A Digital Twin Approach for Stroke Risk Assessment in Atrial Fibrillation Patients

Abstract

Atrial fibrillation (AF) patients have a fivefold increased risk of cerebrovascular events, accounting for 15-18% of all strokes. In stroke prevention, the CHA2DS2-VASc is an important score for distinguishing patients with a low-risk of stroke. However, there is currently no specific measure available to determine the level of stroke risk. In this study, we propose a digital twin (DT) model of the left atrium (LA) and the application of computational fluid dynamic simulations (CFD) to improve patient-specific stroke risk assessment. Simulations were run on patient-specific dynamic LA models in sinus rhythm (SR) on three groups of subjects: 10 controls, 10 paroxysmal AF (PAR) and 10 persistent AF (PER). 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). Following larger-scale testing and classification analysis, the proposed approach could be used to improve stroke risk assessment.