Observational studyCNS neuroscience & therapeutics2022
Automated machine learning-based model predicts postoperative delirium using readily extractable perioperative collected electronic data.
Observational study in CNS neuroscience & therapeutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 4 of them syntheses that pooled it.
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Who cites it
37 citing papers in PubMed, 4 syntheses or guidelines pooled it, 65 citations in OpenAlex.
- Machine learning models for predicting postoperative delirium in non-cardiac surgery patients - systematic review and meta-analysis.GeroScience · 2026Pooled it
- Machine Learning-Based prediction models for postoperative delirium: a systematic review and Meta-Analysis.BMC psychiatry · 2025Pooled it
- The role of perioperative sedative anesthetics in preventing postoperative delirium: a systematic review and network-meta analysis including 6679 patients.BMC cardiovascular disorders · 2024Pooled it
- Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools.Anesthesiology · 2024Pooled it
- Article
- Artificial intelligence for predicting perioperative anaesthetic complications and supporting clinical decision-making: a scoping review.Journal of clinical monitoring and computing · 2026Review
- Machine learning prediction of postoperative pulmonary infection in patients who underwent thoracoscopic lung cancer resection: a retrospective case-control study.BMC medical informatics and decision making · 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 machine-learning-assisted logistic regression model for predicting post-operative delirium in older adults undergoing total knee arthroplasty.Frontiers in neurology · 2026Article
- Combined analysis of the triglyceride-glucose index and melanin-concentrating hormone in metabolic dysfunction-associated fatty liver disease: a machine learning-based study.Frontiers in nutrition · 2026Article
- Multimorbidity patterns are associated with postoperative delirium in older patients undergoing non-cardiac surgery: an observational study.Frontiers in medicine · 2026Article
- Predicting postoperative complications after pneumonectomy using machine learning: a 10-year study.Annals of medicine · 2025Article
- Development of a Nomogram Model to Predict the Risk of Postoperative Delirium in Cardiac Surgery Patients.Journal of cardiovascular translational research · 2025Article
- Establishment of predictive models for postoperative delirium in elderly patients after knee/hip surgery based on total bilirubin concentration: machine learning algorithms.BMC anesthesiology · 2025Article
- A nomogram model to predict postoperative delirium in esophageal cancer patients undergoing esophagectomy.BMC cancer · 2025Article
- Developed a knowledge base of risk factors for postoperative cognitive dysfunction: a retrospective database study.Scientific reports · 2025Article
- Determining the ground truth for the prediction of delirium in adult patients in acute care: a scoping review.JAMIA open · 2025Review
- Machine Learning Multimodal Model for Delirium Risk Stratification.JAMA network open · 2025Article
- Development and Validation of a Routine Electronic Health Record-Based Delirium Prediction Model for Surgical Patients Without Dementia: Retrospective Case-Control Study.JMIR perioperative medicine · 2025Article
- Naïve Bayes is an interpretable and predictive machine learning algorithm in predicting osteoporotic hip fracture in-hospital mortality compared to other machine learning algorithms.PLOS digital health · 2025Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivePostoperative delirium (POD) is a common postoperative complication that is relevant to poor outcomes. Therefore, it is critical to find effective methods to identify patients with high risk of POD rapidly. Creating a fully automated score based on an automated machine-learning algorithm may be a method to predict the incidence of POD quickly. MATERIALS AND
methodsThis is the secondary analysis of an observational study, including 531 surgical patients who underwent general anesthesia. The least absolute shrinkage and selection operator (LASSO) was used to screen essential features associated with POD. Finally, eight features (age, intraoperative blood loss, anesthesia duration, extubation time, intensive care unit [ICU] admission, mini-mental state examination score [MMSE], Charlson comorbidity index [CCI], postoperative neutrophil-to-lymphocyte ratio [NLR]) were used to established models. Four models, logistic regression, random forest, extreme gradient boosted trees, and support vector machines, were built in a training set (70% of participants) and evaluated in the remaining testing sample (30% of participants). Multivariate logistic regression analysis was used to explore independent risk factors for POD further.
resultsModel 1 (logistic regression model) was found to outperform other classifier models in testing data (area under the curve [AUC] of 80.44%, 95% confidence interval [CI] 72.24%-88.64%) and achieve the lowest Brier Score as well. These variables including age (OR = 1.054, 95%CI: 1.017~1.093), extubation time (OR = 1.027, 95%CI: 1.012~1.044), ICU admission (OR = 2.238, 95%CI: 1.313~3.793), MMSE (OR = 0.929, 95%CI: 0.876~0.984), CCI (OR = 1.197, 95%CI: 1.038~1.384), and postoperative NLR (OR = 1.029, 95%CI: 1.002~1.057) were independent risk factors for POD in this study.
conclusionsWe have built and validated a high-performing algorithm to demonstrate the extent to which patient risk changes of POD during the perioperative period, thus leading to a rational therapeutic choice.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.