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

项目摘要/摘要 左心发育不全综合征(HLHS)以左心发育不良为特征， 美国每年1000名活产儿HLHS新生儿接受三期开胸重建 创造正常血液流经心脏的手术。然而，25%的HLHS Fontan患者 (完成三期手术的幸存者)出现三尖瓣反流，并面临显著的 有死亡和心力衰竭的风险。三尖瓣介入治疗可以治疗瓣膜渗漏，但手术结果 由于缺乏对生物力学的机械洞察力，长期修复的耐久性仍然不是最好的 以及影响三尖瓣功能的形态因素。影像房室成像的前期工作 瓣膜有限元分析为解剖这种关系提供了一个可靠的计算框架 瓣膜结构与其生物力学功能之间的关系。然而，体外培养和动物培养的数量很少。 HLHS的模型。因此，量化HLHS患者的典型三尖瓣组织特性 人口仍然是一个挑战。这限制了有限元分析和患者特定的临床翻译 破坏了用于指导HLHS改进手术决定的计算分析的潜力。 该项目的目标是：1)发现具有代表性的三尖瓣组织特性 在HLHS人群中使用物理信息机器学习，并2)评估两者之间的关系 三尖瓣解剖特征和相关生物力学指标(即瓣叶应力、应变和 接合高度和空隙面积)。我们将确定HLHS子集的三尖瓣瓣叶组织特性 三尖瓣(轻度至轻度反流10例，中度至重度反流10例)和 建立组织常数的经验分布。这将通知内部组织异质性的水平 HLHS人口的这一子集。我们还将确定解剖特征和 应用三维超声心动图衍生有限元对这一亚群HLHS人群的生物力学指标 分析。这将指导定制瓣膜修复的设计，以改善个人的手术结果 病人。 K25候选人吴博士在康奈尔大学完成了结构工程博士学位。建议数 研究和培训计划将为她提供初步的生物医学研究机会，为她准备 翻译心血管科学领域的独立研究生涯。此外，这款K25将为她提供 也有机会在心血管疾病和治疗程序方面培养强大的知识基础 在身临其境的临床环境中，AS扩展了她在高级计算建模技能方面的专业知识。 吴博士出类拔萃的指导团队处于独特的地位，可以指导她完成以下发展 成为计算科学多学科研究的独立研究员和领导者 工程学和心血管科学。

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.