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％至10％的法尔洛特修复四症患者（RTOF）在30岁之前死亡，但我们的预测能力 哪些患者将经历死亡，心室心动过速和心室纤颤（DVTF）是有限的。 可能延迟DVTF的肺动脉瓣置换（PVR）的最佳时机也不清楚。电流 DVTF的最佳预测指标和PVR时机指导取决于“传统”措施，例如右心室 体积和射血分数，源自心脏MRI（CMR）。但是，即使是最好的DVTF 模型的预测能力有限，这些“传统”体积措施无法预测适当的 30-40％的患者对PVR的反应。该建议旨在满足基于CMR的关键需求 - 与传统的心室体积相比，与DVTF相关并预测对PVR的反应的指标比传统的响应更好。 这将通过心室变形，运动学和几何形状的发展来实现。 来自经常获得的护理标准CMR数据集的RTOF患者的基于机械指标 将允许在临床实践中快速实施。关键需求将通过两个特定 目标。特定目标1：开发和评估新型基于CMR的基于CMR的临床结果预测因子 rtof。特定目标2：前瞻性评估基于心室几何反应的肺反应预测指标 RTOF患者的瓣膜更换。理由是，如果计算建模技术可以生成 超过传统标记的指标，可以用来改变当前患者管理 最终延迟DVTF的目标。无法开发改进的指标将导致持续过多的死亡率 RTOF患者的临床结果和次优临床结果。横截面和纵向的组合 方法可以在人群中对CMR指标进行更全面的评估 对照试验不可行。这项工作具有快速实施的潜力，从而标记 在临床心脏病学中使用计算建模的范式转移。 候选人的职业目标是成为领导多学科研究团队的独立研究员 为先天性心脏病（CHD）开发新的，更准确，更容易应用的结果预测因子。 将他置于临床儿科心脏病学，生物医学工程和计算机科学的联系。到 实现这一目标，他将了解机器学习和运动学分析，其优势和陷阱，以及 这些分析所需的数据特征。他将学习如何将他的发现带入临床实践 以及使用新开发的指标的设计研究。然后，他将设计为R01资助的研究 前瞻性评估心室机械指标的性能，以指导PVR并预测DVTF。 这一切都将通过一个专门的，多学科的导师/顾问团队来完成，这是一个支持的 学术环境以及教学和动手培训。培训完成后，申请人 计划成为先天性心脏病的先进成像分析应用的世界领导者。