Development of a Multi-Model Ensemble Tool for Early Prediction of 48-Hour Respiratory Failure Risk in CAP Patients

Objective To develop a predictive tool capable of early identification of the risk of acute respiratory failure within 48 hours of hospital admission in patients with community-acquired pneumonia (CAP). Method A retrospective cohort of 257 CAP patients (median age: 76.0 years, IQR: 68.0-84.0; 56.4% male) was analyzed, among whom 148 (57.6%) developed respiratory failure within 48 hours. From 55 clinical variables, key predictors were selected using LASSO regression. Predictive models were then constructed using multivariable logistic regression (MLR) and machine learning algorithms including XGBoost, LightGBM, and Random Forest. To address the probability calibration issue of the XGBoost model, Platt scaling was applied. A final ensemble model was built by weighted averaging of the calibrated XGBoost, LightGBM, and MLR models. Feature importance was analyzed using SHAP (SHapley Additive exPlanations), and clinical utility was evaluated via decision curve analysis (DCA) and calibration plots. Result Respiratory rate, TNF-, IL-1beta, heart rate, pleural effusion, and body temperature were identified as the most important predictors. Other key features included total bilirubin, serum calcium, albumin/globulin ratio, and platelet count. The weighted ensemble model outperformed individual models, achieving an AUC of 0.792 on the test set. Conclusion We developed a predictive tool based on multi-model ensemble learning and interpretable machine learning techniques (SHAP), which provides a basis for early risk stratification and prevention of acute respiratory failure in hospitalized CAP patients.