Development of a Machine Learning-Based Prediction Model for Postoperative Delirium in Frail Elderly Patients Undergoing Non-Cardiac Surgery Under General Anesthesia

Development of a Machine Learning-Based Prediction Model for Postoperative Delirium in Frail Elderly Patients Undergoing Non-Cardiac Surgery Under General Anesthesia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development of a Machine Learning-Based Prediction Model for Postoperative Delirium in Frail Elderly Patients Undergoing Non-Cardiac Surgery Under General Anesthesia Qiufeng Wang, Didi Mu, Xiaofeng wang, Wenmeng Han, Jianpeng Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7554250/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background : In frail older adults, the incidence of postoperative delirium is markedly increased, leading to greater morbidity, prolonged length of stay, and higher healthcare costs. An accurate POD prediction model can direct preventive strategies and improve patient outcomes. Employing advanced machine-learning techniques, this study develops a POD prediction model using comprehensive pre-operative and intra-operative data. Methods : We enrolled 2,089 frail patients aged ≥65 years undergoing general anesthesia for non-cardiac surgery at Fuyang People’s Hospital between February 2023 and February 2025. Thirty-eight baseline, anesthetic, and laboratory variables were extracted; missing data were handled by multiple imputation using chained equations (MICE). The dataset was randomly split 7:3 into training and validation sets. After feature selection with Boruta and LASSO, eight machine-learning models—logistic regression, random forest, support-vector classifier, XGBoost, artificial neural network, naïve Bayes, k-nearest neighbors, and decision tree—were trained and compared, with ROC-AUC as the primary metric, accompanied by accuracy, precision, recall, and F1-score. Model interpretability was achieved using SHAP analysis for the best-performing algorithm. Results : Among 2,089 frail elderly patients, the incidence of POD was 16.52%. After Boruta and LASSO identified 15 key predictors, the XGBoost model achieved an AUC of 0.813, outperforming the other seven algorithms. SHAP analysis identified MMSE score, Charlson Comorbidity Index, and age as the strongest predictors. External validation demonstrated high clinical utility on decision-curve analysis, with an ROC-derived sensitivity of 0.813 and specificity of 0.793, confirming robust performance without overfitting. Conclusions : This study presents a robust XGBoost-based model for predicting postoperative delirium in frail elderly patients undergoing non-cardiac surgery, demonstrating the potential of machine learning for clinical risk stratification. With its balanced performance and high accuracy, the model enables clinicians to identify high-risk patients and initiate timely interventions. Future work should focus on integration into clinical workflows and further external validation. postoperative delirium prevention risk factors machine learning prediction model Full Text Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revisions 18 Oct, 2025 Reviewers agreed at journal 30 Sep, 2025 Reviewers invited by journal 30 Sep, 2025 Editor invited by journal 18 Sep, 2025 Editor assigned by journal 12 Sep, 2025 First submitted to journal 10 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7554250","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":522870484,"identity":"a2e1beae-f265-45b4-8190-e4e975e82b8e","order_by":0,"name":"Qiufeng Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qiufeng","middleName":"","lastName":"Wang","suffix":""},{"id":522870485,"identity":"acce8353-7b7d-4690-895a-c87ffae35745","order_by":1,"name":"Didi Mu","email":"","orcid":"","institution":"Fuyang People's 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Anesthesia\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-geriatric-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"EGEM","sideBox":"Learn more about [European Geriatric Medicine](https://www.springer.com/journal/41999)","snPcode":"41999","submissionUrl":"https://www.editorialmanager.com/egem/default2.aspx","title":"European Geriatric Medicine","twitterHandle":"","acdcEnabled":false,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"postoperative delirium, prevention, risk factors, machine learning, prediction model","lastPublishedDoi":"10.21203/rs.3.rs-7554250/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7554250/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: In frail older adults, the incidence of postoperative delirium is markedly increased, leading to greater morbidity, prolonged length of stay, and higher healthcare costs. An accurate POD prediction model can direct preventive strategies and improve patient outcomes. Employing advanced machine-learning techniques, this study develops a POD prediction model using comprehensive pre-operative and intra-operative data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We enrolled 2,089 frail patients aged ≥65 years undergoing general anesthesia for non-cardiac surgery at Fuyang People’s Hospital between February 2023 and February 2025. Thirty-eight baseline, anesthetic, and laboratory variables were extracted; missing data were handled by multiple imputation using chained equations (MICE). The dataset was randomly split 7:3 into training and validation sets. After feature selection with Boruta and LASSO, eight machine-learning models—logistic regression, random forest, support-vector classifier, XGBoost, artificial neural network, naïve Bayes, k-nearest neighbors, and decision tree—were trained and compared, with ROC-AUC as the primary metric, accompanied by accuracy, precision, recall, and F1-score. Model interpretability was achieved using SHAP analysis for the best-performing algorithm.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Among 2,089 frail elderly patients, the incidence of POD was 16.52%. After Boruta and LASSO identified 15 key predictors, the XGBoost model achieved an AUC of 0.813, outperforming the other seven algorithms. SHAP analysis identified MMSE score, Charlson Comorbidity Index, and age as the strongest predictors. External validation demonstrated high clinical utility on decision-curve analysis, with an ROC-derived sensitivity of 0.813 and specificity of 0.793, confirming robust performance without overfitting.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: This study presents a robust XGBoost-based model for predicting postoperative delirium in frail elderly patients undergoing non-cardiac surgery, demonstrating the potential of machine learning for clinical risk stratification. With its balanced performance and high accuracy, the model enables clinicians to identify high-risk patients and initiate timely interventions. Future work should focus on integration into clinical workflows and further external validation.\u003c/p\u003e","manuscriptTitle":"Development of a Machine Learning-Based Prediction Model for Postoperative Delirium in Frail Elderly Patients Undergoing Non-Cardiac Surgery Under General Anesthesia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-13 05:24:45","doi":"10.21203/rs.3.rs-7554250/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2025-10-19T02:25:11+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-09-30T13:57:57+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-30T12:25:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"European Geriatric Medicine","date":"2025-09-18T09:59:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-12T14:38:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Geriatric Medicine","date":"2025-09-10T11:07:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-geriatric-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"EGEM","sideBox":"Learn more about [European Geriatric Medicine](https://www.springer.com/journal/41999)","snPcode":"41999","submissionUrl":"https://www.editorialmanager.com/egem/default2.aspx","title":"European Geriatric Medicine","twitterHandle":"","acdcEnabled":false,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1857c2ff-e517-4b19-8f97-74f83bc75798","owner":[],"postedDate":"October 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-28T14:45:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-13 05:24:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7554250","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7554250","identity":"rs-7554250","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}