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% ( p 0.05). Anatomical features, including superior-to-inferior vena cava distance, distinguished organized from disorganized patterns, while electrophysiological biomarkers alone did not. Combined high anatomical and electrophysiological marker values were linked to greater inducibility during AF progression (+20%). These findings provide mechanistic evidence for the role of atrial biomarkers in AF initiation and maintenance, highlighting the value of virtual populations in capturing inter-patient variability. By linking structural and electrophysiological characteristics to arrhythmic risk, this approach supports improved patient stratification and the design of individualized therapeutic strategies for AF.