Improving transcatheter aortic valve interventional predictability via fluid-structure interaction modelling using...

Area of Science:

• Cardiovascular Engineering
• Biomedical Fluid Dynamics
• Computational Medicine

Background:

• Transcatheter aortic valve replacement (TAVR) is a standard treatment for severe aortic valve stenosis.
• Expanding TAVR indications include degenerated bioprosthetic valves, bicuspid valves, and aortic valve insufficiency.
• Predicting optimal hemodynamics for TAVR valve size, design, and orientation remains challenging.

Purpose of the Study:

• To present a novel patient-specific evaluation framework for predicting aortic valve (AV) performance.
• To utilize high-fidelity fluid-structure interaction (FSI) analysis including the left ventricle and ascending aorta (AAo).
• To assess pre- and post-TAVR dynamics and compare with a virtual de-calcified AV benchmark.

Main Methods:

• Developed a patient-specific computational model incorporating FSI.
• Retrospectively analyzed a patient undergoing 23 mm TAVR.
• Compared hemodynamics of stenosed AV, post-TAVR, and a hypothetical de-calcified AV.

Main Results:

• Model predictions aligned with clinical data.
• Stenosed AV exhibited turbulent flow; TAVR and de-calcified AV showed transitional flow.
• A fivefold reduction in viscous dissipation was estimated with TAVR and de-calcified AV.
• Highest energy loss during TAVR occurred with greatest valve deformation in early systole.

Conclusions:

• Patient-specific modeling frameworks can enhance predictability in AV interventions.
• Optimizing valve opening dynamics may reduce energy loss post-TAVR.
• This approach aids in planning and improving outcomes for TAVR procedures.