Delirium prediction in the intensive care unit: comparison of two delirium prediction models
Annelies Wassenaar,
Lisette Schoonhoven,
John W. Devlin,
Frank M. P. van Haren,
Arjen J. C. Slooter,
Philippe G. Jorens,
Mathieu van der Jagt,
Koen S. Simons,
Ingrid Egerod,
Lisa D. Burry,
Albertus Beishuizen,
Joaquim Matos,
A. Rogier T. Donders,
Peter Pickkers,
Mark van den Boogaard
Affiliations
Annelies Wassenaar
Department of Intensive Care Medicine, Radboud Institute for Health Sciences, Radboud university medical center
Lisette Schoonhoven
Faculty of Health Sciences and National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care (Wessex), University of Southampton
John W. Devlin
School of Pharmacy, Northeastern University
Frank M. P. van Haren
Intensive Care Unit, The Canberra Hospital
Arjen J. C. Slooter
Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Centre Utrecht
Philippe G. Jorens
Department of Critical Care Medicine, Antwerp University Hospital, University of Antwerp
Mathieu van der Jagt
Department of Intensive Care, Erasmus Medical Center
Koen S. Simons
Department of Intensive Care Medicine, Jeroen Bosch Ziekenhuis
Ingrid Egerod
Intensive Care Unit, Rigshospitalet, University of Copenhagen
Lisa D. Burry
Leslie Dan Faculty of Pharmacy, University of Toronto
Albertus Beishuizen
Department of Intensive Care, Medisch Spectrum Twente
Joaquim Matos
Department of Intensive Care Medicine, Hospital Espírito Santo
A. Rogier T. Donders
Department for Health Evidence, Radboud Institute for Health Sciences, Radboud university medical center
Peter Pickkers
Department of Intensive Care Medicine, Radboud Institute for Health Sciences, Radboud university medical center
Mark van den Boogaard
Department of Intensive Care Medicine, Radboud Institute for Health Sciences, Radboud university medical center
Abstract Background Accurate 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. Methods This 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. Results In 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. Conclusions While 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 registration ClinicalTrials.gov, NCT02518646. Registered on 21 July 2015.
