Construction of risk prediction model for predicting death or readmission in acute heart failure patients during vulnerable phase based on machine learning

Abstract

Objective To construct risk prediction models of death or readmission in patients with acute heart failure (AHF) during the vulnerable phase based on machine learning algorithms and screen the optimal model. Methods A total of 651 AHF patients with admitted to Department of Cardiology of the Second Affiliated Hospital of Army Medical University from October 2019 to July 2021 were included.The clinical data consisting of admission vital signs, comorbidities and laboratory results were collected from electronic medical records.The composite endpoint was defined as all-cause death or readmission for worsening heart failure within 3 months after discharge.The patients were divided into a training set (521 patients) and a test set (130 patients) in a ratio of 8:2 through the simple random sampling.Six machine learning models were developed, including logistic regression (LR), random forest (RF), decision tree (DT), light gradient boosting machine (LGBM), extreme gradient boosting (XGBoost) and neural networks (NN).Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the predictive performance and clinical benefit of the models.Shapley additive explanation (SHAP) was used to explain and evaluate the effect of different clinical characteristics on the models. Results A total of 651 AHF patients were included, of whom 203 patients (31.2%) died or were readmitted during the vulnerable phase.ROC curve analysis showed that the AUC values of the LR, RF, DT, LGBM, XGBoost and NN model were 0.707, 0.756, 0.616, 0.677, 0.768 and 0.681, respectively.The XGBoost model had the highest AUC value.DCA showed that the XGBoost model exhibited greater clinical net benefit compared with other models, with the best predictive performance.SHAP algorithm analysis showed that the clinical features that had the greatest impact on the output of the model were serum uric acid, D-dimer, mean arterial pressure, B-type natriuretic peptide, left atrial diameter, body mass index, and New York Heart Association (NYHA) classification. Conclusion The XGBoost model has the best predictive performance in predicting the risk of death or readmission of AHF patients during the vulnerable phase.

Keywords

• ] acute heart failure
• vulnerable phase
• machine learning
• prediction model
• shapley additive explanation algorithms