4.5 Article

Assessment of Paravalvular Leak Severity and Thrombogenic Potential in Transcatheter Bicuspid Aortic Valve Replacements Using Patient-Specific Computational Modeling

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SPRINGER
DOI: 10.1007/s12265-021-10191-z

关键词

Bicuspid aortic valve (BAV); Computational fluid dynamics (CFD); Paravalvular leakage (PVL); Patient-specific computational modeling; Thrombogenicity; Transcatheter aortic valve implantation (TAVI); Transcatheter aortic valve replacement (TAVR)

资金

  1. National Institutes of Health-National Institute of Biomedical Imaging and Bioengineering [U01EB026414-01]

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This study utilized patient-specific computational modeling to assess post-TAVR complications in BAV patients, finding a significant decrease in post-implantation paravalvular leak and thrombogenic potential with the latest generation TAVR devices. This highlights the potential of simulations in pre-procedural planning for better clinical outcomes and device selection.
Bicuspid aortic valve (BAV), the most common congenital valvular abnormality, generates asymmetric flow patterns and increased stresses on the leaflets that expedite valvular calcification and structural degeneration. Recently adapted for use in BAV patients, TAVR demonstrates promising performance, but post-TAVR complications tend to get exacerbated due to BAV anatomical complexities. Utilizing patient-specific computational modeling, we address some of these complications. The degree and location of post-TAVR PVL was assessed, and the risk of flow-induced thrombogenicity was analyzed in 3 BAV patients - using older generation TAVR devices that were implanted in these patients, and compared them to the performance of the newest generation TAVR devices using in silico patient models. Significant decrease in PVL and thrombogenic potential was observed after implantation of the newest generation device. The current work demonstrates the potential of using simulations in pre-procedural planning to assess post-TAVR complications, and compare the performance of different devices to achieve better clinical outcomes.

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