Machine learning-based prediction of ventilator-associated pneumonia in ICU patients with type 2 diabetes.

backgroundVentilator-associated pneumonia (VAP) remains one of the most common and serious complications among mechanically ventilated patients in the ICU. Patients with type 2 diabetes mellitus (T2DM) are considered a vulnerable population for VAP because of impaired immune function and metabolic dysregulation. This study aimed to develop and validate machine learning (ML) models for the early prediction of VAP in mechanically ventilated ICU patients with T2DM.

methodsA total of 876 mechanically ventilated ICU patients with T2DM from the MIMIC-IV database were included and randomly divided into training and testing sets at a ratio of 7:3. Feature selection was performed using the Boruta algorithm, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Spearman correlation analysis. The Synthetic Minority Over-sampling Technique (SMOTE) was applied to address class imbalance. Four ML models, including logistic regression (LR), random forest (RF), gradient boosting machine (GBM), and eXtreme Gradient Boosting (XGBoost), were developed. Model performance was comprehensively evaluated using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and multiple performance metrics, including recall, specificity, and F1-score. Shapley Additive Explanations (SHAP) were applied to interpret the optimal model, and a web-based risk calculator was further developed. In addition, sensitivity analyses were performed by retaining the Glasgow Coma Scale (GCS) score and using the original dataset without SMOTE to evaluate the robustness of the model.

resultsA total of 876 patients were included, of whom 73 developed VAP and 803 did not. XGBoost demonstrated the best predictive performance, with an area under the receiver operating characteristic curve (AUC) of 0.891 (95% CI 0.862-0.920), a recall of 0.832, a specificity of 0.877, and an F1-score of 0.851 in the testing set. Calibration curves showed good agreement between predicted and observed outcomes, and DCA indicated favorable potential clinical net benefit. SHAP analysis identified the seven most influential predictive features in the model, including Glucose_max, SOFA-Renal score, hypertension, Temp_min, Scr_max, WBC_min, and SOFA-CNS score.

conclusionFour ML models were developed and compared for the early prediction of VAP in mechanically ventilated ICU patients with T2DM, among which XGBoost demonstrated the best predictive performance. SHAP improved the interpretability of the model and identified the seven predictive features that contributed most to model predictions. Future prospective multicenter external validation studies are needed to further evaluate the model's generalizability and provide additional evidence for the early risk assessment of VAP in patients with T2DM.