
- Genetics
- Law
- Anesthesia
- Pathology
- Medical emergency
- Surgery
- Computational biology
Selected publications
Injury · 2026-03-01
articleNeighborhood poverty and rates of witnessed out-of-hospital cardiac arrest (OHCA)
Resuscitation · 2026-01-02
articleOpen accessSenior authorResuscitation · 2025-05-16 · 2 citations
articleOpen accessJournal of the American Heart Association · 2025-02-19 · 2 citations
articleOpen accessBACKGROUND: Social determinants of health such as residential segregation have been identified as drivers of disparities in health outcomes; however, this has been understudied for out-of-hospital cardiac arrest (OHCA). We sought to examine whether there were differences in survival to discharge and survival with good neurological outcome, as well as likelihood of bystander cardiopulmonary resuscitation, using validated measures of racial, ethnic, and economic segregation. METHODS: We conducted a retrospective observational study using data from the Cardiac Arrest Registry to Enhance Survival data set. The primary predictor for this study was the Index of Concentration at the Extremes. The primary outcomes were survival to discharge and survival with good neurological status. RESULTS: During the study period, 626 264 had an out-of-hospital cardiac arrest, and patients had a mean age of 62 years (SD 17.2 years). In multivariable models, we observed an increased likelihood of survival to discharge and survival with good neurological outcome for those patients residing in more highly segregated predominately White population and higher-income census tracts as compared with more highly segregated and lower-income Black and Hispanic/Latinx population census tracts. We found that the magnitude of this disparity was 24% for the outcome of survival to discharge as compared with reference (relative risk,1.24 [95% CI, 1.20-1.28]). CONCLUSIONS: This research suggests that areas impacted by residential and economic segregation are important targets for both public policy interventions as well as addressing disparities in care across the chain of survival for out-of-hospital cardiac arrest.
medRxiv · 2025-04-08 · 3 citations
preprintOpen accessBackground: Emergency department (ED) crowding strains patient care and drives up costs. Early decisions on the need for patient hospital admissions can allow for better planning and potentially improve throughput and alleviate crowding. We sought to prospectively compare nurse predictions with a machine learning (ML) model for hospital admissions and to evaluate whether adding the nurse prediction to ML outputs enhances predictive performance. Methods: In this prospective, observational study at six hospitals in a large mixed quarternary/community ED system (annual ED census ~500,000), triage nurses recorded a binary admission prediction for adult patients. These predictions were compared with an ensemble ML model (XGBoost + Bio-Clinical BERT) trained on structured data (demographics, vital signs, medical history) and triage text. Nurse predictions were similarly analyzed and then integrated with the ML output to assess for improvement in predictive accuracy. Results: The ensemble ML model (XGBoost + Bio-Clinical BERT) was trained on 1.8 million historical ED visits (January 2019-December 2023). It was then tested on 46,912 prospective ED visits with recorded nurse predictions (September to October 2024). In the prospective arm, nurse predictions yielded an accuracy of 81.6% (95% CI, 81.3-81.9), sensitivity of 64.8% (63.7-65.8), and specificity of 85.7% (85.3-86.0). At a 0.30 probability threshold, the ML model attained an accuracy of 85.4% (85.0-85.7) and sensitivity of 70.8% (69.8-71.7). Combining nurse predictions with the ML output did not improve accuracy beyond the model alone. Conclusions: Machine learning-based predictions outperformed triage nurse estimates for hospital admissions. These findings suggest that an admission prediction system anchored by ML can perform reliably using data available at triage.
Journal of the American Heart Association · 2025-09-19
articleOpen accessCirculation · 2025-11-03
articleBackground: Intra-arrest transesophageal echocardiography (TEE) is increasingly used during in-hospital cardiac arrest (IHCA) to guide resuscitation with evidence that directing the area of maximal compression (AMC) may improve outcomes. While TEE offers potential benefits for real-time decision-making, limited data exist regarding the impact of intra-arrest TEE on standard CA resuscitative efforts. We analyzed the Resuscitative TEE Collaborative Registry (rTEECoRe), a multicenter registry of acute care TEE, to evaluate the safety profile of intra-arrest TEE. We examined the temporal relationship of TEE-guided AMC evaluation attempt with the delivery of standard epinephrine administration. Methods: We analyzed IHCA patients evaluated with TEE collected through the Resuscitative TEE Collaborative Registry (NCT04972526). Patients in whom TEE was used to evaluate the AMC and those without AMC evaluation were included for analysis. We investigated the association between the attempt to identify the AMC and the timing of standard resuscitation interventions, specifically the time to first epinephrine administration. Linear regression was used to assess the relationship between AMC evaluation attempt and time to first epinephrine. Statistical significance was set at p < 0.05. Results: Among 117 patients who received intra-arrest TEE during in-hospital cardiac arrest, attempts to evaluate the AMC were not associated with significant delays in the administration of the first epinephrine dose (p = 0.114). In logistic regression analysis, AMC evaluation attempt was also not associated with return of spontaneous circulation (ROSC) (odds ratio [OR] 1.53, 95% confidence interval [CI] 0.59–4.11; p = 0.39) or survival to hospital discharge (OR 0.54, 95% CI 0.15–2.06; p = 0.34). Other covariates, including age, sex, CPR modality, initial rhythm, and arrest location, were not significantly associated with either outcome. Conclusion: In this multicenter cohort of patients undergoing intra-arrest TEE during IHCA, evaluation of AMC was not associated with delays in epinephrine administration or with differences in ROSC or survival to discharge. These findings support the idea that focused intra-arrest TEE imaging, including the assessment of AMC, can be performed without compromising the timely delivery of standard resuscitation interventions.
Mayo Clinic Proceedings Digital Health · 2025-07-15 · 2 citations
articleOpen accessObjective: To prospectively compare nurse predictions with a machine learning (ML) model for hospital admissions and evaluate whether adding the nurse prediction to ML outputs enhances predictive performance. Patients and Methods: In this prospective, observational study at 6 hospitals in a large mixed quaternary/community emergency department (ED) system (annual ED census ∼500,000), triage nurses recorded a binary admission prediction for adult patients. These predictions were compared with an ensemble ML model (XGBoost + Bio-Clinical BERT) trained on structured data (demographics, vital signs, and medical history) and triage text. Nurse predictions were similarly analyzed and then integrated with the ML output to assess for improvement in predictive accuracy. Results: The ensemble ML model (XGBoost + Bio-Clinical BERT) was trained on 1.8 million historical ED visits (January 2019 to December 2023). It was then tested on 46,912 prospective ED visits with recorded nurse predictions (September 1, 2024 to October 31, 2024). In the prospective arm, nurse predictions yielded an accuracy of 81.6% (95% CI, 81.3-81.9), a sensitivity of 64.8% (63.7-65.8), and a specificity of 85.7% (85.3-86.0). At a 0.30 probability threshold, the ML model attained an accuracy of 85.4% (85.0-85.7) and a sensitivity of 70.8% (69.8-71.7). Combining nurse predictions with the ML output did not improve accuracy beyond the model alone. Conclusion: Machine learning-based predictions outperformed triage nurse estimates for hospital admissions. These findings suggest that an admission prediction system anchored by ML can perform reliably using data available at triage.
Critical Care Medicine · 2025-11-12 · 1 citations
articleOBJECTIVES: Acute respiratory distress syndrome (ARDS) represents a significant complication in trauma patients. Yet the epidemiology of ARDS in trauma remains incompletely characterized. We sought to define trends in ARDS frequency and the effect of temporal, patient, and center-level factors on outcomes with the hypothesis that ARDS independently predicts mortality. DESIGN: Retrospective cohort study. SETTING: Hospitals submitting data to the American College of Surgeons National Trauma Data Bank. PATIENTS: Injured patients 18 years old or older from 2007 to 2019 on mechanical ventilation (MV) for greater than or equal to 2 days were included, and patients with ARDS were compared with those without ARDS. A subgroup with transfusion data was also identified. Multivariable logistic regression models by year adjusted for patient demographics, center characteristics, and blood products identified factors independently associated with ARDS diagnosis and 30-day hospital mortality. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 384,032 injured patients on MV, ARDS was documented in 29,359 (8 per 100 MV patients) with a significant decrease over the study period (22 in 2007 vs. 3 in 2019, p < 0.001). Patient-level risk factors independently associated with ARDS were blunt injury (odds ratio [OR] 1.25; 95% CI, 1.20-1.30), severe sepsis (OR 2.16; 95% CI, 2.06-2.27), ventilator-associated pneumonia (OR 2.91; 95% CI, 2.82-3.00), and acute kidney injury (AKI, OR 2.98; 95% CI, 2.85 to 3.12). In the transfusion subset, 24-hour plasma (OR 1.02; 95% CI, 1.01-1.04) and platelets (OR 1.03; 95% CI, 1.02-1.05) were independently associated with ARDS. Crude ARDS mortality increased over the study period (2007, 15.1% vs. 2019, 29.7%, p < 0.001), and after adjusting for significant differences, ARDS was independently associated with 30-day hospital mortality (OR 1.32; 95% CI, 1.27-1.37). Independent risk factors for 30-day mortality in patients with ARDS included head injury (OR 1.54; 95% CI, 1.43-1.66), severe sepsis (OR 1.48; 95% CI, 1.34-1.63), and AKI (OR 2.72; 95% CI, 2.50-2.96). Patients with ARDS managed in Prevention and Early Treatment of Acute Lung Injury and the Extracorporeal Life Support Organization centers were less likely to die (OR 0.78; 95% CI, 0.72-0.84). CONCLUSIONS: From 2007 to 2019, ARDS decreased significantly in trauma patients. Over the same time, mortality increased to nearly 30%, and after adjusting for other risks factors, ARDS was strongly associated with 30-day mortality. Future studies should examine modifiable patient and center-level factors to improve mortality in these high-risk patients.
Critical Care Clinics · 2025-09-14
article
Recent grants
NIH · $674k · 2011
Measuring post-arrest neurologic injury via nanofluidic assay of brain-derived exosomal RNA
NIH · $443k · 2019–2023
NIH · $2.2M · 2016
Frequent coauthors
- 1039 shared
Kathryn M. Beauchamp
Biomedical Research Institute
- 631 shared
Andrew M. Morris
- 613 shared
Jonathan R. Egan
- 573 shared
Marino S. Festa
Children's Hospital at Westmead
- 537 shared
Richard D. Shih
Florida Atlantic University
- 409 shared
Judd E. Hollander
Thomas Jefferson University
- 399 shared
Claudio Ronco
Ospedale San Bortolo
- 384 shared
Tom Lim
Education
- 1992
B.A., Biochemistry
Washington University
- 1993
Other, Genetics
Cambridge University
- 1998
M.D.
Johns Hopkins School of Medicine
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