ArticleIntensive care medicine2015
Multinational development and validation of an early prediction model for delirium in ICU patients.
Article in Intensive care medicine, 2015. 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 91 papers, 3 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.
DElirium prediCtIon in the intenSIve Care Unit: Head to Head comparisON of Two Delirium Prediction Models
Continuous Non-Invasive Eye Tracking for the Early Detection of Delirium on the Intensive Care Unit
Who cites it
91 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Risk prediction models for delirium in ICU patients: a systematic review and critical appraisal.BMC anesthesiology · 2025Pooled it
- Diagnostic models for sepsis-associated encephalopathy: a comprehensive systematic review and meta-analysis.Frontiers in neurology · 2025Pooled it
- 2021 KSCCM clinical practice guidelines for pain, agitation, delirium, immobility, and sleep disturbance in the intensive care unit.Acute and critical care · 2022Guideline
- Melatonin for prevention of delirium in patients receiving mechanical ventilation in the intensive care unit: a multiarm multistage adaptive randomized controlled clinical trial (DEMEL).Intensive care medicine · 2025Trial
- Prediction model for delirium in patients with cardiovascular surgery: development and validation.Journal of cardiothoracic surgery · 2022Trial
- Trial
- Delirium prediction in the intensive care unit: comparison of two delirium prediction models.Critical care (London, England) · 2018Trial
- Trial
- Rosuvastatin versus placebo for delirium in intensive care and subsequent cognitive impairment in patients with sepsis-associated acute respiratory distress syndrome: an ancillary study to a randomised controlled trial.The Lancet. Respiratory medicine · 2016Trial
- A large language model-driven multidisciplinary AI agent system predicts delirium in emergency critically ill patients.Cell reports. Medicine · 2026Article
- Quality of Life in Intensive Care Unit Survivors-Family Member Dyads: A Conceptual Framework and Longitudinal Study Protocol.Nursing in critical care · 2026Article
- Predicting ICU Delirium with a Regularized Logistic Regression Model: Device-Support Burden as an Informative Predictor Domain in a Turkish Cohort.Diagnostics (Basel, Switzerland) · 2026Article
- Prediction of delirium in trauma patients using interpretable machine learning.Scientific reports · 2026Article
- Translating predictive models into clinical practice: fast-and-frugal trees for postoperative delirium using routine data.Scientific reports · 2026Article
- Predicting delirium in critically Ill COVID-19 patients using EEG-derived data: a machine learning approach.GeroScience · 2026Observational
- Derivation and validation of a prediction rule for sedative-associated delirium during acute respiratory failure requiring mechanical ventilation.Journal of critical care · 2026Article
- Association between acute kidney injury and delirium in critically ill patients: a retrospective cohort study using two independent databases.Critical care (London, England) · 2026Article
- Unravelling the complex inflammatory landscape of COVID-19 infection: a pathway to biomarkers identification in infection-associated delirium in the ICU.Intensive care medicine experimental · 2025Article
- A large language model for delirium prediction in the intensive care unit using structured electronic health records.Scientific reports · 2025Article
- Trajectories of blood urea nitrogen and 28-day mortality in patients with sepsis-associated delirium: a retrospective cohort analysis of the MIMIC-IV database.BMC infectious diseases · 2025Article
31 more citing papers are in PubMed but not listed here.
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
rationaleDelirium incidence in intensive care unit (ICU) patients is high and associated with poor outcome. Identification of high-risk patients may facilitate its prevention. PURPOSE: To develop and validate a model based on data available at ICU admission to predict delirium development during a patient's complete ICU stay and to determine the predictive value of this model in relation to the time of delirium development.
methodsProspective cohort study in 13 ICUs from seven countries. Multiple logistic regression analysis was used to develop the early prediction (E-PRE-DELIRIC) model on data of the first two-thirds and validated on data of the last one-third of the patients from every participating ICU.
resultsIn total, 2914 patients were included. Delirium incidence was 23.6%. The E-PRE-DELIRIC model consists of nine predictors assessed at ICU admission: age, history of cognitive impairment, history of alcohol abuse, blood urea nitrogen, admission category, urgent admission, mean arterial blood pressure, use of corticosteroids, and respiratory failure. The area under the receiver operating characteristic curve (AUROC) was 0.76 [95% confidence interval (CI) 0.73-0.77] in the development dataset and 0.75 (95% CI 0.71-0.79) in the validation dataset. The model was well calibrated. AUROC increased from 0.70 (95% CI 0.67-0.74), for delirium that developed <2 days, to 0.81 (95% CI 0.78-0.84), for delirium that developed >6 days.
conclusionPatients' delirium risk for the complete ICU length of stay can be predicted at admission using the E-PRE-DELIRIC model, allowing early preventive interventions aimed to reduce incidence and severity of ICU delirium.
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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.