Personalized computational hemodynamic analysis in transcatheter aortic valve: investigation of long-term degeneration

Introduced as an alternative to open-heart surgery for elderly patients, Transcatheter Aortic Valve Implantation (TAVI) has recently been extended to younger patients due to comparable performance with the gold-standard. However, the long-term durability of the bio-prosthetic TAVI valves is limited by Structural Valve Deterioration (SVD), an inevitable degenerative process whose pathogenesis is still unclear. In this study, we aim to computationally investigate a possible relation between aortic hemodynamics and SVD development. To this aim, we collect data from twelve patients with and without SVD at long-term follow-up exams. Starting from pre-operative clinical images, we build early post-operative virtual scenarios and we perform Computational Fluid Dynamics simulations by prescribing a personalized flow rate based on Echo Doppler data. In order to identify a premature onset of SVD, we propose three computational hemodynamic indices: Wall Damage Index (WDI), Leaflet Delamination Index (LDI), and Leaflet Permeability Index (LPI). Additionally, to each index we associate a score and, using the Wilcoxon rank-sum test, we find that each score individually shows a statistically greater median value in the SVD sub-population (WDI: p=0.008, LDI: p=0.001, LPI: p=0.020). Finally, we define a synthetic scoring system that clearly separates between SVD and non-SVD patients. Our results suggest that aortic hemodynamics may drive a premature onset of SVD, and the synthetic score could potentially assist clinicians in a patient-specific planning of follow-up exams to closely monitor those patients at high SVD risk.