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In Silico Modeling of Transcatheter Heart Valve Oversizing and Ellipticity, Part I: Establishing Credibility of an Advanced Model

Boxwell, S.; Armfield, D.; Cahalane, R. M. E.; Hickey, W.; Cook, S.; Kelly, P.; Cardiff, P.; McNamara, L.

2025-09-27 cardiovascular medicine
10.1101/2025.09.25.25336634 medRxiv
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Background and ObjectivesTranscatheter aortic valve implantation (TAVI) is the most common modality of treatment for aortic stenosis. However, transcatheter heart valves (THVs) can be prone to early failure and an increase in thrombogenic events, yet the risk factors associated with these failure modes remain poorly understood. Computational modeling may be used to predict biomechanical indices associated with degeneration and thrombogenicity, however existing models do not fully account for complex stent and leaflet material behavior, and establishing model credibility according to ASME VV-40 is required. MethodsIn this study, we developed an advanced structural and hemodynamic in silico framework to predict the in vitro performance of a supra-annular, self-expanding THV across a range of clinically-relevant expansion and ellipticity indices. The THV was modelled by incorporating a novel 3-fibre material model for pericardium tissue leaflets and a super-elastic nitinol stent. ResultsCalculation verification was conducted and, on this basis, we provide recommendations on mesh density, element integration and target time increment. Following verification, we validated our models with radial force, structural camera and hemodynamic particle image velocimetry testing across multiple THV deployment configurations. In the min-oversizing, circular case, we predicted a similar geometric orifice area (4.35 vs 4.02cm2), pinwheeling index (2.6% vs 2.7%), stent deflection (1.95 vs 1.76mm) and flow velocity (1.33 vs 1.27m/s) to in vitro data. ConclusionThus, we validated a novel structural and hemodynamic in silico framework for studying THVs, which will be applied to understand deployment factors contributing to structural degeneration and thrombogenicity. This framework also holds potential for guiding next-generation THV design and predictive procedural modeling.

Published in Computer Methods and Programs in Biomedicine (predicted rank #3) · training set

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