ArticleBMC psychiatry2025
Development and validation of a risk prediction model for depression in patients with chronic obstructive pulmonary disease.
Article in BMC psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A TabPFN-based prediction system for refractive error and dry eye comorbidity: a retrospective study using large-scale real-world data.Frontiers in cell and developmental biology · 2026Article
- Advances in artificial intelligence applications for the management of chronic obstructive pulmonary disease.Frontiers in medicine · 2025Review
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Authors and funding
4 authors.
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Abstract
backgroundChronic Obstructive Pulmonary Disease (COPD) is a prevalent respiratory condition often accompanied by depression, which exacerbates disease burden and impairs quality of life. Early identification of depression risk in COPD patients remains a clinical challenge.
objectiveThis study aimed to develop a machine learning-based model to predict depression risk in COPD patients, utilizing interpretable features from clinical and demographic data to support early intervention.
methodsData were extracted from the National Health and Nutrition Examination Survey (NHANES), involving 1,638 COPD patients. Depression was assessed using the Patient Health Questionnaire-9 (PHQ-9) scale. Feature selection was performed with Boruta and least absolute shrinkage and selection operator (LASSO) algorithms, identifying key predictors from demographic, lifestyle, medical history, and laboratory variables. Nine machine learning models were trained and evaluated, with performance assessed via accuracy, area under the curve (AUC), calibration, and clinical utility metrics.
resultsSignificant predictors of depression included sleep disturbances, age, poverty, hypertension, and comorbidities like cardiovascular disease. The Support Vector Machine (SVM) model achieved the highest performance, with an AUC of 0.890 in the validation set and 0.887 in the test set, demonstrating robust discriminative ability and clinical applicability. SHapley Additive exPlanations (SHAP) analysis enhanced model interpretability. Notably, sleep disturbances, younger age, and greater socioeconomic deprivation were associated with an elevated risk of depression.
conclusionThis study presents a reliable SVM-based model for predicting depression risk in COPD patients, leveraging NHANES data and interpretable features. It offers a valuable tool for early screening and personalized care, with potential to improve mental health outcomes in this population.
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