Machine Learning–Based risk stratification for in-hospital mortality in ICU patients with cardiovascular diseases and diabetes

Background This study aimed to develop and validate a machine learning model to predict in-hospital mortality in ICU patients with diabetes and cardiovascular diseases and to construct a web-based risk calculator to assist clinical decision-making. Methods This study retrospectively collected data on diabetic patients with cardiovascular diseases from the MIMIC-IV and eICU-CRD databases, including 4,074 patients from MIMIC-IV for model training and internal validation and 1,261 patients from eICU-CRD for external validation. Thirteen feature variables were selected using the Boruta algorithm, and eight machine learning algorithms were applied to construct prediction models. Model performance was evaluated using ROC-AUC, precision-recall, calibration curves, and SHAP algorithm for model interpretability. Results The Logistic Regression model demonstrated the best predictive performance, with an ROC-AUC of 0.896 (95%CI 0.834-0.943) and 0.820 (95%CI 0.768-0.869) for internal and external validation, respectively. It also achieved high sensitivity (0.851 and 0.931) in the internal and external validation cohorts. SHAP analysis indicated that vasopressor usage, APSIII score, GLU level, respiratory rate, and oxygen saturation were the most critical features influencing in-hospital mortality risk. Conclusion The developed Logistic Regression model exhibited high predictive accuracy and robustness in ICU patients with diabetes and cardiovascular diseases. A Web-based risk calculator was successfully constructed to provide personalized mortality risk assessment and decision support for clinicians.