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

基本信息

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
项目类别：
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

项目摘要

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.

法洛四联症修复 (rTOF) 患者中有 5% 到 10% 在 30 岁之前死亡，但我们的预测能力哪些患者会出现死亡、室性心动过速和心室颤动 (DVTF) 的情况有限。目前尚不清楚可能延迟 DVTF 的肺动脉瓣置换术 (PVR) 的最佳时机。 DVTF 的最佳预测因素和 PVR 时机指导依赖于“传统”测量，例如右心室容量和射血分数，来自心脏 MRI (CMR) 然而，即使是最好的 DVTF。模型的预测能力有限，并且这些“传统”体积测量无法预测适当的 30-40% 的患者对 PVR 的反应该提案旨在满足基于 CMR 的迫切需求。与 DVTF 相关并比传统心室容量更好地预测 PVR 反应的指标。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获取的护理标准 CMR 数据集的 rTOF 患者的力学指标，其中将允许在临床实践中快速实施，这一关键需求将通过两个具体方案得到解决。具体目标 1：开发和评估基于 CMR 的新型临床结果预测因子 rTOF。具体目标 2：前瞻性评估基于心室几何形状的肺反应预测因子 rTOF 患者的瓣膜置换术的基本原理是计算建模技术是否可以生成。优于传统标记的指标，它们可用于改变当前的患者管理延迟 DVTF 的最终目标是如果未能制定改进的指标，将导致死亡率持续过高。 rTOF 患者的临床结果欠佳。横断面和纵向的结合。方法允许对随机分组的人群中的 CMR 指标进行更全面的评估这项工作具有快速实施的潜力，因此标志着一项对照试验不可行。临床心脏病学中使用计算模型的范式转变。候选人的职业目标是成为一名独立研究者，领导多学科研究团队开发新的、更准确且易于应用的先天性心脏病 (CHD) 结果预测因子。将使他处于临床儿科心脏病学、生物医学工程和计算机科学的交叉点。为了实现这一目标，他将了解机器学习和运动学分析、它们的优点和缺点，以及他将学习如何将他的发现应用于临床实践。然后，他将设计 R01 资助的研究以使用新开发的指标。前瞻性评估心室机械指标的性能以指导 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、一个支持性的团队来完成。学术环境以及教学和实践培训完成本次培训后，申请人。计划成为先天性心脏病先进成像分析应用领域的世界领导者。