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

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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.