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

肥厚型心肌病（HCM）是最常见的遗传性心脏病， 1：200人在一般人群中。HCM最初在心脏性猝死（SCD）的背景下描述， 通常与心力衰竭（HF）和心房纤颤（AF）相关。在过去的两年里， 几十年的研究使我们能够识别出SCD风险最大的HCM患者，他们可以从治疗中获益。 预防性植入式心律转复除颤器（ICD）。随着SCD预防的进展，HCM管理 HCM现在将其重点转移到HF和AF。近50%的HCM患者有轻度至重度HF症状。HF是 现在被认为是HCM相关死亡的最常见原因。AF是最常见的持续性 心律失常，发生在近25%的HCM患者中，并导致生活质量下降， 增加中风风险。目前，我们无法预测哪些HCM患者更有可能进展为 使用超声心动图和心脏MR进行心血管成像， 在我们对HCM不断发展的理解中发挥着核心作用。超声心动图提供了一个强大的评估左 心室（LV）流出道梗阻和舒张功能障碍。凭借其高空间分辨率和卓越的 组织定征能力，心脏MR已成为一种非常适合表征 HCM表型。该提案的目标是通过利用 人工智能（AI）的最新进展，以改善HCM患者管理。我们将深入调查 用于预测不良心血管结局的学习（DL）风险模型，包括（a）标准临床 和成像参数，以及（B）使用（i）放射组学分析（即， 自动提取和选择临床上重要的成像标记的计算方法）或（ii）深 使用深度卷积神经网络（CNN）提取的成像签名。履行这些 将使用在塔夫茨医学中心、BIDMC和 多伦多大学。

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.