Trial reportCritical care (London, England)2018
Delirium prediction in the intensive care unit: comparison of two delirium prediction models.
Trial report in Critical care (London, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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.
DElirium prediCtIon in the intenSIve Care Unit: Head to Head comparisON of Two Delirium Prediction Models
Constructing a Model of Pupillary Parameters in Predicting Delirium Among Critically Ill Patients in the Intensive Unit
Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 81 citations in OpenAlex.
- Risk prediction models for delirium in ICU patients: a systematic review and critical appraisal.BMC anesthesiology · 2025Pooled it
- Development of a machine learning-based prediction model for hypothyroidism-associated delirium in elderly hypothyroid patients in the intensive care unit.BMC geriatrics · 2026Article
- Development of the ERATbi App, a Clinical Decision Support System for Early Recovery After Traumatic Brain Injury in the ICU: Usability Study.JMIR human factors · 2026Article
- Online machine learning model for predicting delirium risk in elderly patients with chronic kidney disease: development and preliminary validation.European journal of medical research · 2025Article
- Adherence to optimal delirium management practices in intensive care units in Brazil: a nationwide survey.JBI evidence implementation · 2025Article
- Validation of PREdiction of DELIRium in ICu patients (PRE-DELIRIC) model for ICU delirium in general ICU and patients with liver disease: a retrospective cohort study.Journal of intensive care · 2025Article
- Development and internal verification of nomogram for forecasting delirium in the elderly admitted to intensive care units: an analysis of MIMIC-IV database.Frontiers in neurology · 2025Article
- Predictive model for assessment of ICU delirium among patients in critical care units: Prospective observational study.Journal of education and health promotion · 2025Article
- Advancing Delirium Treatment Trials in Older Adults: Recommendations for Future Trials From the Network for Investigation of Delirium: Unifying Scientists (NIDUS).Critical care medicine · 2025Article
- Interpretable machine learning model for early prediction of delirium in elderly patients following intensive care unit admission: a derivation and validation study.Frontiers in medicine · 2024Article
- External validity of an automated delirium prediction model (DEMO) and comparison to the manual VMS-questions: a retrospective cohort study.International journal of clinical pharmacy · 2023Article
- Establishment of a model for predicting the outcome of induced labor in full-term pregnancy based on machine learning algorithm.Scientific reports · 2022Article
- Development and validation of prediction models for neurocognitive disorders in adult patients admitted to the ICU with sleep disturbance.CNS neuroscience & therapeutics · 2022Article
- External validation and comparison of two delirium prediction models in patients admitted to the cardiac intensive care unit.Frontiers in cardiovascular medicine · 2022Article
- A Machine Learning-Based Algorithm for the Prediction of Intensive Care Unit Delirium (PRIDE): Retrospective Study.JMIR medical informatics · 2021Article
- Article
- Delirium in Intensive Care.Current anesthesiology reports · 2021Review
- Tools Are Needed to Promote Sedation Practices for Mechanically Ventilated Patients.Frontiers in medicine · 2021Article
- Poor outcomes of delirium in the intensive care units are amplified by increasing age: A retrospective cohort study.World journal of emergency medicine · 2021Article
- ICU Delirium-Prediction Models: A Systematic Review.Critical care explorations · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 11 institutions in 8 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate prediction of delirium in the intensive care unit (ICU) may facilitate efficient use of early preventive strategies and stratification of ICU patients by delirium risk in clinical research, but the optimal delirium prediction model to use is unclear. We compared the predictive performance and user convenience of the prediction model for delirium (PRE-DELIRIC) and early prediction model for delirium (E-PRE-DELIRIC) in ICU patients and determined the value of a two-stage calculation.
methodsThis 7-country, 11-hospital, prospective cohort study evaluated consecutive adults admitted to the ICU who could be reliably assessed for delirium using the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist. The predictive performance of the models was measured using the area under the receiver operating characteristic curve. Calibration was assessed graphically. A physician questionnaire evaluated user convenience. For the two-stage calculation we used E-PRE-DELIRIC immediately after ICU admission and updated the prediction using PRE-DELIRIC after 24 h.
resultsIn total 2178 patients were included. The area under the receiver operating characteristic curve was significantly greater for PRE-DELIRIC (0.74 (95% confidence interval 0.71-0.76)) compared to E-PRE-DELIRIC (0.68 (95% confidence interval 0.66-0.71)) (z score of - 2.73 (p < 0.01)). Both models were well-calibrated. The sensitivity improved when using the two-stage calculation in low-risk patients. Compared to PRE-DELIRIC, ICU physicians (n = 68) rated the E-PRE-DELIRIC model more feasible.
conclusionsWhile both ICU delirium prediction models have moderate-to-good performance, the PRE-DELIRIC model predicts delirium better. However, ICU physicians rated the user convenience of E-PRE-DELIRIC superior to PRE-DELIRIC. In low-risk patients the delirium prediction further improves after an update with the PRE-DELIRIC model after 24 h.
trial registrationClinicalTrials.gov, NCT02518646 . Registered on 21 July 2015.
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.