{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T09:06:45Z","timestamp":1790845605996,"version":"4.1.0"},"reference-count":44,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T00:00:00Z","timestamp":1776902400000},"content-version":"vor","delay-in-days":22,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Brain and Behavior"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Depressive symptoms commonly co\u2010occur with obstructive sleep apnea (OSA), increase disease burden, and may precede major depressive disorder (MDD). In routine sleep\u2010clinic practice, mood symptoms can be overlooked during the work\u2010up for suspected OSA and at the time of polysomnography (PSG)\u2010confirmed diagnosis, because consultations focus primarily on sleep\u2010breathing and cardiometabolic complaints. Guided by the biopsychosocial model, this multicenter study aimed to develop and validate an interpretable machine\u2010learning (ML) model to predict the risk of depressive symptoms in patients with OSA.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This study included 634 adults with OSA from two sleep centers. Participants from the first center (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a0400) were randomly allocated to a training cohort and an internal validation cohort in a 7:3 ratio. An external validation cohort (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a0234) was recruited from the second center. Depressive symptoms were defined as a Patient Health Questionnaire\u20119 (PHQ\u20119) score \u2265 10. Candidate predictors covered biological, psychological, and social factors. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection. Eight ML algorithms were trained and tuned by 10\u2011fold cross\u2011validation. The best\u2011performing model was interpreted using SHapley Additive exPlanations (SHAP), and a web\u2011based prediction tool was constructed.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In the external validation cohort, the random forest (RF) model showed the best overall performance, with an area under the receiver operating characteristic curve (AUC) of 0.815, accuracy of 0.833, and Brier score of 0.154. Decision\u2011curve analysis supported its clinical utility. SHAP analysis identified perceived stress level, apnea\u2013hypopnea index (AHI), and hypertension as the most influential predictors, followed by OSA severity, sleep quality, total sleep time, mean oxygen saturation (MSaO\n                      <jats:sub>2<\/jats:sub>\n                      ), body mass index (BMI), and sex.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study developed and externally validated an interpretable random forest\u2010based model to predict the risk of depressive symptoms in patients with OSA. The model integrates key biopsychosocial features, shows good discrimination and calibration, and offers a favorable net benefit. The accompanying web\u2011based tool supports practical risk assessment and may facilitate early identification, risk stratification, and personalized intervention to help prevent progression to MDD.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/brb3.71399","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T07:19:27Z","timestamp":1776928767000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Development and External Validation of an Interpretable Machine Learning\u2010Based Prediction Model for Depressive Symptoms in Patients With Obstructive Sleep Apnea: A Multicenter Study"],"prefix":"10.1002","volume":"16","author":[{"given":"Enguang","family":"Li","sequence":"first","affiliation":[{"name":"College of Management Changchun University of Chinese Medicine  Changchun Jilin People's Republic of 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