Patterns of Hospital Performance in CABG Surgery Outcomes Using Federated Learning Analysis

Thomas M. Menino Convention & Exhibition Center 

Coronary artery bypass grafting (CABG) is performed in nearly 400,000 U.S. patients annually, improving survival and quality of life. However, 30-40% experience complications and 10–15% are re-hospitalized, many preventable with guideline-based care. Current value-based programs are limited by their focus on independent outcomes rather than joint evaluation of related endpoints, inadequate adjustment for patient and social risk factors, and the inability to integrate distributed patient-level data across hospitals. Motivated by these challenges, we develop a federated learning framework that leverages multi-site EHR data to jointly analyze multiple outcomes with robust case-mix adjustment. Our framework first fits a multivariate generalized linear mixed-effects model in a federated manner and then uses resulting estimates to compute directly standardized event rates to assess hospital performance. Validation with simulation studies and real-world CABG cohorts demonstrates the feasibility and utility of our approach while protecting patients' confidentiality, enabling multidimensional hospital performance evaluation using real-world data in distributed research networks.

Federated Learning

Multivariate Methods

Hospital Profiling

Electronic Health Record

Mixed-effects Modeling 

Biometrics Section