Multimodal machine learning predicts type 2 respiratory failure in COPD exacerbations: a multicenter XGBoost model with clinical nomogram.

Background: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) frequently lead to life-threatening type 2 respiratory failure (T2RF). Existing predictive models rely on single biomarkers or linear methods and lack rigorous external validation. This study aimed to develop a multimodal machine learning framework to predict in-hospital T2RF risk with temporal-geographic external validation. Methods: We employed a two-source design. A development cohort of 6,954 AECOPD patients from a single tertiary hospital (2023-2025) was randomly divided into training ( Results: In the internal test set, XGBoost achieved an AUROC of 0.660 (95% CI: 0.631-0.689). In the external validation set, XGBoost achieved an AUROC of 0.699 (95% CI: 0.661-0.738), with 45.9% sensitivity and 79.0% specificity. LightGBM performed comparably (AUROC 0.700). Seven predictors were selected: lymphocyte count, eosinophil count, COPD duration, RDW-CV, age, hypertension, and sex. SHAP analysis identified low lymphocyte count and long COPD duration as dominant risk drivers. The logistic nomogram achieved an external AUROC of 0.666. Conclusion: This externally validated framework enables early T2RF risk stratification at admission using routine blood counts and demographics. Future work should integrate dynamic monitoring and prospective multicenter validation.