# Novel Computational Methods for Detecting Early Right Ventricular Failure in the Tetralogy of Fallot Population

> **NIH NIH F32** · STANFORD UNIVERSITY · 2020 · $61,211

## Abstract

Project Summary/Abstract
Tetralogy of Fallot is the most prevalent severe congenital heart disease. Although surgery has led to improved
life expectancy, residual pulmonary regurgitation is common. Patients with pulmonary regurgitation go on to
develop exercise intolerance, heart failure, arrhythmias and sudden cardiac death. Pulmonary valve
replacement is increasingly used to prevent right ventricular failure. Our limited understanding of remodeling
of the right heart hampers our ability to detect subtle right ventricular dysfunction in the early stages and
prevent irreversible heart failure. Echocardiographic metrics have poor sensitivity in assessing the very early
stages of right ventricular failure. Currently we select patients for pulmonary valve intervention based on a
right ventricular volume threshold measured on MRI. This paradigm places too much emphasis on volume and
too little on ventricular function. The lack of precise imaging markers of early right heart failure in Tetralogy of
Fallot is one of the largest gaps in knowledge facing congenital cardiologists. Being able to detect early right
heart failure before progression to irreversible heart failure would allow cardiologists to follow these at-risk
patients more closely and offer pulmonary valve replacements sooner rather than later. In addition, being able
to use imaging to identify subtle stages of early right heart failure can help pharmaceutical companies and
other physician-scientists test therapeutic response. I hypothesize that innovative imaging markers derived
from computational assessments of cardiac MRI and echocardiographic imaging during peak exercise will
outperform traditional markers in predicting early right ventricular failure in Tetralogy of Fallot patients.
The goal of this study is to compare the relationship between cardiac flow and right ventricular
function in Tetralogy of Fallot patients, and then predict heart failure using changes in
ventricular shape, strain, and contraction at peak exercise. By completing this project, I will uncover
key insights into the relationship among cardiac flow, morphologic changes of the right ventricle and right
ventricular function which will bridge a major knowledge gap regarding imaging markers for the detection of
early and subtle right heart failure in the Tetralogy of Fallot population. Being able to identify early right heart
failure will change how cardiologists treat Tetralogy of Fallot patients clinically. My long-term goal as a
physician-scientist is to become an academic cardiologist, future junior faculty member and expert in right
heart failure in adults with congenital heart disease.

## Key facts

- **NIH application ID:** 10066159
- **Project number:** 1F32HL154529-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Jennifer Woo
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $61,211
- **Award type:** 1
- **Project period:** 2020-09-01 → 2021-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10066159

## Citation

> US National Institutes of Health, RePORTER application 10066159, Novel Computational Methods for Detecting Early Right Ventricular Failure in the Tetralogy of Fallot Population (1F32HL154529-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10066159. Licensed CC0.

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