{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:04:35Z","timestamp":1782846275777,"version":"3.54.5"},"reference-count":44,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Inform"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Pulmonary dysfunctions are common and frequently co-occur with depressive symptoms, worsening outcomes, and increasing health care burden. Clinically usable models for identifying pulmonary dysfunction\u2013depression comorbidity remain limited by suboptimal interpretability, inconsistent validation, and uncertain generalizability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study developed and compared logistic regression (LR), Bayesian network (BN), and Extreme Gradient Boosting (XGBoost) models for identifying factors associated with pulmonary dysfunction\u2013depression comorbidity and evaluated their clinical usefulness across different decision thresholds.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data were drawn from the 2011 and 2015 waves of the China Health and Retirement Longitudinal Study. The analytical sample comprised 1146 adults with confirmed pulmonary dysfunction, of whom 514 (44.9%) exhibited clinically significant depressive symptoms (10-item Center for Epidemiologic Studies Depression Scale [CESD-10] score of \u226510). Models incorporated demographic, biomarker, comorbidity, and behavioral variables. Performance was assessed via discrimination (area under the receiver operating characteristic curve [AUROC]), calibration (Hosmer-Lemeshow test), and decision curve analysis. Sensitivity analyses excluding psychiatric history addressed potential conceptual overlap with the outcome.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>LR and BN showed similar discrimination across cohorts (AUROC\u22480.73), exceeding XGBoost (0.690 training; 0.650 validation). LR had the most balanced validation performance (specificity 0.721; sensitivity 0.647), whereas BN favored sensitivity (0.884) over specificity (0.401). Training calibration was good for LR or BN, but only LR remained acceptable in validation; XGBoost was miscalibrated. XGBoost\u2019s training net benefit did not generalize. Psychiatric history was the strongest factor (odds ratio 3.46\u20107.63), followed by nephropathy, arthritis, and gastropathy; BMI and household registration were inversely associated. Excluding psychiatric history modestly reduced AUROC. With 20 shared predictors, AUROCs converged (0.658\u20100.665), BN calibrated best, LR or BN remained sensitivity-forward, and XGBoost remained specificity-forward.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Using routinely available clinical and sociodemographic variables, LR and BN matched or exceeded XGBoost in externally validated performance and produced more reliable probability estimates. Model choice should align with intended use: BN (or LR) is preferable for sensitivity-forward screening, whereas XGBoost may be reserved for high-threshold confirmatory decisions only after recalibration. Across methods, psychiatric history, nephropathy, arthritis, gastropathy, household registration status, and BMI emerged as stable markers of vulnerability to depressive symptoms in pulmonary dysfunction.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/77940","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T06:36:26Z","timestamp":1775802986000},"page":"e77940-e77940","source":"Crossref","is-referenced-by-count":0,"title":["Comparative Performance of 3 Analytical Models in Identifying Associated Factors of Pulmonary Dysfunction\u2013Depression Comorbidity: China Health and Retirement Longitudinal Study\u2013Based Nationwide Cross-Sectional 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