What is the Delirium Bibliography? The searchable Delirium Bibliography page is one of our most popular features, allowing you to quickly gain access to the literature on delirium and acute care of older persons. It is primarily intended for clinicians and researchers interested in exploring these topics. The NIDUS team keeps it updated for you on a monthly basis!
How to Search for Articles: Search by author, title, year, and/or keywords. Each article is indexed by keywords taken from MEDLINE and other relevant databases. Click on the title of the article to read the abstract, journal, etc.
Reference Information
- Title
- Machine Learning-Based Prediction Models for Delirium: A Systematic Review and Meta-Analysis
- Authors
- Xie, Q. Wang, X. Pei, J. Wu, Y. Guo, Q. Su, Y. Yan, H. Nan, R. Chen, H. Dou, X.
- Year
- 2022
- Journal
- J Am Med Dir Assoc
- Abstract
OBJECTIVE: To critically appraise and quantify the performance studies by employing machine learning (ML) to predict delirium. DESIGN: A systematic review and meta-analysis. SETTING AND PARTICIPANTS: Articles reporting the use of ML to predict delirium in adult patients were included. Studies were excluded if (1) the primary goal was only the identification of various risk factors for delirium; (2) the full-text article was not found; and (3) the article was published in a language other than English/Chinese. METHODS: PubMed, Embase, Cochrane Library database, Web of Science, Grey literature, and other relevant databases for the related publications were searched (from inception to December 15, 2021). The data were extracted using a standard checklist, and the risk of bias was assessed through the prediction model risk of bias assessment tool. Meta-analysis with the area under the receiver operating characteristic curve, sensitivity, and specificity as effect measures, was performed with Metadisc software. Cochran Q and I(2) statistics were used to assess the heterogeneity. Meta-regression was performed to determine the potential effect of adjustment for the key covariates. RESULTS: A total of 22 studies were included. Only 4 of 22 studies were quantitatively analyzed. The studies varied widely in reporting about the study participants, features and selection, handling of missing data, sample size calculations, and the intended clinical application of the model. For ML models, the overall pooled area under the receiver operating characteristic curve for predicting delirium was 0.89, sensitivity 0.85 (95% confidence interval 0.84‒0.85), and specificity 0.80 (95% confidence interval 0.81-0.80). CONCLUSIONS AND IMPLICATIONS: We found that the ML model showed excellent performance in predicting delirium. This review highlights the potential shortcomings of the current approaches, including low comparability and reproducibility. Finally, we present the various recommendations on how these challenges can be effectively addressed before deploying these models in prospective analyses.
- PMID
- Keywords
Machine learning algorithm
delirium
meta-analysis
predictive model
systematic review
- Page(s)
- Volume
- Issue
Title | Authors | Journal | Year | Keywords |
---|---|---|---|---|
Have you SCAND MMe Please? A framework to prevent harm during acute hospitalisation of older persons: A retrospective audit. | Redley, B. Baker, T. | Journal of Clinical Nursing | 2019 |
acute disease |
Low-Dose Ketamine Infusion to Decrease Postoperative Delirium for Spinal Fusion Patients. | Plyler, S. S. Muckler, V. C. Titch, J. F. Gupta, D. K. Rice, A. N. | J Perianesth Nurs | 2019 |
3d-cam |
Nurses' experiences of caring for older patients afflicted by delirium in a neurological department. | Kristiansen, S. Konradsen, H. Beck, M. | Journal of Clinical Nursing | 2019 |
adult |
Association of Delirium Response and Safety of Pharmacological Interventions for the Management and Prevention of Delirium: A Network Meta-analysis. | Wu, Y. C. Tseng, P. T. Tu, Y. K. Hsu, C. Y. Liang, C. S. Yeh, T. C. Chen, T. Y. Chu, C. S. Matsuoka, Y. J. Stubbs, B. Carvalho, A. F. Wada, S. Lin, P. Y. Chen, Y. W. Su, K. P. | JAMA Psychiatry | 2019 | |
Effect of electroencephalography-guided anesthetic administration on postoperative delirium among older adults undergoing major surgery the engages randomized clinical trial. | Wildes, T. S. Mickle, A. M. Abdallah, A. B. Maybrier, H. R. Oberhaus, J. Budelier, T. P. Kronzer, A. McKinnon, S. L. Park, D. Torres, B. A. Graetz, T. J. Emmert, D. A. Palanca, B. J. Goswami, S. Jordan, K. Lin, N. Fritz, B. A. Stevens, T. W. Jacobsohn, E. | JAMA | 2019 |
NCT02241655 |
Perioperative Epidural Use and Risk of Delirium in Surgical Patients: A Secondary Analysis of the PODCAST Trial. | Vlisides, P. E. Thompson, A. Kunkler, B. S. Maybrier, H. R. Avidan, M. S. Mashour, G. A. | Anesth Analg | 2019 | |
Effect of Intravenous Acetaminophen vs Placebo Combined with Propofol or Dexmedetomidine on Postoperative Delirium among Older Patients Following Cardiac Surgery: The DEXACET Randomized Clinical Trial. | Subramaniam, B. Shankar, P. Shaefi, S. Mueller, A. O'Gara, B. Banner-Goodspeed, V. Gallagher, J. Gasangwa, D. Patxot, M. Packiasabapathy, S. Mathur, P. Eikermann, M. Talmor, D. Marcantonio, E. R. | JAMA | 2019 |
NCT02546765 |
The use of a screening scale improves the recognition of delirium in older patients after cardiac surgery - a retrospective observational study. | Smulter, N. Claesson Lingehall, H. Gustafson, Y. Olofsson, B. Engstrom, K. G. | J Clin Nurs | 2019 |
Assessments scales |
Incidence and predictors of postoperative delirium in the older acute care surgery population: a prospective study. | Saravana-Bawan, B. Warkentin, L. M. Rucker, D. Carr, F. Churchill, T. A. Khadaroo, R. G. | Canadian Journal of Surgery | 2019 |
aged |
Association of Duration of Surgery With Postoperative Delirium Among Patients Receiving Hip Fracture Repair. | Ravi, B. Pincus, D. Choi, S. Jenkinson, R. Wasserstein, D. N. Redelmeier, D. A. | JAMA Netw Open | 2019 | |
Depression Predicts Delirium After Coronary Artery Bypass Graft Surgery Independent of Cognitive Impairment and Cerebrovascular Disease: An Analysis of the Neuropsychiatric Outcomes After Heart Surgery Study. | Oldham, M. A. Hawkins, K. A. Lin, I. H. Deng, Y. Hao, Q. Scoutt, L. M. Yuh, D. D. Lee, H. B. | American Journal of Geriatric Psychiatry | 2019 |
aged |
Accuracy of the Delirium Observational Screening Scale (DOS) as a screening tool for delirium in patients with advanced cancer. | Neefjes, E. C. W. van der Vorst, Mjdl Boddaert, M. S. A. Verdegaal, Batt Beeker, A. Teunissen, S. C. C. Beekman, A. T. F. Zuurmond, W. W. A. Berkhof, J. Verheul, H. M. W. | BMC Cancer | 2019 |
Delirium |
The impact of intravenous isotonic and hypotonic maintenance fluid on the risk of delirium in adult postoperative patients: retrospective before-after observational study. | Nagae, M. Egi, M. Furushima, N. Okada, M. Makino, S. Mizobuchi, S. | J Anesth | 2019 |
Delirium |
Association between delirium, adverse clinical events and functional outcomes in older patients admitted to rehabilitation settings after a hip fracture: A multicenter retrospective cohort study. | Morandi, A. Mazzone, A. Bernardini, B. Suardi, T. Prina, R. Pozzi, C. Gentile, S. Trabucchi, M. Bellelli, G. | Geriatrics & Gerontology International | 2019 |
aged |
Handover of anesthesia care is associated with an increased risk of delirium in elderly after major noncardiac surgery: results of a secondary analysis. | Liu, G. Y. Su, X. Meng, Z. T. Cui, F. Li, H. L. Zhu, S. N. Wang, D. X. | J Anesth | 2019 |
Delirium |
Undiagnosed delirium is frequent and difficult to predict: Results from a prevalence survey of a tertiary hospital. | Lange, P. W. Lamanna, M. Watson, R. Maier, A. B. | J Clin Nurs | 2019 |
Undiagnosed delirium |