Atrial fibrillation (AF) is associated with a fivefold increased risk of cerebrovascular events, contributing to 15-18 % of all strokes. Stroke prevention in clinical practice is typically guided by the CHA₂DS₂-VASc score, which depends on general clinical risk factors but falls short in predicting risk at an individual patient level. In this study, we introduce a digital twin model of the left atrium (LA) combined with computational fluid dynamics (CFD) simulations to enhance personalized stroke risk assessment. Simulations were performed on patient-specific dynamic LA models in sinus rhythm (SR) across three patient groups: 10 controls (CTRL), 10 with paroxysmal AF (PAR-AF), and 10 with persistent AF (PER-AF). Blood flow velocity and areas susceptible to thrombogenesis, based on several factors including endothelial damage, were identified in the left atrial appendage (LAA). In general, control subjects exhibited higher average blood velocity in both the LAA and its ostium (0.11 ± 0.03 m/s and 0.28 ± 0.05 m/s, respectively) compared to those with AF. In the AF groups, the velocities were lower (LAA: PAR-AF 0.05 ± 0.02 m/s, PER-AF 0.04 ± 0.02 m/s; LAA ostium: PAR-AF 0.14 ± 0.03 m/s, PER-AF 0.11 ± 0.04 m/s). CFD analysis revealed that endothelial cell activation potential (ECAP) was significantly higher in AF patients (PAR-AF: 3.96 ± 3.28 Pa⁻¹, PER-AF: 4.77 ± 2.08 Pa⁻¹) compared to controls (0.93 ± 0.63 Pa⁻¹). These findings suggest that AF patients experience slower and more oscillatory blood flow in the LAA, increasing their risk of thrombosis. Additionally, blood tends to stagnate within the LAA, further raising the likelihood of clot formation. This proposed method could be used to enhance stroke risk stratification in AF patients by incorporating an index that integrates blood velocity-derived parameters.

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