ArticleCNS neuroscience & therapeutics2023
Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study.
Article in CNS neuroscience & therapeutics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 3 of them syntheses that pooled it.
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Who cites it
39 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine learning application in the prediction of postoperative delirium among elderly patients: a systematic review and meta-analysis.Langenbeck's archives of surgery · 2026Pooled it
- Machine learning models for predicting postoperative delirium in non-cardiac surgery patients - systematic review and meta-analysis.GeroScience · 2026Pooled it
- Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools.Anesthesiology · 2024Pooled it
- Improving Delirium Prediction in Surgery: Enhanced Machine Learning Models vs. Traditional Risk Scores.Journal of Korean medical science · 2026Article
- Development of a machine learning-based prediction model for hypothyroidism-associated delirium in elderly hypothyroid patients in the intensive care unit.BMC geriatrics · 2026Article
- Clustering of disease trajectories with explainable machine learning: A case study on postoperative delirium phenotypes.PLOS digital health · 2026Article
- Machine learning and artificial intelligence for delirium prediction with Electronic Health Records (EHR): a scoping review.BMC medical informatics and decision making · 2026Article
- A Dynamic Prognosis Model of Patients with Chronic Heart Failure: A Prospective Cohort Study Using Follow-Up Data and Recurrent Neural Networks.Vascular health and risk management · 2026Observational
- Development and validation of a machine learning model for predicting 3-year recurrence of adenomatous colorectal polyps after EMR: a multicenter study.Frontiers in oncology · 2026Article
- Online machine learning model for predicting delirium risk in elderly patients with chronic kidney disease: development and preliminary validation.European journal of medical research · 2025Article
- Association of transversus abdominis plane block (TAPB) with postoperative delirium (POD) in elderly patients undergoing major gastrointestinal surgery: a retrospective cohort study.BMC anesthesiology · 2025Article
- Leveraging small-sample machine learning for rigorous prediction of JOA recovery in cervical spondylotic myelopathy patients: insights from imaging parameters and modeling strategies.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2025Article
- The prediction models for the optimal timing of surgical intervention for necrotizing enterocolitis: nomogram vs. five machine learning models.Pediatric surgery international · 2025Article
- Development and validation of a prediction model for postoperative delirium in older patients undergoing major noncardiac surgery: protocol for a prospective multi-centre cohort study.BMC geriatrics · 2025Article
- Prediction model for postoperative pulmonary complications after thoracoscopic surgery with machine learning algorithms and SHapley Additive exPlanations.Journal of thoracic disease · 2025Article
- Development of a Machine Learning-Based Predictive Model for Postoperative Delirium in Older Adult Intensive Care Unit Patients: Retrospective Study.Journal of medical Internet research · 2025Article
- A prediction model for diabetes complications using the Kokuho Database and its application to public health services in Japan.Journal of diabetes investigation · 2025Article
- Determining the ground truth for the prediction of delirium in adult patients in acute care: a scoping review.JAMIA open · 2025Review
- Development of a Disease Model for Predicting Postoperative Delirium Using Combined Blood Biomarkers.Annals of clinical and translational neurology · 2025Article
- Comparison of machine learning and nomogram to predict 30-day in-hospital mortality in patients with acute myocardial infarction combined with cardiogenic shock: a retrospective study based on the eICU-CRD and MIMIC-IV databases.BMC cardiovascular disorders · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsTo compare the performance of logistic regression and machine learning methods in predicting postoperative delirium (POD) in elderly patients.
methodThis was a retrospective study of perioperative medical data from patients undergoing non-cardiac and non-neurology surgery over 65 years old from January 2014 to August 2019. Forty-six 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-ROC), sensitivity, specificity, and precision.
resultsIn total, 29,756 patients were enrolled, and the incidence of POD was 3.22% after variable screening. AUCs were 0.783 (0.765-0.8) 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%.
conclusionsThe 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.
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