{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T14:21:32Z","timestamp":1786544492673,"version":"build-2736575974"},"reference-count":38,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T00:00:00Z","timestamp":1665446400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["CNS Neurosci Ther"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Aims<\/jats:title><jats:p>To compare the performance of logistic regression and machine learning methods in predicting postoperative delirium (POD) in elderly patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Method<\/jats:title><jats:p>This was a retrospective study of perioperative medical data from patients undergoing non\u2010cardiac and non\u2010neurology surgery over 65\u2009years old from January 2014 to August 2019. Forty\u2010six perioperative variables were used to predict POD. A traditional logistic regression and five machine learning models (Random Forest, GBM, AdaBoost, XGBoost, and a stacking ensemble model) were compared by the area under the receiver operating characteristic curve (AUC\u2010ROC), sensitivity, specificity, and precision.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In total, 29,756 patients were enrolled, and the incidence of POD was 3.22% after variable screening. AUCs were 0.783 (0.765\u20130.8) for the logistic regression method, 0.78 for random forest, 0.76 for GBM, 0.74 for AdaBoost, 0.73 for XGBoost, and 0.77 for the stacking ensemble model. The respective sensitivities for the 6 aforementioned models were 74.2%, 72.2%, 76.8%, 63.6%, 71.6%, and 67.4%. The respective specificities for the 6 aforementioned models were 70.7%, 99.8%, 96.5%, 98.8%, 96.5%, and 96.1%. The respective precision values for the 6 aforementioned models were 7.8%, 52.3%, 55.6%, 57%, 54.5%, and 56.4%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The optimal application of the logistic regression model could provide quick and convenient POD risk identification to help improve the perioperative management of surgical patients because of its better sensitivity, fewer variables, and easier interpretability than the machine learning model.<\/jats:p><\/jats:sec>","DOI":"10.1111\/cns.13991","type":"journal-article","created":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T07:09:10Z","timestamp":1665472150000},"page":"158-167","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective 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