Designing a Novel Asymmetric Transcatheter Aortic Valve for Stenotic Bicuspid Aortic Valves Using Patient-Specific Computational Modeling

Bicuspid aortic valve (BAV), the most common congenital heart malformation, is characterized by the presence of only two valve leaflets with asymmetrical geometry, resulting in elliptical systolic opening. BAV often leads to early onset of calcific aortic stenosis (AS). Following the rapid expansion of transcatheter aortic valve replacement (TAVR), designed specifically for treating conventional tricuspid AS, BAV patients with AS were initially treated off-label with TAVR, which recently gained FDA and CE regulatory approval. Despite its increasing use in BAV, pathological BAV anatomy often leads to complications stemming from mismatched anatomical features. To mitigate these complications, a novel eccentric polymeric TAVR valve incorporating asymmetrical leaflets was designed specifically for BAV anatomies. Computational modeling was used to optimize its asymmetric leaflets for lower functional stresses and improved hemodynamic performance. Deployment and flow were simulated in patient-specific BAV models (n = 6) and compared to a current commercial TAVR valve (Evolut R 29 mm), to assess deployment and flow parameters. The novel eccentric BAV-dedicated valve demonstrated significant improvements in peak systolic orifice area, along with lower jet velocity and wall shear stress (WSS). This feasibility study demonstrates the clinical potential of the first known BAV-dedicated TAVR design, which will foster advancement of patient-dedicated valvular devices.

Automated summary

Bicuspid aortic valve (BAV) is the most common congenital heart malformation and often leads to early onset of calcific aortic stenosis (AS). Although transcatheter aortic valve replacement (TAVR) has been used to treat BAV patients, complications due to pathological BAV anatomy are common. To address these issues, a novel eccentric TAVR valve specifically designed for BAV anatomy was developed and optimized using computational modeling.