Probabilistic Modeling of Stenotic Aortic Valve Intervention

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DESCRIPTION (provided by applicant): Project Summary/Abstract Aortic stenosis is the most common valvular heart disease in the Western world and its prevalence is growing with an aging population. The current preferred method of treatment is complete valve replacement with a surgically implanted prosthetic valve. However, for high-risk patients with advanced age and co- morbidities, operative mortality escalates. Recently, minimally invasive transcatheter aortic valve (TAV) implantation has been investigated as an endovascular alternative to surgical valve replacement. Although significant experience has been gained, TAV implantation clinical trials have been associated with complications such as device migration, paravalvular leakage, coronary obstruction, and access site injury. Furthermore, the long-term durability and safety of TAV prostheses are largely unknown and must be evaluated and studied carefully. To gain a quantitative understanding of the biomechanics involved in TAV intervention, our objectives in this project are to develop probabilistic computational models to investigate aortic tissue-TAV structural interaction and hemodynamics under a variety of patient conditions, and to offer scientific rationale for TAV patient screening and TAV design improvement. To accomplish these goals, the following specific aims are proposed: 1) Investigation of elastic properties and microstructure of the human stenotic aortic root through a series of biomechanical tests performed on 50 human cadaver hearts; 2) Image analysis of clinical CT scans, which will yield 60 reconstructed patient-specific aortic valve geometries. Statistical shape models will be developed to facilitate the reconstruction process as well as the description of anatomic geometric variation among the patient population; and 3) Probabilistic computational analysis of aortic tissue-TAV structural interaction and hemodynamics. Deterministic finite element (FE) models and computational fluid dynamics (CFD) models will be developed using 12 actual TAV patient data measured prior to the TAV intervention, and validated by the post-TAV clinical CT scans, flow and pressure measurements. A statistical description of patient material properties and geometric variations will be mapped into the computational models and a probabilistic analysis will be conducted to evaluate aortic tissue-TAV structural interaction and hemodynamics. The fundamental study of the biomechanics involved in TAV intervention and the computational modeling of tissue-implant interaction proposed here could lead to the development of a new knowledgebase that has been previously unavailable to academia, clinicians, and the heart valve industry. The methodologies and computational framework developed in this study will serve as a basis for future studies, which will include more design and environmental variables such as different patient demographics, and could also be utilized to facilitate the development of other novel device designs or pre-operative patient screening techniques for different valve diseases, such as mitral valve regurgitation.