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

左心发育不全综合征三尖瓣修复的患者特异性计算模型

• 批准号: 10643122
• 负责人: Wensi Wu
• 金额: $ 14.69万
• 依托单位: CHILDREN'S HOSP OF PHILADELPHIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10643122
• 关键词: Address; Affect; Age; Anatomy; Animal Model; Area; Award; Biomechanics; Biomedical Research; Blood; Cardiac; Cardiovascular Diseases; Cardiovascular system; Chest; Child; Childhood; Circulation; Clinical; Communities; Computational Science; Computer Analysis; Computer Models; Data; Development; Doctor of Philosophy; Drug or chemical Tissue Distribution; Elements; Engineering; Environment; Etiology; Exposure to; Extravasation; Failure; Finite Element Analysis; Floods; Functional disorder; Future; Geometry; Goals; Heart; Heart Valve Diseases; Heart Valves; Heart failure; Height; Heterogeneity; Hypoplastic Left Heart Syndrome; Image; Individual; Infant; Intervention; Investigation; Learning; Left; Machine Learning; Mentors; Methods; Modeling; Morbidity - disease rate; Morphology; Operative Surgical Procedures; Pathology; Patients; Pediatric Hospitals; Pennsylvania; Phase; Philadelphia; Physics; Population; Population Heterogeneity; Positioning Attribute; Procedures; Property; Reconstructive Surgical Procedures; Research; Research Personnel; Risk; Science; Scientist; Series; Severities; Single ventricle congenital heart disease; Stress; Structure; Survivors; Techniques; Technology; Three-Dimensional Echocardiography; Tissues; Training; Translating; Tricuspid Valve Insufficiency; Tricuspid valve structure; Universities; Work; career; clinical translation; cohort; computer framework; computer studies; design; image registration; improved; indexing; individual patient; insight; knowledge base; mortality; mortality risk; multidisciplinary; neonate; novel; prevent; repaired; response; scientific computing; shape analysis; skills; sound; success; surgery outcome; tissue stress; virtual; virtual surgery

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.

项目总结/摘要 左心发育不良综合征（HLHS）是以左心发育不良为特征， 美国每年有1000例活产婴儿接受三期开胸重建 通过外科手术使血液正常流过心脏。然而，25%的HLHS Fontan患者 （完成三阶段手术的幸存者）发生三尖瓣返流， 死亡和心力衰竭的风险。三尖瓣介入治疗可以治疗瓣膜漏，但手术结果 由于缺乏对生物力学的机械见解， 以及影响三尖瓣功能的形态学因素。先前在图像衍生房室传导方面的工作 瓣膜有限元分析提供了一个合理的计算框架， 瓣膜结构与其生物力学功能之间的关系。然而，缺乏离体和动物实验。 HLHS模型因此，量化HLHS中患者的代表性三尖瓣组织特性 人口仍然是一个挑战。这限制了有限元分析的患者特异性临床转化， 破坏了计算分析在指导HLHS中改进手术决策的潜力。 本项目的目标是：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，以及2）评估 三尖瓣解剖特征和相关的生物力学指标（即，瓣叶应力、应变和 接合高度和间隙面积）。我们将确定HLHS亚组的三尖瓣瓣叶组织特性 三尖瓣（n = 10例轻微至轻度返流，n = 10例中度至重度返流）和 建立组织常数的经验分布。这将告知组织内异质性的水平， HLHS人群的这个子集。我们还将确定解剖特征与 使用3D超声心动图衍生的有限元分析HLHS人群这一子集的生物力学指标 分析.这将指导定制瓣膜修复术的设计，以改善个体患者的手术结果。 患者 K25候选人吴博士完成了博士学位。康奈尔大学的结构工程专业拟议 研究和培训计划将为她提供初步接触生物医学研究，因为她准备了一个 在转化心血管科学中的独立研究生涯。此外，K25将为她提供 有机会培养心血管疾病和治疗程序的强大知识基础，以及 在沉浸式临床环境中扩展她在高级计算建模技能方面的专业知识。 博士吴的特殊辅导团队是独一无二的定位，以指导她通过她的发展， 成为计算科学多学科研究的独立研究者和领导者 工程学和心血管科学。