SynthesisBMC psychiatry2025
Machine Learning-Based prediction models for postoperative delirium: a systematic review and Meta-Analysis.
Synthesis in BMC psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Development and internal validation of an interpretable machine learning prognostic prediction model for postoperative delirium after esophagectomy.Journal of thoracic disease · 2026Article
- Plasma Amyloid-β 1-40 and Postoperative Delirium After Cardiac Surgery.JAMA network open · 2026Observational
- Machine learning models for predicting postoperative delirium after noncardiac surgery: A comparative study.The Journal of international medical research · 2026Article
- Explainable artificial intelligence in anesthesiology prediction models: bridging the gap from black-box algorithms to clinical decision support.Frontiers in medicine · 2026Review
- Postoperative delirium risk in elderly high-risk patients undergoing hip and knee arthroplasty (ASA Ⅲ-Ⅳ): construction and validation of a nomogram model based on frail frailty index.Frontiers in medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundThe number of risk prediction models for postoperative delirium(POD) has increased yearly, but their quality and applicability in clinical practice and future research remain unclear.
aimsThis systematic review aimed to evaluate published studies on POD risk prediction models, evaluate the diagnostic accuracy of these models, and provide guidance for model establishment and improvement.
methodsWe conducted a systematic search in PubMed, Embase, the Cochrane Library, and other scientific databases to identify eligible studies published up to February 15, 2025. The included studies provided data on the sensitivity and specificity of predictive models.
resultsWe included 17 articles with 69 machine learning (ML) prediction models, encompassing 205,202 individuals who underwent surgerg, among whom 50,899 developed POD (24.8%). The average number of cases per study was 12,070, with the largest study including 163,436 cases. The combined area under the receiver operating characteristic curve (AUROC) for predicting POD, calculated by averaging the AUROC values reported in the studies, was 0.83 [95% CI, 0.79-0.86], with a sensitivity of 0.73 [95% CI, 0.68-0.74] and a specificity of 0.79 [95% CI, 0.74-0.82]. Subgroup analysis revealed that the random forest model exhibited the highest pooled AUROC, reaching 0.89 [95% CI, 0.86-0.92]. Notably, models predicting POD for orthopedic surgeries (AUROC: 0.88 [95% CI, 0.85-0.90]) and patients younger than 60 years (AUROC: 0.84 [95% CI, 0.81-0.87]) demonstrated superior predictive performance. Furthermore, models that underwent only internal validation had lower predictive performance compared to those validated both internally and externally (AUROC: 0.84 [95% CI, 0.81-0.87]). Compared to models developed for European and American populations, those applied to Asian populations performed better (AUROC: 0.85 [95% CI, 0.82-0.88]). Across the included studies, advanced age, preoperative cognitive impairment, comorbidities, anemia, and hypoalbuminemia were the most consistently reported predictors of POD .
conclusionsML models perform well in predicting the occurrence of POD, with stable performance across various surgical and demographic subgroups.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.