Objective: To explore the value of predicting new-onset heart failure events in patients with hypertrophic cardiomyopathy (HCM) using clinical and cardiac magnetic resonance (CMR) features based on machine learning algorithms. Methods: The study was a retrospective cohort study. Patients with a confirmed diagnosis of HCM who underwent CMR examinations at Beijing Anzhen Hospital from May 2017 to March 2021 were selected and randomly divided into the training set and the validation set in a ratio of 7∶3. Clinical data and CMR parameters (including conventional parameters and radiomics features) were collected. The endpoint events were heart failure hospitalization and heart failure death, with follow-up ending in January 2023. Features with high stability and P value<0.05 in univariate Cox regression analysis were selected. Subsequently, three machine learning algorithms-random forest, decision tree, and XGBoost-were used to build heart failure event prediction models in the training set. The model performance was then evaluated using the independent validation set, with the performance assessed based on the concordance index. Results: A total of 462 patients were included, with a median age of 51 (39, 62) years, of whom 332 (71.9%) were male. There were 323 patients in the training set and 139 in the validation set. The median follow-up time was 42 (28, 52) months. A total of 44 patients (9.5% (44/462)) experienced endpoint events (8 cases of heart failure death and 36 cases of heart failure hospitalization), with 31 events in the training set and 13 in the validation set. Univariate Cox regression analysis identified 39 radiomic features, 4 conventional CMR parameters (left ventricular end-diastolic volume index, left ventricular end-systolic volume index, left ventricular ejection fraction, and late gadolinium enhancement ratio), and 1 clinical feature (history of non-sustained ventricular tachycardia) that could be included in the machine learning model. In the prediction models built with the training set, the concordance indices for the random forest, decision tree, and XGBoost models were 0.966 (95%CI 0.813-0.995), 0.956 (95%CI 0.796-0.992), and 0.973 (95%CI 0.823-0.996), respectively. In the validation set, the concordance indices for the random forest, decision tree, and XGBoost models were 0.854 (95%CI 0.557-0.964), 0.706 (95%CI 0.399-0.896), and 0.703 (95%CI 0.408-0.890), respectively. Conclusion: Integrating clinical and CMR features of HCM patients through machine learning aids in predicting heart failure events, with the random forest model showing superior performance.
目的： 探讨基于机器学习算法使用临床及心脏磁共振（CMR）特征预测肥厚型心肌病（HCM）患者新发心力衰竭的价值。 方法： 本研究为回顾性队列研究。选取2017年5月至2021年3月于北京安贞医院行CMR检查且诊断明确的HCM患者，并将其按7∶3的比例随机分为训练集和验证集，收集其临床资料、CMR参数（包括常规参数和影像组学特征）。终点事件为心力衰竭入院和心力衰竭死亡，随访截止时间为2023年1月。筛选出具有高稳定性且单因素Cox回归分析中P<0.05的特征，随后利用随机森林、决策树和XGBoost等3种机器学习算法在训练集中构建心力衰竭事件预测模型，使用一致性指数评价模型效能。 结果： 共纳入462例患者，年龄51（39，62）岁，其中男性332例（71.9%）。训练集323例，验证集139例。随访时间为42（28，52）个月，44例［9.5%（44/462）］发生了终点事件（心力衰竭死亡8例，心力衰竭住院36例），训练集中有31例，验证集中有13例。单因素Cox回归分析结果显示39个影像组学特征、4个常规CMR参数（左心室舒张末期容积指数、左心室收缩末期容积指数、左心室射血分数、延迟强化比例）、1个临床特征（非持续性心动过速史）可被纳入机器学习模型。训练集构建的预测模型中，随机森林、决策树及XGBoost模型的一致性指数分别为0.966（0.813，0.995）、0.956（0.796，0.992）及0.973（0.823，0.996）。在验证集中，随机森林、决策树及XGBoost模型的一致性指数分别为0.854（0.557，0.964）、0.706（0.399，0.896）及0.703（0.408，0.890）。 结论： 通过机器学习整合临床及CMR特征有助于实现HCM患者心力衰竭预测，其中随机森林模型表现出较好效果。.