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.