Machine learning-based prediction of early invasive mechanical ventilation in ICU patients with pneumonia: Development and external validation.

BACKGROUND: Pneumonia is a common critical illness in the intensive care unit (ICU), and a subset of patients rapidly progresses to respiratory failure requiring invasive mechanical ventilation (IMV). Current decisions often rely on fragmented clinical indicators, highlighting the need for integrated, data-driven tools for early risk assessment. METHODS: We conducted a retrospective cohort study using the Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) database as the development cohort. The cohort was randomly split into training (80%) and test (20%) sets. Candidate predictors at ICU admission were selected through multi-step feature selection. Eight machine learning models were trained with 5-fold cross-validation and hyperparameter tuning. Model performance was evaluated in terms of discrimination, calibration, and clinical utility. The best-performing model was externally validated in an independent cohort from Maoming People's Hospital. A web-based calculator was further developed to facilitate potential clinical application and individualized risk assessment. RESULTS: The development cohort included 5,608 patients, of whom 856 (15.3%) required IMV within 24 h. The final model retained seven predictors: age, oxygen flow, FiO2, pH, PaO2, PaCO2, and platelet count. LightGBM showed the best performance in the internal test set (AUC = 0.799) and achieved an AUC of 0.702 in the external validation cohort (n = 155). Calibration showed acceptable agreement, and decision curve analysis demonstrated net clinical benefit. Risk stratification further enabled identification of clinically distinct patient groups with progressively increasing IMV incidence. SHAP analysis further enhanced model interpretability by identifying key predictors associated with IMV risk. CONCLUSIONS: This study developed and externally validated a machine learning model and online calculator for predicting early IMV in ICU pneumonia patients. The model provides an interpretable, data-driven approach for early risk stratification and may serve as an adjunctive tool to assist clinical decision-making. Multicenter prospective studies are warranted to confirm clinical utility.