Clinical Prediction of Heart Failure in Hemodialysis Patients: Based on the Extreme Gradient Boosting Method - Wang, Yanfeng; Miao, Xisha; Xiao, Gang; Huang, Chun; Sun, Junwei; Wang, Ying; Li, Panlong; You, Xu (Frontiers in Genetics, 2022)
Background: Heart failure (HF) is a main cause of mortality in hemodialysis (HD) patients. However, it is still a challenge for the prediction of HF in HD patients. Therefore, we aimed to establish and validate a prediction model to predict HF event in HD patients. Methods: A total of 355 maintenance HD patients from two hospitals were included in this retrospective study. Twenty-one variables including traditional demographic characteristics, medical history, and blood biochemical indicators were used. Two classification models were established based on extreme gradient boosting (XGBoost) algorithm and traditional linear logistic regression. The performance of two models was evaluated based on calibration curves and area under the receiver operating characteristic curves (AUC). Feature importance and SHapley Additive exPlanation (SHAP) were used to recognize risk factors from the variables. The Kaplan-Meier curve of each risk factors was constructed and compared with the log-rank test. Results: Compared with the traditional linear logistic regression, XGBoost model had better performance in accuracy (78.5% vs 74.8%), sensitivity (79.6% vs 75.6%), specificity (78.1% vs 74.4%) and AUC (0.814 vs 0.722). The feature importance and SHAP value of XGBoost indicated that age, hypertension, platelet count (PLT), C-reactive protein (CRP), white blood cell count (WBC) were risk factors of HF. These results were further confirmed by Kaplan-Meier curves. Conclusions: The HF prediction model based on XGBoost had a satisfactory performance in predicting HF event, which could provide a useful tool for the early prediction of HF in HD.