ArticleBMJ open2022
Head-to-head comparison of 14 prediction models for postoperative delirium in elderly non-ICU patients: an external validation study.
Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05992506 (Electroencephalographic Biomarker to Predict the Development of Postoperative Delirium), which is not on this map. Cited by 8 papers, 2 of them syntheses that pooled it.
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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.
Electroencephalographic Biomarker to Predict the Development of Postoperative Delirium: a Protocol of an Observational Study in a Cohort of Patients From Five Centers
Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 12 citations in OpenAlex.
- Development and validation of an international preoperative risk assessment model for postoperative delirium.Age and ageing · 2023Pooled it
- Risk of bias in prognostic models of hospital-induced delirium for medical-surgical units: A systematic review.PloS one · 2023Pooled it
- Retrospective external validation of the Mayo Delirium Prediction tool in a Swiss cohort of medical and surgical inpatients.BMC medical informatics and decision making · 2026Article
- Prediction of postoperative delirium in older adults from preoperative cognition and occipital alpha power from resting-state electroencephalogram.Age and ageing · 2025Observational
- Predicting ICU Delirium in Critically Ill COVID-19 Patients Using Demographic, Clinical, and Laboratory Admission Data: A Machine Learning Approach.Life (Basel, Switzerland) · 2025Article
- Prospective external validation of the automated PIPRA multivariable prediction model for postoperative delirium on real-world data from a consecutive cohort of non-cardiac surgery inpatients.BMJ health & care informatics · 2025Article
- Postoperative delirium: A tragedy for elderly cancer patients.World journal of gastrointestinal oncology · 2024Article
- Introducing a machine learning algorithm for delirium prediction-the Supporting SURgery with GEriatric Co-Management and AI project (SURGE-Ahead).Age and ageing · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesDelirium is associated with increased morbidity, mortality, prolonged hospitalisation and increased healthcare costs. The number of clinical prediction models (CPM) to predict postoperative delirium has increased exponentially. Our goal is to perform a head-to-head comparison of CPMs predicting postoperative delirium in non-intensive care unit (non-ICU) elderly patients to identify the best performing models.
settingSingle-site university hospital.
designSecondary analysis of prospective cohort study. PARTICIPANTS AND INCLUSION: CPMs published within the timeframe of 1 January 1990 to 1 May 2020 were checked for eligibility (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). For the time period of 1 January 1990 to 1 January 2017, included CPMs were identified in systematic reviews based on prespecified inclusion and exclusion criteria. An extended literature search for original studies was performed independently by two authors, including CPMs published between 1 January 2017 and 1 May 2020. External validation was performed using a surgical cohort consisting of 292 elderly non-ICU patients. PRIMARY OUTCOME MEASURES: Discrimination, calibration and clinical usefulness.
results14 CPMs were eligible for analysis out of 366 full texts reviewed. External validation was previously published for 8/14 (57%) CPMs. C-indices ranged from 0.52 to 0.74, intercepts from -0.02 to 0.34, slopes from -0.74 to 1.96 and scaled Brier from -1.29 to 0.088. Based on predefined criteria, the two best performing models were those of Dai
conclusionOur head-to-head analysis identified 2 out of 14 CPMs as best-performing models with a fair discrimination and acceptable calibration. Based on our findings, these models might assist physicians in postoperative delirium risk estimation and patient selection for preventive measures.
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