Explainable machine learning model for early prediction of ICU death in chronic heart failure with pulmonary infection.
TL;DR
The XGBoost-based model provides interpretable prediction of ICU mortality in CHF patients with pulmonary infection, and suggested that mortality risk mechanisms differ between heart failure subtypes, supporting more individualized risk assessment and management in critically ill CHF patients.
Abstract
Objective
To develop and validate a machine learning model for predicting ICU mortality in CHF patients with pulmonary infection.
Methods
Clinical data were extracted from the MIMIC-IV database, and ICU mortality within 15 days was defined as the primary endpoint. Patients were stratified into HFrEF, HFmrEF, and HFpEF based on left ventricular ejection fraction. Six models were constructed-logistic regression, random forest, light gradient boosting machine, extreme gradient boosting (XGBoost), multilayer perceptron, and k-nearest neighbor-and optimized using cross-validated grid search. Predictive performance was evaluated using accuracy, precision, recall, F1-score, AUC, Brier score, Youden index, and calibration slope. Model interpretability was assessed using SHAP and LIME. External validation was conducted using the eICU-CRD database.
Results
A total of 1308 patients were included, with an ICU mortality of 15.98%. Among all models, XGBoost achieved the best balance of discrimination and calibration (AUC 0.968; calibration slope 1.070), and retained useful but attenuated discrimination in external testing (AUC 0.718). SHAP analysis identified lactic acid, white blood cell count, prior diagnosis of hypertension, serum potassium, and pre-existing diabetes mellitus as the five most influential predictors of the outcome. Subgroup analyses further demonstrated distinct pathophysiological trajectories associated with mortality: HFrEF predominantly exhibited hemodynamic instability, impaired tissue perfusion, and electrolyte disturbances; in contrast, HFpEF were more frequently associated with chronic metabolic-inflammatory dysregulation and coagulopathy.
Conclusion
The XGBoost-based model provides interpretable prediction of ICU mortality in CHF patients with pulmonary infection. Phenotype-specific interpretability analysis further suggested that mortality risk mechanisms differ between heart failure subtypes, supporting more individualized risk assessment and management in critically ill CHF patients.