Toward Patient-Specific Computational Modeling of Tricuspid Valve Repair in Hypoplastic Left Heart Syndrome

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PROJECT SUMMARY/ABSTRACT Hypoplastic left heart syndrome (HLHS) is characterized by maldevelopment of the left heart and affects over 1000 live-born infants annually in the U.S. Neonates with HLHS undergo three staged open-chest reconstruction surgeries to create normal blood flood through the heart. However, twenty-five percent of HLHS Fontan patients (survivors who completed the three-staged surgeries) develop tricuspid regurgitation and are facing a significant risk of death and heart failure. Tricuspid valve intervention may treat valve leakage, but the surgical outcomes and long-term repair durability remain suboptimal due to the lack of mechanistic insights into the biomechanical and morphological factors that influence the tricuspid valve function. Prior work on image-derived atrioventricular valve finite element analysis has offered a sound computational framework for dissecting the relationship between valve structure and its biomechanical function. There is, however, a paucity of ex vivo and animal models of HLHS. As such, quantifying representative tricuspid valve tissue properties for patients in the HLHS population remains a challenge. This limits patient-specific clinical translation of finite element analysis and undermines the potential of computational analysis for guiding improved surgical decisions in HLHS. The objectives of this proposed project are to 1) discover representative tricuspid valve tissue properties in the HLHS population using physics-informed machine learning, and 2) to evaluate the relationship between tricuspid valve anatomic feature and the associated biomechanical indices (i.e., leaflet stress, strain, and coaptation height and gap area). We will identify the tricuspid valve leaflet tissue properties for a subset of HLHS tricuspid valves (n = 10 with trivial to mild regurgitation, n = 10 with moderate to severe regurgitation) and establish an empirical distribution of the tissue constants. This will inform the level of tissue heterogeneity within this subset of the HLHS population. We will also identify the association between anatomic features and biomechanical indices for this subset of the HLHS population using 3D echocardiography-derived finite element analysis. This will guide the design of customized valve repairs to improve surgical outcomes for individual patients. K25 Candidate Dr. Wu completed a Ph.D. in Structural Engineering at Cornell University. The proposed research and training plan will provide her with an initial exposure to biomedical research as she prepares for an independent research career in translational cardiovascular science. Further, this K25 will offer her the opportunity to cultivate a strong knowledge base in cardiovascular disease and treatment procedures, as well as expand her expertise in advanced computational modeling skills, within an immersive clinical environment. Dr. Wu’s exceptional mentoring team is uniquely positioned to guide her through her development toward becoming an independent investigator and leader in the multidisciplinary study of computational science engineering and cardiovascular science.