Explainable Machine Learning for Predicting Deep Vein Thrombosis in Critically Ill Patients with COPD: Development and Multicenter External Validation

Background: Deep vein thrombosis (DVT) is a frequent yet underrecognized complication in critically ill patients with chronic obstructive pulmonary disease (COPD). Existing risk assessment tools are not specifically tailored to this high-risk population. We aimed to develop and externally validate an interpretable machine-learning model for early prediction of DVT in ICU-admitted COPD patients. Methods: Adult COPD patients admitted to the ICU were identified from the MIMIC-IV database and randomly divided into training and internal validation cohorts. Eight machine-learning algorithms were constructed and compared. The best-performing model was externally validated in MIMIC-III and eICU cohorts. Model discrimination, calibration, and clinical utility were assessed using AUC, calibration plots, decision-curve analysis (DCA), and Brier scores. SHAP analysis was applied for global and individual interpretability. A web-based calculator was developed for clinical application. Results: Among 6,672 ICU patients with COPD, 462 (6.9%) developed DVT. XGBoost showed the best overall performance, with an AUC of 0.840 (95% CI 0.812–0.868) in the internal validation cohort and good calibration. External validation confirmed stable discrimination in both MIMIC-III and eICU cohorts. Model interpretation identified prolonged PTT, elevated RDW, reduced SpO₂, and increased respiratory rate as important contributors to DVT risk. Decision-curve analysis suggested potential clinical benefit across relevant risk thresholds. Conclusion: We developed and externally validated an explainable machine-learning model for early prediction of DVT in ICU patients with COPD. By providing individualized risk estimates and interpretable explanations, this tool may help clinicians identify high-risk patients earlier and support more targeted thromboprophylaxis and imaging surveillance strategies.