# Cardiac MR-Based Risk Stratification for Heart Failure and Atrial Fibrillation in HCM

> **NIH NIH R01** · BETH ISRAEL DEACONESS MEDICAL CENTER · 2021 · $782,727

## Abstract

Hypertrophic cardiomyopathy (HCM) is the most common genetic heart disease, affecting as many as
1:200 individuals in the general population. HCM, initially described in the context of sudden cardiac death (SCD),
is commonly associated with heart failure (HF) and atrial fibrillation (AF). Rigorous research over the past two
decades has enabled us to identify HCM patients at the greatest risk of SCD who could benefit from a
prophylactic implantable cardioverter defibrillator (ICD). With advances in SCD prevention, HCM management
has now shifted its focus to HF and AF. Nearly 50% of HCM patients have mild to severe HF symptoms. HF is
now considered the most common cause of HCM-related mortality. AF is the most common sustained
arrhythmia, occurring in nearly 25% of HCM patients, and responsible for a decreased quality of life and
increased stroke risk. Currently, we are not able to predict which HCM patients are more likely to progress toward
end-stage HF or develop AF. Cardiovascular imaging using echocardiography and cardiac MR has played a
central role in our evolving understanding of HCM. Echocardiography provides a robust assessment of left
ventricular (LV) outflow obstruction and diastolic dysfunction. With its high spatial resolution and remarkable
tissue characterization capabilities, cardiac MR has emerged as an imaging modality well suited to characterize
the HCM phenotype. The goal of this proposal is to develop novel risk stratification paradigms by leveraging
recent advances in artificial intelligence (AI) to improve HCM patient management. We will investigate a deep
learning (DL) risk model for prediction of adverse cardiovascular outcomes that incorporates (a) standard clinical
and imaging parameters and (b) novel cardiac MR signatures extracted using (i) radiomic analysis (i.e. a
computational method to automatically extract and select clinically significant imaging markers) or (ii) deep
imaging signatures, extracted using deep convolutional neural networks (CNN). The performance of these
models will be rigorously evaluated using 3 HCM cohorts collected at Tufts Medical Center, BIDMC, and
University of Toronto.

## Key facts

- **NIH application ID:** 10235698
- **Project number:** 1R01HL158098-01
- **Recipient organization:** BETH ISRAEL DEACONESS MEDICAL CENTER
- **Principal Investigator:** Martin S Maron
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $782,727
- **Award type:** 1
- **Project period:** 2021-04-15 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10235698

## Citation

> US National Institutes of Health, RePORTER application 10235698, Cardiac MR-Based Risk Stratification for Heart Failure and Atrial Fibrillation in HCM (1R01HL158098-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10235698. Licensed CC0.

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