ABSTRACT
BACKGROUND: Cardiovascular patients experience high rates of adverse outcomes following discharge from hospital, which may be preventable through early identification and targeted action. This study aimed to investigate the effectiveness and explainability of machine learning algorithms in predicting unplanned readmission and death in cardiovascular patients at 30 days and 180 days from discharge. METHODS: Gradient boosting machines were trained and evaluated using data from hospital electronic medical records linked to hospital administrative and mortality data for 39,255 patients admitted to four hospitals in New South Wales, Australia between 2017 and 2021. Sociodemographic variables, admission history, and clinical information were used as potential predictors. The performance was compared to LASSO regression, as well as the HOSPITAL and LACE risk score indices. Important risk factors identified by the gradient-boosting machine model were explored using Shapley values. RESULTS: The models performed well, especially for the mortality outcomes. Area under the receiver operating characteristic curve values were 0.70 for readmission and 0.87-0.90 for mortality using the full gradient boosting machine algorithms. Among the top predictors for 30-day and 180-day readmission were increased red cell distribution width, old age (especially above 80 years), high measured troponin and urea levels, not being married or in a relationship, and low albumin levels. For mortality, these included increased red cell distribution width, old age (especially older than 70 years), high measured troponin and urea levels, high neutrophil and monocyte counts, and low eosinophil and lymphocyte counts. The Shapley values gave clear insight into the dynamics of decision-tree-based models. CONCLUSIONS: We demonstrated an explainable predictive algorithm to identify cardiovascular patients who are at high risk of readmission or death at discharge from the hospital and identified key risk factors.
