Table_1_Machine Learning for Predicting Heart Failure Progression in Hypertrophic Cardiomyopathy.DOCX

Background: Development of advanced heart failure (HF) symptoms is the most common adverse pathway in hypertrophic cardiomyopathy (HCM) patients. Currently, there is a limited ability to identify HCM patients at risk of HF.
Objectives: In this study, we present a machine learning (ML)-based model to identify individual HCM patients who are at high risk of developing advanced HF symptoms.
Methods: From a consecutive cohort of HCM patients evaluated at the Tufts HCM Institute from 2001 to 2018, we extracted a set of 64 potential risk factors measured at baseline. Only patients with New York Heart Association (NYHA) functional class I/II and LV ejection fraction (LVEF) by echocardiography >35% were included. The study cohort (n = 1,427 patients) was split into three disjoint subsets: development (50%), model selection (10%), and independent validation (40%). The least absolute shrinkage and selection operator was used to select the most influential clinical variables. An ensemble of ML classifiers, including logistic regression, was used to identify patients with high risk of developing a HF outcome. Study outcomes were defined as progression to NYHA class III/IV, drop in LVEF below 35%, septal reduction procedure, and/or heart transplantation.
Results: During a mean follow-up of 4.7 ± 3.7 years, advanced HF occurred in 283 (20% out of 1,427) patients. The model features included patients' sex, NYHA class (I or II), HCM type (i.e., obstructive or not), LV wall thickness, LVEF, presence of HF symptoms (e.g., dyspnea, presyncope), comorbidities (atrial fibrillation, hypertension, mitral regurgitation, and systolic anterior motion), and type of cardiac medications. The developed risk stratification model showed strong differentiation power to identify patients at advanced HF risk in the testing dataset (c-statistics = 0.81; 95% confidence interval [CI]: 0.76, 0.86). The model allowed correct identification of high-risk patients with accuracy 74% (CI: 0.70, 0.78), sensitivity 80% (CI: 0.77, 0.83), and specificity 72% (CI: 0.68, 0.76). The model performance was comparable among different sex and age groups.
Conclusions: A 5-year risk prediction of progressive HF in HCM patients can be accurately estimated using ML analysis of patients' clinical and imaging parameters. A set of 17 clinical and imaging variables were identified as the most important predictors of progressive HF in HCM.

背景：肥厚型心肌病（Hypertrophic Cardiomyopathy, HCM）患者最常见的不良病程为进展为晚期心力衰竭（Heart Failure, HF）症状。目前，临床对识别存在HF风险的HCM患者的能力仍较为有限。

目的：本研究构建了一种基于机器学习（Machine Learning, ML）的模型，用于识别存在晚期HF症状高风险的HCM患者个体。

方法：本研究纳入2001年至2018年塔夫茨肥厚型心肌病研究所（Tufts HCM Institute）评估的连续HCM患者队列，提取了基线时测定的64项潜在风险因素。本研究仅纳入纽约心脏协会（New York Heart Association, NYHA）心功能分级I/II级，且超声心动图测得左心室射血分数（Left Ventricular Ejection Fraction, LVEF）>35%的患者。最终研究队列共1427例患者，被分为3个互不重叠的子集：建模集（50%）、模型选择集（10%）及独立验证集（40%）。采用最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator, LASSO）筛选最具影响力的临床变量，并构建包括逻辑回归在内的集成机器学习分类器，以识别存在HF不良结局高风险的患者。本研究的终点事件定义为：进展为NYHA心功能分级III/IV级、LVEF降至35%以下、行室间隔减容手术及/或心脏移植。

结果：平均随访4.7±3.7年期间，共有283例（占总队列的20%）患者进展为晚期HF。本模型纳入的特征包括患者性别、NYHA心功能分级（I或II级）、HCM分型（梗阻性或非梗阻性）、左心室壁厚度、LVEF、HF相关症状（如呼吸困难、先兆晕厥）、合并症（心房颤动、高血压、二尖瓣反流及收缩期前向运动）以及心脏用药类型。所构建的风险分层模型在测试集中展现出较强的晚期HF风险识别能力，C统计量为0.81，95%置信区间（Confidence Interval, CI）为0.76~0.86。该模型对高风险患者的识别准确率为74%（CI：0.70~0.78），灵敏度为80%（CI：0.77~0.83），特异度为72%（CI：0.68~0.76）。且该模型的表现在不同性别及年龄组间均具有可比性。

结论：通过对患者临床及影像学参数进行机器学习分析，可准确预测HCM患者5年内进展为HF的风险。本研究共筛选出17项临床及影像学变量，其为HCM患者进展为HF的最重要预测因子。