Table_1_Predicting in-hospital all-cause mortality in heart failure using machine learning.DOCX

BackgroundThe 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.MethodsSix 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%.ResultsThe 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.ConclusionDespite 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.

背景：心力衰竭的发病年龄及其原因在高收入国家和低收入及中等收入国家（LMIC）之间存在差异。LMIC的心力衰竭患者在住院期间也经历了更高的死亡率。该地区需要创新的策略来对心力衰竭患者进行风险分层。本研究旨在证明机器学习在预测三级学术中心住院心力衰竭患者全因死亡率中的实用性。方法：训练了六种监督式机器学习算法，以使用来自500例连续心力衰竭患者的数据预测院内全因死亡率，这些患者的左心室射血分数（LVEF）低于50%。结果：平均年龄为55.2 ± 16.8岁。其中男性271例（54.2%），平均LVEF为29 ± 9.2%。住院中位时间为7天（四分位数范围：4–11），与出院时存活的患者和死亡的患者之间没有差异。在4年的预测窗口期（四分位数范围：2–6）后，84（16.8%）患者在出院前死亡。随机森林、逻辑回归、支持向量机（SVM）、极限梯度提升、多层感知器（MLP）和决策树的受试者工作特征曲线下面积为0.82、0.78、0.77、0.76、0.75和0.62，测试阶段的准确率分别为88、87、86、82、78和76%。支持向量机是表现最好的算法，呋塞米、β受体阻滞剂、螺内酯、早期舒张期杂音和心前区抬举感与目标特征呈正相关，而冠状动脉疾病、血钾、水肿等级、缺血性心肌病和心电图上右束支传导阻滞与目标特征呈负相关。结论：尽管样本量较小，监督式机器学习算法仍以适度的准确率成功预测了全因死亡率。在开发一个独特的非洲风险预测工具之前，将通过南非多个心脏病学中心的数据对外部验证支持向量机模型，该工具有望通过精准医疗变革心力衰竭的管理。