ArticleBMC psychiatry2023
Machine learning-based prediction model for postoperative delirium in non-cardiac surgery.
Article in BMC psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- 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
- Machine Learning-Based prediction models for postoperative delirium: a systematic review and Meta-Analysis.BMC psychiatry · 2025Pooled it
- Machine learning-based prediction model for predicting the impact of insulin resistance on the risk of ischemic cardiomyopathy.BMC medical informatics and decision making · 2026Article
- Machine Learning for Predicting Critical Postoperative Interventions: Proof-of-Concept Study Using the INSPIRE Dataset.JMIR perioperative medicine · 2026Article
- Evolving Trends and Collaborative Networks in Postoperative Delirium Research: A Two-Decade Bibliometric Analysis.Brain and behavior · 2026Article
- Machine learning models for predicting postoperative delirium after noncardiac surgery: A comparative study.The Journal of international medical research · 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
- Clinical clusters during acute illness predict long-term mortality in older patients.BMC medicine · 2025Article
- Multicenter validation of a scalable, interpretable, multitask prediction model for multiple clinical outcomes.NPJ digital medicine · 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
- Determining the ground truth for the prediction of delirium in adult patients in acute care: a scoping review.JAMIA open · 2025Review
- Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study.Journal of medical Internet research · 2025Article
- Article
- Enhancing Delirium Prediction and Prevention in Elderly Patients Through Machine Learning-Based Analysis.Sultan Qaboos University medical journal · 2025Article
- Predictors of postoperative complications following thyroidectomy: A systematic review.Surgery in practice and science · 2024Review
- Evaluation of machine learning approach for surgical results of Ahmed valve implantation in patients with glaucoma.BMC ophthalmology · 2024Article
- Article
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPostoperative delirium is a common complication that is distressing. This study aimed to demonstrate a prediction model for delirium.
methodsAmong 203,374undergoing non-cardiac surgery between January 2011 and June 2019 at Samsung Medical Center, 2,865 (1.4%) were diagnosed with postoperative delirium. After comparing performances of machine learning algorithms, we chose variables for a prediction model based on an extreme gradient boosting algorithm. Using the top five variables, we generated a prediction model for delirium and conducted an external validation. The Kaplan-Meier and Cox survival analyses were used to analyse the difference of delirium occurrence in patients classified as a prediction model.
resultsThe top five variables selected for the postoperative delirium prediction model were age, operation duration, physical status classification, male sex, and surgical risk. An optimal probability threshold in this model was estimated to be 0.02. The area under the receiver operating characteristic (AUROC) curve was 0.870 with a 95% confidence interval of 0.855-0.885, and the sensitivity and specificity of the model were 0.76 and 0.84, respectively. In an external validation, the AUROC was 0.867 (0.845-0.877). In the survival analysis, delirium occurred more frequently in the group of patients predicted as delirium using an internal validation dataset (p < 0.001).
conclusionBased on machine learning techniques, we analyzed a prediction model of delirium in patients who underwent non-cardiac surgery. Screening for delirium based on the prediction model could improve postoperative care. The working model is provided online and is available for further verification among other populations.
trial registrationKCT 0006363.
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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.