Finite Element Analysis of Self Expanding Transcatheter Aortic Valves reveals Increased Stent contact Pressure is Associated with Higher Conduction Abnormality Risk

Abstract

For transcatheter aortic valve implantation (TAVI), selecting optimal valve size and implantation depth within aortic root is crucial for preventing complications, such as conduction problems and paravalvular leak. Computational modeling offers comprehensive assessment of patient-specific TAVI scenarios at pre-operative stage enabling calculation of aortic wall mechanical stresses and deformations from the implanted valve. Here, our objective is to develop patient-specific finite element computational modeling approaches for comprehensive biomechanical assessment of TAVI to investigate correlations between post-TAVI aortic wall mechanics and post-TAVI complications. This investigation is in collaboration with Hamad Heart Hospital TAVI program in Qatar where Medtronic Core Valve is the most commonly used TAV, hence we focused on this valve type. A total of 54 retrospective cases were analyzed. Among the study cohort, 24 patients received 26mm valve, 23 patients received 29mm valve, and 7 patients received 34mm valve. ABAQUS finite element analysis (FEA) software was used for complete TAVI simulation operation. For each TAVI case, FEA revealed biomechanical stress and deformation patterns on aortic root. Studied parameters are average and maximum values of contact pressure, von mises stresses, and radial displacements. Studied cases were grouped into cases that experienced complications post-TAVI and cases that did not experience complications post-TAVI. Using ABAQUS, we successfully simulated complete TAVI implantation and carried out FEA for aortic root stress and deformation patterns for studied cases. Statistical analysis revealed that TAVI contact pressure was higher for cases suffering from conduction abnormalities, suggesting stent pressure is an important factor influencing conduction abnormalities experienced post-TAVI. FEA is a powerful approach for simulating TAVI and predicting possible post-TAVI complications, and advancing such models will enhance therapy planning for TAVI therapy.