Computational modeling of thrombosis after transcatheter aortic valve implantation: A systematic review of hemodynamic and mechanical risk metrics.

BACKGROUND AND OBJECTIVE: Although transcatheter aortic valve implantation (TAVI) generally provides favorable procedural outcomes, thrombotic complications remain an important concern for the long-term performance and durability of transcatheter heart valves. A subset of patients develops hypo-attenuated leaflet thickening (HALT), reduced leaflet mobility, or late thrombotic complications, often in the absence of overt clinical manifestations. This has motivated the use of computational modeling to investigate post-TAVI hemodynamics and valve mechanics and to identify flow- and device-related factors associated with thrombus formation. METHODS: In this review, we systematically analyze 68 computational studies focused on post-TAVI hemodynamics and thrombosis. The literature is organized into three categories based on their relevance to thrombogenic risk modeling: Peripheral (31 studies), Intermediate (21 studies), and Core (16 studies). This classification reflects the presence of hemodynamic modeling, the use of dedicated proxy metrics of thrombogenicity, and the extent to which thrombosis constitutes the primary focus of the investigation. We further examine the methodological spectrum of existing approaches, including computational fluid dynamics, fluid-structure interaction, and finite element analysis; the use of patient-specific versus idealized geometries; and the application of thrombogenicity-related metrics, such as residence time and washout indices, wall shear stress-based measures, and stress-history-based indicators of mechanical platelet activation. RESULTS: Our study shows that while direct simulation of thrombus formation is still mostly lacking, most published studies use indirect hemodynamic and mechanical proxies to evaluate thrombogenic risk. The existence and geometry of paravalvular leaks, valve implantation depth, asymmetric stent expansion, and patient-specific characteristics of aortic root architecture are the risk factors that are most frequently reported across studies. Notably, research in the Core category shows a distinct move away from descriptive flow analyzes and toward a more mechanistic understanding of thrombogenic risk, connecting the cumulative mechanical exposure of blood constituents, unstable neo-sinus hemodynamics, and implantation parameters. CONCLUSIONS: Overall, this analysis shows that modern in silico methods already make it possible to compare implantation techniques and valve design elements quantitatively in terms of thrombogenic risk. Simultaneously, a shift from exclusively proxy-based evaluations to hybrid and multiscale modeling frameworks that incorporate geometry, hemodynamics, and a minimally adequate description of the biological mechanisms behind thrombus formation will be necessary for future advancements in the field.