Patient Specific Computational Fluid Dynamic in the Left Atrium. Atrial Fibrillation vs Sinus Rhythm

Abstract

Atrial fibrillation (AF) is the most common sustained arrhythmia and a leading cause of stroke, yet the direct hemodynamic consequences of AF remain difficult to isolate in clinical practice. Imaging captures anatomy, and ECGs record rhythm, but neither alone can resolve the full, three-dimensional, time-resolved blood flow patterns that link AF to clot formation.This study develops a patient-specific computational framework to address this limitation. Critically, we analyze the same patients under both sinus rhythm (SR) and AF, enabling direct comparison of how rhythm alone alters atrial flow. Using imaging and ECG data, we reconstruct left atrial (LA) geometries, incorporate motion across the cardiac cycle, and simulate blood flow to evaluate changes in transport dynamics and stasis between the two conditions. The comparisons reveal clear hemodynamic signatures of AF: reduced atrial emptying, irregular contraction, and disrupted flow organization, all of which contribute to conditions favorable for thrombus formation. At the same time, differences in atrial appendage morphology and rhythm-specific electrical patterns highlight the patient-to-patient variability that shapes risk. By directly contrasting SR and AF within the same individuals, this framework bridges a critical gap between clinical observation and mechanistic understanding. It shows how computational modeling can uncover hidden flow dynamics inaccessible to imaging alone and offers a foundation for personalized assessment of AF-related stroke risk.