Applying a Digital Twin Framework for Stroke Risk Evaluation in Atrial Fibrillation Patients

Atrial fibrillation (AF) carries a fivefold higher risk of cerebrovascular events, accounting for 15-18% of all strokes.Stroke prevention in clinical practice is based on the CHA2DS2-VASc score, which relies on general clinical risk factors and lacks enough predictive accuracy at the patient-specific level.In this study, we propose a digital twin model of the left atrium (LA) and the application of computational fluid dynamic (CFD) simulations to improve patient-specific stroke risk assessment.Simulations were performed on patient-specific dynamic LA models in sinus rhythm on three groups of subjects: 10 controls, 10 paroxysmal AF subjects and 10 persistent AF subjects.Blood velocity and regions prone to thrombogenesis based on endothelial damage were all measured in both the LA chamber and the left atrial appendage (LAA).Furthermore, the proposed approach could be used to stratify stroke risk assessment in AF patients based on a unique index integrating blood velocity derived parameters.