Credibility assessment of patient-specific modeling in transcatheter aortic valve implantation. I. A population-based validation of patient-specific modeling
- Authors: Catalano, C.; Scuoppo, R.; Turgut, T.; Bouwman, V.; Götzen, N.; Cannata, S.; Gentile, G.; Gandolfo, C.; Pasta, S.
- Publication year: 2025
- Type: Articolo in rivista
- OA Link: http://hdl.handle.net/10447/691185
Abstract
Transcatheter aortic valve implantation (TAVI) has become a standardized treatment for aortic valve stenosis, supported by computational modeling to enhance procedural planning. However, the credibility of TAVI simulations requires rigorous validation following regulatory standards. This study aims to perform a population-based validation of the structural and hemodynamic simulation performance of the SAPIEN 3 (S3) Ultra device by comparing computational predictions with clinical post-TAVI data, following the ASME V&V 40 framework. A patient-specific structural model followed by fluid-structure interaction was developed to simulate S3 deployment and then assess post-TAVI hemodynamics in 20 patients. Structural parameters (device diameters) and hemodynamic indices (effective orifice area, EOA, and transmural pressure gradient, TPG) were extracted. Validation was performed using empirical cumulative distribution function (ECDF) analysis, with an acceptance threshold of 5% for model credibility. EOA and TPG predictions showed reasonable agreement with echocardiographic data (errors within 10%). ECDF-based comparison demonstrated a high level of accuracy for device diameters (<= 5% area metric), whereas hemodynamic parameters exhibited slightly greater discrepancies, potentially due to clinical measurement variability. This study establishes a robust computational validation framework for patient-specific TAVI modeling, ensuring regulatory compliance and clinical applicability. These findings highlight the potential of in silico trials to support TAVI planning and decision-making. This study is complemented by a second part dedicated to uncertainty quantification and sensitivity analysis.
