{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T20:19:06Z","timestamp":1791145146743,"version":"4.1.0"},"reference-count":45,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,10,18]],"date-time":"2022-10-18T00:00:00Z","timestamp":1666051200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010857","name":"Jiangxi Provincial Department of Science and Technology","doi-asserted-by":"publisher","award":["No.20212BAG70010"],"award-info":[{"award-number":["No.20212BAG70010"]}],"id":[{"id":"10.13039\/501100010857","id-type":"DOI","asserted-by":"publisher"}]}],"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>Objective<\/jats:title><jats:p>This study used machine learning algorithms to identify critical variables and predict postoperative delirium (<jats:styled-content style=\"fixed-case\">POD)<\/jats:styled-content> in patients with degenerative spinal disease.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We included 663 patients who underwent surgery for degenerative spinal disease and received general anesthesia. The <jats:styled-content style=\"fixed-case\">LASSO<\/jats:styled-content> method was used to screen essential features associated with <jats:styled-content style=\"fixed-case\">POD<\/jats:styled-content>. Clinical characteristics, preoperative laboratory parameters, and intraoperative variables were reviewed and were used to construct nine machine learning models including a training set and validation set (80% of participants), and were then evaluated in the rest of the study sample (20% of participants). The area under the receiver\u2010operating characteristic curve (<jats:styled-content style=\"fixed-case\">AUROC<\/jats:styled-content>) and Brier scores were used to compare the prediction performances of different models. The <jats:styled-content style=\"fixed-case\">eXtreme<\/jats:styled-content> Gradient Boosting algorithms (<jats:styled-content style=\"fixed-case\">XGBOOST<\/jats:styled-content>) model was used to predict <jats:styled-content style=\"fixed-case\">POD<\/jats:styled-content>. The <jats:styled-content style=\"fixed-case\">SHapley<\/jats:styled-content> Additive <jats:styled-content style=\"fixed-case\">exPlanations<\/jats:styled-content> (<jats:styled-content style=\"fixed-case\">SHAP<\/jats:styled-content>) package was used to interpret the <jats:styled-content style=\"fixed-case\">XGBOOST<\/jats:styled-content> model. Data of 49 patients were prospectively collected for model validation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The <jats:styled-content style=\"fixed-case\">XGBOOST<\/jats:styled-content> model outperformed the other classifier models in the training set (area under the curve [<jats:styled-content style=\"fixed-case\">AUC<\/jats:styled-content>]: 92.8%, 95% confidence interval [<jats:styled-content style=\"fixed-case\">CI<\/jats:styled-content>]: 90.7%\u201395.0%), validation set (<jats:styled-content style=\"fixed-case\">AUC<\/jats:styled-content>: 87.0%, 95% <jats:styled-content style=\"fixed-case\">CI<\/jats:styled-content>: 80.7%\u201393.3%). This model also achieved the lowest Brier Score. Twelve vital variables, including age, serum albumin, the admission\u2010to\u2010surgery time interval, C\u2010reactive protein level, hypertension, intraoperative blood loss, intraoperative minimum blood pressure, cardiovascular\u2010cerebrovascular disease, smoking, alcohol consumption, pulmonary disease, and admission\u2010intraoperative maximum blood pressure difference, were selected. The <jats:styled-content style=\"fixed-case\">XGBOOST<\/jats:styled-content> model performed well in the prospective cohort (accuracy: 85.71%).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>A machine learning model and a web predictor for delirium after surgery for the degenerative spinal disease were successfully developed to demonstrate the extent of <jats:styled-content style=\"fixed-case\">POD<\/jats:styled-content> risk during the perioperative period, which could guide appropriate preventive measures for high\u2010risk patients.<\/jats:p><\/jats:sec>","DOI":"10.1111\/cns.14002","type":"journal-article","created":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T04:45:22Z","timestamp":1666154722000},"page":"282-295","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Automated machine learning\u2010based model for the prediction of delirium in patients after surgery for 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