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Peer-reviewed veterinary case report

In silico modeling of transcatheter heart valve oversizing and ellipticity, Part I: Establishing credibility of an advanced model.

Year:
2026
Authors:
Boxwell S et al.
Affiliation:
Department of Biomedical Engineering

Abstract

Transcatheter 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 and hemodynamic 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. In 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-fiber material model for pericardium tissue leaflets and a super-elastic nitinol stent. Calculation 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 high-speed camera and hemodynamic particle image velocimetry testing across multiple THV deployment configurations. In the 'nominal sizing, circular' case, we predicted a similar geometric orifice area (4.35 vs 4.02 cm2), pinwheeling index (2.6% vs 2.7%), stent deflection (1.95 vs 1.76 mm) and flow velocity (1.33 vs 1.27 m/s) to in vitro data. 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.

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Original publication: https://europepmc.org/article/MED/42066381