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Introduction Aortic stenosis (AS) is characterised by the restriction or narrowing of the aortic valve, causing incomplete valve opening and restriction of blood flow into the aorta. It is the most common valvular heart disease in developed countries, and left untreated has a high mortality rate, with an average survival rate of around 50% at 2 years and 20% at 5 years for severe AS without intervention. 1Transcatheter Aortic Valve Implantation (TAVI) has become a standard treatment for AS, particularly for those deemed high-risk for surgical intervention.1 2 TAVI success is determined, among other factors, by a sufficient reduction in transvalvular pressure gradient and flow velocity and absence of procedural and post-procedural complications or symptoms. However, as evidence builds for TAVI as a suitable treatment option in younger, lower-risk patient demographics, additional factors such as turbulent flow patterns in the aortic root, which may affect long-term hydrodynamic performance, device durability, and thrombus formation in TAVI, hold increasing importance. We aimed to develop a standardised computational pipeline to simulate and analyse turbulent aortic blood flow in individual AS patients undergoing TAVI.Methods Our computational pipeline included automated CT and echocardiographic image processing, patient-specific model generation, flow simulation, and post-processing, standardised across cases. The pipeline is used with a finite element-based solver to generate and analyse turbulent flow patterns pre- and post-TAVI to better understand the factors affecting long-term TAVI performance and durability and make predictions regarding TAVI success.Results Promising results were shown for an initial retrospective study of three patient cases, one of which with post-TAVI images available confirming post-procedural complication ( figure 1). While average vorticity, describing the rotation of the flow, was reduced post-TAVI both close to and downstream of the valve in this case, the turbulent kinetic energy, describing the energy contained in the turbulent fluctuations of the flow, increased significantly, rising from the lowest value across examined cases to the highest, potentially suggestive of a higher risk of turbulence-related device degeneration and/or leaflet thrombosis in this case (figure 2).Conclusions We developed a computational pipeline to simulate and analyse turbulent blood flow patterns in AS patients undergoing TAVI, with promising initial results. By integrating post-TAVI imaging with an analysis of critical flow regions, this study advances the understanding of turbulent hemodynamics underpinning long-term TAVI performance and durability.References Nishimura RA, et al. Circulation 2014;129(23):2440–92.Baumgartner H, et al. Eur Heart J. 2017;38(36):2739–91.Abstract BS05 Figure 1Patient-specific aortic root models and simulated flow patterns, represented by the Q-criterion to visualise vortices in the flow field, for three individual aortic stenosis patientsAbstract BS05 Figure 2Assessment of mean vorticity magnitude and turbulent kinetic energy density (averaged over three simulated heartbeats) in different regions of interest downstream of the aortic valve, for three retrospective clinical cases