Mpanya Dineo, Celik Turgay, Klug Eric, Ntsinjana Hopewell
Division of Cardiology, Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Wits Institute of Data Science, University of the Witwatersrand, Johannesburg, South Africa.
Front Cardiovasc Med. 2023 Jan 11;9:1032524. doi: 10.3389/fcvm.2022.1032524. eCollection 2022.
The age of onset and causes of heart failure differ between high-income and low-and-middle-income countries (LMIC). Heart failure patients in LMIC also experience a higher mortality rate. Innovative ways that can risk stratify heart failure patients in this region are needed. The aim of this study was to demonstrate the utility of machine learning in predicting all-cause mortality in heart failure patients hospitalised in a tertiary academic centre.
Six supervised machine learning algorithms were trained to predict in-hospital all-cause mortality using data from 500 consecutive heart failure patients with a left ventricular ejection fraction (LVEF) less than 50%.
The mean age was 55.2 ± 16.8 years. There were 271 (54.2%) males, and the mean LVEF was 29 ± 9.2%. The median duration of hospitalisation was 7 days (interquartile range: 4-11), and it did not differ between patients discharged alive and those who died. After a prediction window of 4 years (interquartile range: 2-6), 84 (16.8%) patients died before discharge from the hospital. The area under the receiver operating characteristic curve was 0.82, 0.78, 0.77, 0.76, 0.75, and 0.62 for random forest, logistic regression, support vector machines (SVM), extreme gradient boosting, multilayer perceptron (MLP), and decision trees, and the accuracy during the test phase was 88, 87, 86, 82, 78, and 76% for random forest, MLP, SVM, extreme gradient boosting, decision trees, and logistic regression. The support vector machines were the best performing algorithm, and furosemide, beta-blockers, spironolactone, early diastolic murmur, and a parasternal heave had a positive coefficient with the target feature, whereas coronary artery disease, potassium, oedema grade, ischaemic cardiomyopathy, and right bundle branch block on electrocardiogram had negative coefficients.
Despite a small sample size, supervised machine learning algorithms successfully predicted all-cause mortality with modest accuracy. The SVM model will be externally validated using data from multiple cardiology centres in South Africa before developing a uniquely African risk prediction tool that can potentially transform heart failure management through precision medicine.
高收入国家与低收入和中等收入国家（LMIC）心力衰竭的发病年龄和病因有所不同。LMIC的心力衰竭患者死亡率也更高。需要有创新方法对该地区的心力衰竭患者进行风险分层。本研究的目的是证明机器学习在预测三级学术中心住院的心力衰竭患者全因死亡率方面的效用。
使用来自500例连续的左心室射血分数（LVEF）低于50%的心力衰竭患者的数据，训练六种监督式机器学习算法来预测院内全因死亡率。
平均年龄为55.2±16.8岁。男性有271例（54.2%），平均LVEF为29±9.2%。中位住院时间为7天（四分位间距：4 - 11天），存活出院患者与死亡患者之间无差异。在4年的预测窗口（四分位间距：2 - 6年）后，84例（16.8%）患者在出院前死亡。随机森林、逻辑回归、支持向量机（SVM）、极端梯度提升、多层感知器（MLP）和决策树的受试者工作特征曲线下面积分别为0.82、0.78、0.77、0.76、0.75和0.62，测试阶段随机森林、MLP、SVM、极端梯度提升、决策树和逻辑回归的准确率分别为88%、87%、86%、82%、78%和76%。支持向量机是表现最佳的算法，呋塞米、β受体阻滞剂、螺内酯、舒张早期杂音和胸骨旁抬举与目标特征呈正系数，而冠状动脉疾病、血钾、水肿分级、缺血性心肌病和心电图右束支传导阻滞呈负系数。
尽管样本量较小，但监督式机器学习算法成功地以适度的准确率预测了全因死亡率。在开发一种独特的非洲风险预测工具之前，SVM模型将使用来自南非多个心脏病中心的数据进行外部验证，该工具可能通过精准医学改变心力衰竭的管理。
