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

5%至10%的法洛四联症修复术患者在30岁之前死亡，但我们的预测能力 哪些患者将经历死亡、室性心动过速和室颤(DVTF)是有限的。这个 肺动脉瓣置换术(PVR)的最佳时机也不清楚，这可能会延迟DVTF。海流 DVTF的最佳预测指标和PVR时机的指导依赖于右室等传统的测量方法 容量和射血分数，由心脏磁共振成像(CMR)得出。然而，即使是最好的DVTF 模型的预测能力有限，而这些“传统的”体积测量方法无法预测适当的 30-40%的患者对PVR有反应。本提案旨在解决基于CMR的迫切需求- 与DVTF相关并预测PVR反应的指标比传统的心室容量测量更好。 这将通过心室变形、运动学和几何学的发展来实现。 基于常规获取的标准护理CMR数据集的rTOF患者的力学指标，该数据集 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的 目标。具体目标1：开发和评估新的基于CMR的临床预后预测指标 RTOF。特定目标2：前瞻性评估基于脑室几何形状的肺功能反应预测因素 RTOF患者的瓣膜置换术。其基本原理是，如果计算建模技术能够生成 优于传统标记的指标，它们可以用来通过 最终目标是推迟DVTF。未能开发出改进的指标将导致持续的超额死亡率 RTOF患者的临床结局不太理想。横断面与纵断面相结合 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 计算模型在临床心脏病学中使用的范式转变。 候选人的职业目标是成为一名独立调查员，领导多学科研究团队 开发新的、更准确、更易于应用的先天性心脏病(CHD)预后预测指标。这 将使他成为临床儿科心脏病学、生物医学工程和计算机科学的结合点。至 为了实现这一目标，他将学习机器学习和运动学分析，了解它们的优点和缺陷，以及 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资助的研究以 前瞻性评估室壁机械指标的表现，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/顾问团队来完成，一个支持性的 学术环境，授课和动手培训。在完成此培训后，申请人 计划成为应用先进成像分析技术治疗先天性心脏病的世界领先者。

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.