Annelies Wassenaar1, Lisette Schoonhoven2,3, John W Devlin4,5, Frank M P van Haren6,7,8, Arjen J C Slooter9, Philippe G Jorens10, Mathieu van der Jagt11, Koen S Simons12, Ingrid Egerod13, Lisa D Burry14,15, Albertus Beishuizen16, Joaquim Matos17, A Rogier T Donders18, Peter Pickkers1,19, Mark van den Boogaard1. 1. Department of Intensive Care Medicine, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands. 2. Faculty of Health Sciences and National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care (Wessex), University of Southampton, Southampton, United Kingdom. 3. Scientific Institute for Quality of Healthcare, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands. 4. School of Pharmacy, Northeastern University, Boston, MA. 5. Division of Pulmonary, Critical Care and Sleep Medicine, Tufts Medical Center, Boston, MA. 6. Intensive Care Unit, Department of Intensive Care Medicine, The Canberra Hospital, Canberra, ACT, Australia. 7. Faculty of Health, University of Canberra, Canberra, ACT, Australia. 8. College of Health and Medicine, Australian National University, Canberra, ACT, Australia. 9. Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Centre Utrecht, Utrecht, The Netherlands. 10. Department of Critical Care Medicine, Antwerp University Hospital, University of Antwerp, Edegem (Antwerp), Belgium. 11. Department of Intensive Care, Erasmus Medical Center, Rotterdam, The Netherlands. 12. Department of Intensive Care Medicine, Jeroen Bosch Ziekenhuis, 's-Hertogenbosch, The Netherlands. 13. Intensive Care Unit, Department of Intensive Care Medicine, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark. 14. Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada. 15. Department of Pharmacy, Mount Sinai Hospital, Sinai Health System, Toronto, ON, Canada. 16. Department of Intensive Care, Medisch Spectrum Twente, Enschede, The Netherlands. 17. Department of Intensive Care Medicine, Hospital Espírito Santo, Evora, Portugal. 18. Department for Health Evidence, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands. 19. Radboud Center for Infectious Diseases, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.
Abstract
OBJECTIVES: To externally validate two delirium prediction models (early prediction model for ICU delirium and recalibrated prediction model for ICU delirium) using either the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist for delirium assessment. DESIGN: Prospective, multinational cohort study. SETTING: Eleven ICUs from seven countries in three continents. PATIENTS: Consecutive, delirium-free adults admitted to the ICU for greater than or equal to 6 hours in whom delirium could be reliably assessed. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The predictors included in each model were collected at the time of ICU admission (early prediction model for ICU delirium) or within 24 hours of ICU admission (recalibrated prediction model for ICU delirium). Delirium was assessed using the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist. Discrimination was determined using the area under the receiver operating characteristic curve. The predictive performance was determined for the Confusion Assessment Method-ICU and Intensive Care Delirium Screening Checklist cohort, and compared with both prediction models' original reported performance. A total of 1,286 Confusion Assessment Method-ICU-assessed patients and 892 Intensive Care Delirium Screening Checklist-assessed patients were included. Compared with the area under the receiver operating characteristic curve of 0.75 (95% CI, 0.71-0.79) in the original study, the area under the receiver operating characteristic curve of the early prediction model for ICU delirium was 0.67 (95% CI, 0.64-0.71) for delirium as assessed using the Confusion Assessment Method-ICU and 0.70 (95% CI, 0.66-0.74) using the Intensive Care Delirium Screening Checklist. Compared with the original area under the receiver operating characteristic curve of 0.77 (95% CI, 0.74-0.79), the area under the receiver operating characteristic curve of the recalibrated prediction model for ICU delirium was 0.75 (95% CI, 0.72-0.78) for assessing delirium using the Confusion Assessment Method-ICU and 0.71 (95% CI, 0.67-0.75) using the Intensive Care Delirium Screening Checklist. CONCLUSIONS: Both the early prediction model for ICU delirium and recalibrated prediction model for ICU delirium are externally validated using either the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist for delirium assessment. Per delirium prediction model, both assessment tools showed a similar moderate-to-good statistical performance. These results support the use of either the early prediction model for ICU delirium or recalibrated prediction model for ICU delirium in ICUs around the world regardless of whether delirium is evaluated with the Confusion Assessment Method-ICU or Intensive Care Delirium Screening Checklist.
OBJECTIVES: To externally validate two delirium prediction models (early prediction model for ICU delirium and recalibrated prediction model for ICU delirium) using either the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist for delirium assessment. DESIGN: Prospective, multinational cohort study. SETTING: Eleven ICUs from seven countries in three continents. PATIENTS: Consecutive, delirium-free adults admitted to the ICU for greater than or equal to 6 hours in whom delirium could be reliably assessed. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The predictors included in each model were collected at the time of ICU admission (early prediction model for ICU delirium) or within 24 hours of ICU admission (recalibrated prediction model for ICU delirium). Delirium was assessed using the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist. Discrimination was determined using the area under the receiver operating characteristic curve. The predictive performance was determined for the Confusion Assessment Method-ICU and Intensive Care Delirium Screening Checklist cohort, and compared with both prediction models' original reported performance. A total of 1,286 Confusion Assessment Method-ICU-assessed patients and 892 Intensive Care Delirium Screening Checklist-assessed patients were included. Compared with the area under the receiver operating characteristic curve of 0.75 (95% CI, 0.71-0.79) in the original study, the area under the receiver operating characteristic curve of the early prediction model for ICU delirium was 0.67 (95% CI, 0.64-0.71) for delirium as assessed using the Confusion Assessment Method-ICU and 0.70 (95% CI, 0.66-0.74) using the Intensive Care Delirium Screening Checklist. Compared with the original area under the receiver operating characteristic curve of 0.77 (95% CI, 0.74-0.79), the area under the receiver operating characteristic curve of the recalibrated prediction model for ICU delirium was 0.75 (95% CI, 0.72-0.78) for assessing delirium using the Confusion Assessment Method-ICU and 0.71 (95% CI, 0.67-0.75) using the Intensive Care Delirium Screening Checklist. CONCLUSIONS: Both the early prediction model for ICU delirium and recalibrated prediction model for ICU delirium are externally validated using either the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist for delirium assessment. Per delirium prediction model, both assessment tools showed a similar moderate-to-good statistical performance. These results support the use of either the early prediction model for ICU delirium or recalibrated prediction model for ICU delirium in ICUs around the world regardless of whether delirium is evaluated with the Confusion Assessment Method-ICU or Intensive Care Delirium Screening Checklist.
Authors: Sung Eun Kim; Ryoung-Eun Ko; Soo Jin Na; Chi Ryang Chung; Ki Hong Choi; Darae Kim; Taek Kyu Park; Joo Myung Lee; Young Bin Song; Jin-Oh Choi; Joo-Yong Hahn; Seung-Hyuk Choi; Hyeon-Cheol Gwon; Jeong Hoon Yang Journal: Front Cardiovasc Med Date: 2022-08-03