Construction and internal-external validation of a machine learning-based risk prediction model for multidrug resistance in ICU patients with acute exacerbation of chronic obstructive pulmonary disease.

Objective: This study aimed to create and validate a machine learning (ML) model for predicting the risk of multidrug-resistant (MDR) infection in critically ill patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) admitted to the intensive care unit (ICU). Methods: Data from patients diagnosed with AECOPD were retrospectively extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. A total of 1,018 patients were split at a 7:3 ratio into a training set ( Results: Boruta identified SpO₂, RBC, hemoglobin, hematocrit, blood urea nitrogen, creatinine, Nbpd, and Nbpm as significant predictors. The LightGBM outperformed the other algorithms: in the internal validation set, it achieved 78.9% accuracy, 90.5% sensitivity, 87.3% specificity, 60.5% F1 score, and an AUC of 0.966 (95% CI 0.951-0.981); in the external validation set, it reached 74.4% accuracy, 71.1% sensitivity, 77.8% specificity, 58.7% F1 score, and an AUC of 0.926 (95% CI 0.895-0.958). SHAP analysis indicated that hematocrit and SpO₂ were the primary drivers of model decisions. Interactive and dynamic nomograms were successfully developed. Conclusion: Multidrug-resistant occurrence in AECOPD patients was associated with SpO₂, RBC, hemoglobin, hematocrit, blood urea nitrogen, creatinine, Nbpd, and Nbpm. The LightGBM model demonstrated good discriminative ability but limited sensitivity for detecting positive cases, offering potential value as a rule-out screening tool for MDR risk in critically ill AECOPD patients admitted to ICU.