Predicting Unplanned 7-day Intensive Care Unit Readmissions with Machine Learning Models for Improved Discharge Risk Assessment

Unplanned readmission to the intensive care unit (ICU) confers excess morbidity and mortality. We explore whether machine learning models can outperform the current standard, the Stability and Workload Index for Transfer (SWIFT) score, in assessing 7-day ICU readmission risk at discharge. Logistic regression, random forest, support vector machine, and gradient boosting models were trained and validated on Stanford Hospital data (2009-2019), externally validated on Beth Israel Deaconess Medical Center (BIDMC) data (2008-2019) and benchmarked against SWIFT. The best performing model was gradient boosting, with AUROC of 0.85 and 0.60 and F1-score of 0.43 and 0.14 on internal and external validation, respectively. SWIFT had an AUROC of 0.67 and 0.51 and F1-score of 0.33 and 0.10 on Stanford and BIDMC data, respectively. Machine learning models predicting 7-day ICU readmission risk can improve current ICU discharge risk assessment standards, but performance may be limited without local training.