Programa del congreso

Left atrial appendage occlusion (LAAO) devices are effective in reducing stroke risk in atrial fibrillation (AF) patients, but device-related thrombosis (DRT) remains a serious complication. In-silico modeling provides a valuable tool for studying the underlying hemodynamic mechanisms, although the accuracy of predictions depends strongly on modeling assumptions. This study presents the first large-scale uncertainty quantification framework to systematically assess the sensitivity of computational fluid dynamics (CFD) simulations to variations in modeling settings for addressing DRT risk, including LAAO devices. A dataset of 50 AF patients was used, with two implanted occluders (Amplatzer Amulet and Watchman FLX) virtually positioned in two configurations relative to the pulmonary ridge. For each patient-specific case, seven modeling conditions were tested, including Newtonian versus non-Newtonian blood rheology, inclusion of the A-wave in the mitral profile, scaled outlet velocities, increased pulmonary vein pressure, and rigid versus dynamic wall motion. In total, 1,400 CFD simulations were performed. Preliminary analysis of a representative case showed that AF conditions led to reduced velocities near the device, while inclusion of the A-wave increased velocity magnitude and complexity. Outlet velocity scaling had the strongest influence on flow dynamics, particularly in recirculating regions around the device. In contrast, non-Newtonian rheology and pressure changes had minimal impact on global velocity fields, though localized effects were noted. These findings highlight the critical role of boundary condition assumptions in in-silico DRT risk prediction and underscore the importance of systematic sensitivity analysis for reliable clinical translation.