Development and temporal prospective validation of an interpretable machine learning model for identifying depressive...

To develop and temporally prospectively validate an interpretable model for identifying concurrent depressive symptoms in hospitalized patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD).

This study was conducted in two stages: retrospective development and prospective validation. In the development stage, 304 AECOPD patients admitted to the Fourth Affiliated Hospital of Anhui Medical University from September 2023 to February 2025 were retrospectively enrolled and randomly divided into a training set (n = 214) and an internal validation set (n = 90) at a 7:3 ratio. Candidate predictors were screened using least absolute shrinkage and selection operator (LASSO) regression, and the selected variables were subsequently incorporated into an unpenalized multivariable logistic regression model as the primary clinical model. A nomogram was developed based on the logistic regression coefficients for individual risk estimation. Additionally, 10 machine learning algorithms were compared, and a stacked ensemble strategy was explored as a supplementary analysis to evaluate potential incremental predictive value. Model discrimination, calibration, and clinical utility were assessed using multiple metrics. In the validation stage, 120 AECOPD patients admitted to the same center from October to December 2025 were prospectively enrolled for single-center temporal validation. Model interpretability was provided by the final logistic regression coefficients and the nomogram.

LASSO regression screened four candidate predictors: physical function (PHYS), mental health (PSYCH), environmental support (ENVIR), and quality of life. The selected variables were entered into an unpenalized multivariable logistic regression model as the final clinical tool. The final logistic regression model achieved an AUC of 0.866 (95% CI: 0.819-0.912) in the training set, 0.862 in the internal validation set, and 0.866 (95% CI: 0.772-0.960) in the prospective validation set. In exploratory comparisons, the SVM + LR stacked ensemble demonstrated comparable discrimination to the logistic regression model (AUCs: 0.863, 0.868, and 0.864 in the training, internal validation, and prospective validation cohorts, respectively), but no statistically significant improvement was observed (DeLong test, p = 0.312). Calibration analysis showed good agreement in the training and internal validation sets, whereas miscalibration was observed in the prospective cohort. Decision curve analysis suggested potential clinical utility for risk stratification. Model interpretability was provided by the final logistic regression coefficients and the nomogram.

A four-feature interpretable model was developed to identify AECOPD patients with concurrent depressive symptoms during hospitalization. The model demonstrated satisfactory discriminative performance and may assist early risk stratification; however, multicenter external validation, local recalibration, and decision-impact analysis are required before routine clinical implementation.