title: Multinational development and validation of an early prediction model for delirium in ICU patients.
abstract: BACKGROUND
  Delirium incidence in intensive care unit (ICU) patients is high and associated with poor outcome. Identification of high-risk patients may facilitate its prevention.
  
  
  OBJECTIVE
  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.
  
  
  METHODS
  Prospective 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.
  
  
  RESULTS
  In 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.
  
  
  CONCLUSIONS
  Patients' 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.
authors.affiliation: Scientific Institute for Quality of Healthcare, Radboud Institute for Health Sciences, Radboud university medical center, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands, annelies.wassenaar@radboudumc.nl.
authors.forename: A
authors.initials: A
authors.lastname: Wassenaar
authors.forename: M
authors.initials: M
authors.lastname: van den Boogaard
authors.forename: T
authors.initials: T
authors.lastname: van Achterberg
authors.forename: A J C
authors.initials: AJ
authors.lastname: Slooter
authors.forename: M A
authors.initials: MA
authors.lastname: Kuiper
authors.forename: M E
authors.initials: ME
authors.lastname: Hoogendoorn
authors.forename: K S
authors.initials: KS
authors.lastname: Simons
authors.forename: E
authors.initials: E
authors.lastname: Maseda
authors.forename: N
authors.initials: N
authors.lastname: Pinto
authors.forename: C
authors.initials: C
authors.lastname: Jones
authors.forename: A
authors.initials: A
authors.lastname: Luetz
authors.forename: A
authors.initials: A
authors.lastname: Schandl
authors.forename: W
authors.initials: W
authors.lastname: Verbrugghe
authors.forename: L M
authors.initials: LM
authors.lastname: Aitken
authors.forename: F M P
authors.initials: FM
authors.lastname: van Haren
authors.forename: A R T
authors.initials: AR
authors.lastname: Donders
authors.forename: L
authors.initials: L
authors.lastname: Schoonhoven
authors.forename: P
authors.initials: P
authors.lastname: Pickkers
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003661:Decision Support Techniques; D003693:Delirium / Q000175:diagnosis* / Q000517:prevention & control; D005260:Female; D005544:Forecasting; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D055815:Young Adult