Novel Cardiac MRI-Based Predictors for Tetralogy of Fallot: Deformation, Kinematic, and Geometric Analyses

基于心脏 MRI 的新型法洛四联症预测因子：变形、运动学和几何分析

• 批准号: 10352409
• 负责人: Animesh Tandon
• 金额: $ 17.27万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-15 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10352409
• 关键词: Address; Adult; Adverse event; Affect; Age; Arrhythmia; Biomedical Engineering; Cardiac; Cardiac development; Cardiology; Cardiovascular system; Cessation of life; Characteristics; Clinical; Clinical Research; Computer Models; Data; Data Analytics; Data Set; Death Rate; Development; EFRAC; Environment; Excess Mortality; Failure; Funding; Geometry; Goals; Guidelines; Heart Diseases; Heart failure; Image; Interdisciplinary Study; Intervention; Lead; Learning; Left; Left Ventricular Ejection Fraction; Left Ventricular Function; Machine Learning; Magnetic Resonance Imaging; Measures; Mechanics; Mentors; Mission; Modeling; Morbidity - disease rate; Motion; National Heart, Lung, and Blood Institute; Obstruction; Operative Surgical Procedures; Outcome; Patient-Focused Outcomes; Patients; Pediatric cardiology; Performance; Population; Positioning Attribute; Predictive Value; Prospective Studies; Prospective cohort; Public Health; Pulmonary Valve Insufficiency; Randomized Controlled Trials; Recording of previous events; Research; Research Design; Research Personnel; Right Ventricular Hypertrophy; Risk; Shapes; Techniques; Testing; Tetralogy of Fallot; Time; Training; Ventricular; Ventricular Fibrillation; Ventricular Tachycardia; Work; adverse outcome; base; cardiac magnetic resonance imaging; career; clinical practice; cohort; computer science; congenital heart disorder; design; early experience; experience; image processing; improved; improved outcome; kinematics; multidisciplinary; novel; outcome prediction; predict clinical outcome; predicting response; premature; prospective; pulmonary valve replacement; repaired; responders and non-responders; response; skills; standard of care; structural heart disease; tool; vector

Five to 10% of patients with repaired tetralogy of Fallot (rTOF) die before age 30, but our ability to predict which patients will experience death, ventricular tachycardia, and ventricular fibrillation (DVTF) is limited. The optimal timing of pulmonary valve replacement (PVR), which may delay DVTF, is also not clear. The current best predictors of DVTF and guidance for PVR timing rely on “traditional” measures such as right ventricular volume and ejection fraction, which are derived from cardiac MRI (CMR). However, even the best DVTF models have limited predictive power, and these “traditional” volumetric measures fail to predict appropriate response to PVR for 30-40% of patients. This proposal aims to address the critical need for CMR based- metrics that correlate with DVTF and predict response to PVR better than traditional ventricular volumetrics. This will be accomplished through the development of ventricular deformation-, kinematic-, and geometry- based mechanics metrics for rTOF patients from routinely acquired, standard of care CMR datasets, which would allow rapid implementation in clinical practice. The critical need will be addressed through two Specific Aims. Specific Aim 1: Develop and evaluate novel CMR-based predictors of clinical outcomes in patients with rTOF. Specific Aim 2: Prospectively assess ventricular geometry-based predictors of response to pulmonary valve replacement in rTOF patients. The rationale is that if computational modeling techniques can generate metrics that outperform traditional markers, they can be used to change current patient management with the eventual goal to delay DVTF. The failure to develop improved metrics will lead to continued excess mortality and suboptimal clinical outcomes for patients with rTOF. The combination of cross-sectional and longitudinal approaches allows a more comprehensive assessment of CMR metrics in a population where randomized controlled trials are not feasible. This work has the potential for rapid implementation and thus to mark a paradigm shift in the use of computational modeling in clinical cardiology. The candidate’s career goal is to be an independent investigator leading multidisciplinary research teams to develop new, more accurate, and easily applied outcome predictors for congenital heart disease (CHD). This would place him at the nexus of clinical pediatric cardiology, biomedical engineering, and computer science. To achieve this goal, he will learn about machine learning and kinematic analyses, their strengths and pitfalls, and the data characteristics needed for these analyses. He will learn how to bring his findings to clinical practice and design studies using the newly developed metrics. He will then design R01-funded research to prospectively assess the performance of the ventricular mechanical metrics to guide PVR and predict DVTF. This will all be accomplished through a dedicated, multi-disciplinary mentor/advisor team, a supportive academic environment, and didactic and hands-on training. At the completion of this training, the applicant plans to be a world leader in the application of advanced imaging analytics for congenital heart disease.

5 - 10%的法洛四联症（rTOF）修复患者在30岁之前死亡，但我们预测 患者将经历死亡、室性心动过速和心室纤颤（DVTF）的风险是有限的。的 肺动脉瓣置换术（PVR）的最佳时机也不清楚，这可能会延迟DVTF。当前 DVTF的最佳预测因子和PVR时间的指导依赖于"传统"测量，如右心室 容积和射血分数，其源自心脏MRI（CMR）。然而，即使是最好的DVTF 模型的预测能力有限，这些"传统"的体积测量无法预测适当的 30 - 40%的患者对PVR有反应。该提案旨在满足基于- 与DVTF相关的指标，并预测PVR的反应优于传统的心室容积指标。 这将通过心室变形、运动学和几何学的发展来实现。 基于常规采集的标准护理CMR数据集的rTOF患者力学指标， 将允许在临床实践中快速实施。将通过两项具体措施来满足这一迫切需要。 目标。具体目标1：开发和评价新的基于CMR的临床结局预测因子， rTOF。具体目标2：Prophylaxis评估基于心室几何结构的肺动脉高压反应预测因子 rTOF患者的瓣膜置换术。基本原理是，如果计算建模技术可以生成 这些指标优于传统标记，可用于改变当前的患者管理， 最终目的是推迟DVTF。未能制定改进的衡量标准将导致死亡率继续过高 和不理想的临床结果。横向与纵向相结合 方法允许在随机化的人群中更全面地评估CMR指标， 对照试验是不可行的。这项工作有可能迅速实施，从而标志着一个 在临床心脏病学中使用计算建模的范式转变。 候选人的职业目标是成为一名独立的调查员，领导多学科研究团队， 为先天性心脏病（CHD）开发新的、更准确的、易于应用的结局预测因子。这 将把他放在临床儿科心脏病学，生物医学工程和计算机科学的中心。到 为了实现这一目标，他将学习机器学习和运动学分析，它们的优势和缺陷， 这些分析所需的数据特征。他将学习如何将他的发现应用于临床实践 并使用新开发的指标设计研究。然后，他将设计R01资助的研究， 前瞻性评估心室力学指标的性能，以指导PVR和预测DVTF。 这一切都将通过一个专门的、多学科的导师/顾问团队、一个支持性的 学术环境，教学和实践培训。在完成培训后，申请人 计划成为先天性心脏病先进成像分析应用的世界领导者。