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
- Postoperative delirium prediction using machine learning models and preoperative electronic health record data
- Authors
- Bishara, A. Chiu, C. Whitlock, E. L. Douglas, V. C. Lee, S. Butte, A. J. Leung, J. M. Donovan, A. L.
- Year
- 2022
- Journal
- BMC Anesthesiol
- Abstract
BACKGROUND: Accurate, pragmatic risk stratification for postoperative delirium (POD) is necessary to target preventative resources toward high-risk patients. Machine learning (ML) offers a novel approach to leveraging electronic health record (EHR) data for POD prediction. We sought to develop and internally validate a ML-derived POD risk prediction model using preoperative risk features, and to compare its performance to models developed with traditional logistic regression. METHODS: This was a retrospective analysis of preoperative EHR data from 24,885 adults undergoing a procedure requiring anesthesia care, recovering in the main post-anesthesia care unit, and staying in the hospital at least overnight between December 2016 and December 2019 at either of two hospitals in a tertiary care health system. One hundred fifteen preoperative risk features including demographics, comorbidities, nursing assessments, surgery type, and other preoperative EHR data were used to predict postoperative delirium (POD), defined as any instance of Nursing Delirium Screening Scale ≥2 or positive Confusion Assessment Method for the Intensive Care Unit within the first 7 postoperative days. Two ML models (Neural Network and XGBoost), two traditional logistic regression models (“clinician-guided” and “ML hybrid”), and a previously described delirium risk stratification tool (AWOL-S) were evaluated using the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive likelihood ratio, and positive predictive value. Model calibration was assessed with a calibration curve. Patients with no POD assessments charted or at least 20% of input variables missing were excluded. RESULTS: POD incidence was 5.3%. The AUC-ROC for Neural Net was 0.841 [95% CI 0. 816-0.863] and for XGBoost was 0.851 [95% CI 0.827-0.874], which was significantly better than the clinician-guided (AUC-ROC 0.763 [0.734-0.793], p < 0.001) and ML hybrid (AUC-ROC 0.824 [0.800-0.849], p < 0.001) regression models and AWOL-S (AUC-ROC 0.762 [95% CI 0.713-0.812], p < 0.001). Neural Net, XGBoost, and ML hybrid models demonstrated excellent calibration, while calibration of the clinician-guided and AWOL-S models was moderate; they tended to overestimate delirium risk in those already at highest risk. CONCLUSION: Using pragmatically collected EHR data, two ML models predicted POD in a broad perioperative population with high discrimination. Optimal application of the models would provide automated, real-time delirium risk stratification to improve perioperative management of surgical patients at risk for POD.
- PMID
- Keywords
Aged
Cohort Studies
Delirium/*diagnosis
Electronic Health Records/*statistics & numerical data
Female
Humans
*Machine Learning
Male
Middle Aged
Postoperative Complications/*diagnosis
Predictive Value of Tests
Preoperative Period
Reproducibility of Results
Retrospective Studies
*Delirium prevention
*Geriatric surgery
*Postoperative delirium
*Risk prediction model
improve healthcare quality interventions. 2.AJB is a co-founder and consultant to
Personalis and NuMedii
consultant to Samsung, Mango Tree Corporation, and in the
recent past, 10x Genomics, Helix, Pathway Genomics, and Verinata (Illumina)
has
served on paid advisory panels or boards for Geisinger Health, Regenstrief
Institute, Gerson Lehman Group, AlphaSights, Covance, Novartis, Genentech, and
Merck, and Roche
is a shareholder in Personalis and NuMedii
is a minor shareholder
in Apple, Facebook, Alphabet (Google), Microsoft, Amazon, Snap, 10x Genomics,
Illumina, CVS, Nuna Health, Assay Depot, Vet24seven, Regeneron, Sanofi, Royalty
Pharma, AstraZeneca, Moderna, Biogen, and Sutro, and several other non-health
related companies and mutual funds
and has received honoraria and travel
reimbursement for invited talks from Johnson and Johnson, Roche, Genentech, Pfizer,
Merck, Lilly, Takeda, Varian, Mars, Siemens, Optum, Abbott, Celgene, AstraZeneca,
AbbVie, Westat, and many academic institutions, medical or disease specific
foundations and associations, and health systems. Atul Butte receives royalty
payments through Stanford University, for several patents and other disclosures
licensed to NuMedii and Personalis. Atul Butte’s research has been funded by NIH,
Northrup Grumman (as the prime on an NIH contract), Genentech, Johnson and Johnson,
FDA, Robert Wood Johnson Foundation, Leon Lowenstein Foundation, Intervalien
Foundation, Priscilla Chan and Mark Zuckerberg, the Barbara and Gerson Bakar
Foundation, and in the recent past, the March of Dimes, Juvenile Diabetes Research
Foundation, California Governor’s Office of Planning and Research, California
Institute for Regenerative Medicine, L’Oreal, and Progenity. 3.The above
organizations had no direct involvement in study design
in the collection,
analysis, and interpretation of data
in the writing of the report
or in the
decision to submit the article for publication. 4.The remainder of authors declare
no conflicts of interest.
- Page(s)
- 8
- Volume
- 22
- Issue
- 1
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 |