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


Biography

Overview
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.
R01HL158098
NEZAFAT, REZA

Time
2021-04-15
2025-03-31
Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.