An ensemble machine learning-based risk stratification tool for 30-day mortality prediction in critically ill cardiovascular patients

BACKGROUND: Early mortality prediction in critically ill patients with cardiovascular disease remains challenging. This study aimed to develop and validate an ensemble machine learning (ML) model to predict 30-day mortality, comparing its performance with conventional severity scores and interrogating the incremental prognostic value of stress hyperglycemia ratio (SHR). METHODS: A retrospective cohort of 1,595 ICU patients with cardiovascular disease combined with diabetes (2008-2022) was analyzed. SHR was calculated as admission glucose divided by estimated average glucose (eAG) from HbA1c. Six ML models (eXtreme Gradient Boosting [XGBoost], Decision Tree [DT], Random Forest [RF], Artificial Neural Network [ANN], Logistic Regression [LR], and Support Vector Machine [SVM]) were trained on 80% of the data, with the top three performers combined into an ensemble model. Model performance was evaluated using area under the curve (AUC), precision-recall, calibration, and clinical utility metrics. RESULTS: The 30-day mortality rate was 10.8% in the entire cohort (n = 173). The ensemble model demonstrated superior predictive performance with an AUC of 0.912 (95% CI: 0.888-0.936), outperforming both individual ML models (XGBoost, AUC = 0.903) and traditional scoring systems (APS III/SOFA/SAPS II AUCs ≤ 0.742; all P < 0.001). The top six important predictors included anti-hypertensives, aspirin, blood urea nitrogen (BUN), white blood cell (WBC), age, and red blood cell (RBC), with the Shapley Additive Explanations analysis revealing clinically meaningful patterns: a nonlinear risk escalation for age, linear risk increases with rising BUN and bilirubin levels, a protective effect associated with higher RBC counts, and both low and high WBC levels linked to increased early death risk. While SHR significantly improved the performance of traditional scoring systems (e.g., increasing SOFA AUC from 0.741 to 0.757, P = 0.010), its addition to the ensemble model provided limited incremental benefit (ΔAUC = - 0.032, P = 0.094). External validation in an independent cohort (n = 307) confirmed the model's robustness (AUC = 0.891, 95% CI: 0.864-0.917), with decision curve analysis demonstrating superior clinical utility across a wide range of risk thresholds. CONCLUSIONS: The ensemble ML model outperformed conventional prognostic tools in predicting 30-day mortality, with SHR augmenting traditional tools but not the ensemble ML model. This approach offers a reliable, interpretable framework for risk stratification in high-risk cardiovascular patients.