Development and Validation of a Machine Learning-Based Model for Elevated Depressive Symptom Risk in COPD Patients: A Multi-Cohort Cross-Sectional Study
TL;DR
The XGBoost model generalized to an independent cohort and was deployed as a research prototype web application for risk stratification and exhibited a U‑shaped association.
Abstract
Background Depression is a common yet underrecognized comorbidity in chronic obstructive pulmonary disease (COPD). Existing prediction models often lack external validation, limiting cross-population generalizability. We aimed to develop and externally test machine learning models for predicting concurrent elevated depressive symptoms in COPD using three international aging cohorts. Methods We included COPD patients from the Health and Retirement Study (HRS, USA, n=1,899) and the China Health and Retirement Longitudinal Study (CHARLS, China, n=2,206) for model development, and the English Longitudinal Study of Ageing (ELSA, UK, n=538) for external testing. Elevated depressive symptoms were defined using harmonized cutoffs (CES-D‑8≥3 for HRS/ELSA; CES-D‑10≥10 for CHARLS). Following recursive feature elimination, seven machine learning algorithms were trained and assessed for discrimination, calibration, and clinical utility. SHapley Additive exPlanations (SHAP) analysis was employed for model interpretability. Results Eight variables were used to build the seven models. The XGBoost model achieved the best internal performance (training AUC 0.824, 95% CI: 0.809–0.838; validation AUC 0.757, 95% CI: 0.731–0.783) and maintained robust external validity in ELSA (test AUC 0.785, 95% CI: 0.748–0.824). Calibration and decision curve analyses supported clinical utility. SHAP analysis identified functional dependency, self‑rated health, and life satisfaction as predominant predictors. Higher dependency and poorer self-rated health had positive SHAP contributions, indicating higher model-predicted risk, whereas higher life satisfaction had negative contributions, indicating lower model-predicted risk. BMI exhibited a U‑shaped association. Conclusion The XGBoost model generalized to an independent cohort and was deployed as a research prototype web application for risk stratification. SHAP analysis identified important model contributors for risk stratification in COPD, but these explanations describe model predictions and do not establish causal intervention targets. Prospective studies are needed to evaluate clinical utility.