Virtual population-based analysis of anatomical and electrophysiological risk markers for atrial fibrillation.

Atrial fibrillation (AF) prevalence has increased markedly over the past two decades, underscoring the need for personalized approaches to diagnosis, risk stratification, and therapy. Computational modeling provides a framework to integrate patient-specific anatomy and electrophysiology, offering mechanistic insights into arrhythmia susceptibility. We analyzed a virtual population of 160 bi-atrial models to investigate how structural and electrophysiological markers contribute to AF vulnerability and patterns. Each virtual patient incorporated geometries with varying enlargement, structural (conduction velocity, CV), and electrical (action potential duration, APD) remodeling. Virtual patients were characterized through anatomical markers, defined as inter-landmark distances, and electrophysiological markers, such as total activation time. AF vulnerability was tested by programmed stimulation from multiple atrial sites in 800 simulations. Lower CV and APD increased arrhythmic incidence, consistent with prior work. Larger atrial lateral extent and longer Bachmann's bundle length raised AF initiation likelihood by ∼20% (