Predicting 7-day unplanned readmission in elderly patients with coronary heart disease using machine learning - Song, Xuewu; Tong, Yitong; Luo, Yi; Chang, Huan; Gao, Guangjie; Dong, Ziyi; Wu, Xingwei; Tong, Rongsheng (Frontiers in Cardiovascular Medicine, 2023)
Background: Short-term unplanned readmission is always neglected, especially for elderly patients with coronary heart disease (CHD). However, tools to predict unplanned readmission are lacking. This study aimed to establish the most effective predictive model for the unplanned 7-day readmission in elderly CHD patients using machine learning (ML) algorithms.The detailed clinical data of elderly CHD patients were collected retrospectively. Five ML algorithms including extreme gradient boosting (XGB), random forest (RF), multilayer perceptron (MLP), categorical boosting (CB) and logistic regression (LR) were used to establish predictive models. We used area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, F1 value, Brier score, area under the precision-recall curve (AUPRC) and calibration curve to evaluate the performance of ML models. SHapley Additive exPlanations (SHAP) value was used for the interpretation of the best model.The final study included 834 elderly CHD patients, among whom the average age was 73.5 ± 8.4 years, 426 (51.08%) were male and 139 patients had 7-day unplanned readmissions. The XGB model had the best performance shown as the highest AUC (0.9729), accuracy (0.9173), F1 value (0.9134) and AUPRC (0.9766). The Brier score of the XGB model was 0.08. The calibration curve of the XGB model showed good calibration performance. The SHAP method showed that fracture, hypertension, length of stay, aspirin, and D-dimer were the most important indicators for the risk of 7-day unplanned readmissions. The top 10 variables were used to build a compact XGB, which also showed good predictive performance. This is a provisional file, not the final typeset article Conclusions: In this study, 5 ML algorithms were used to predict 7-day unplanned readmission in elderly patients with CHD. The XGB model had the best predictive performance and potential clinical application perspective.