Machine Learning for Predicting Heart Failure Progression in Hypertrophic Cardiomyopathy.
Machine Learning for Predicting Heart Failure Progression in Hypertrophic Cardiomyopathy.
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DOI：
10.3389/fcvm.2021.647857
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发表时间：
2021
影响因子：
3.6
通讯作者：
Nezafat R
中科院分区：
文献类型：
--
作者：
Fahmy AS;Rowin EJ;Manning WJ;Maron MS;Nezafat R
背景：晚期心力衰竭（HF）症状的发展是肥厚型心肌病（HCM）患者最常见的不良反应。目前，识别有HF风险的HCM患者的能力有限。目的：在这项研究中，我们提出了一个基于机器学习（ML）的模型来识别具有发展晚期HF症状高风险的HCM患者。研究方法：从2001年至2018年在塔夫茨HCM研究所评估的HCM患者的连续队列中，我们提取了一组基线测量的64个潜在风险因素。仅纳入纽约心脏协会（NYHA）心功能I/II级和超声心动图检查左室射血分数（LVEF）>35%的患者。研究队列（n = 1，427例患者）分为三个不相交的子集：开发（50%），模型选择（10%）和独立验证（40%）。使用最小绝对收缩和选择算子来选择最有影响力的临床变量。ML分类器的集合，包括逻辑回归，用于识别具有发展HF结果的高风险的患者。研究结局定义为进展至NYHA III/IV级、LVEF降至35%以下、房间隔缩小术和/或心脏移植。结果：在平均4.7 ± 3.7年的随访期间，283例（1，427例中的20%）患者发生了晚期HF。模型特征包括患者的性别、NYHA分级（I或II）、HCM类型（即，阻塞性或非阻塞性）、LV壁厚度、LVEF、HF症状的存在（例如，呼吸困难、先兆晕厥）、合并症（房颤、高血压、二尖瓣返流和收缩期前向运动）和心脏药物类型。所开发的风险分层模型显示了在测试数据集中识别晚期HF风险患者的强大区分能力（c-统计量= 0.81; 95%置信区间[CI]：0.76，0.86）。该模型允许正确识别高风险患者，准确性为74%（CI：0.70，0.78），灵敏度为80%（CI：0.77，0.83），特异性为72%（CI：0.68，0.76）。不同性别和年龄组的模型表现具有可比性。结论：HCM患者进行性HF的5年风险预测可以通过对患者临床和影像学参数的ML分析进行准确估计。一组17个临床和影像学变量被确定为HCM进展性HF的最重要预测因子。
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.