International journal of chronic obstructive pulmonary disease
Development and Validation of a Machine Learning-Based Model for Elevated Depressive Symptom Risk in COPD Patients: A Multi-Cohort Cross-Sectional Study
By Cheng Z, Wu J and Yan X.
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.