Delirium prediction in the intensive care unit: comparison of two delirium prediction models
Delirium prediction in the intensive care unit: comparison of two delirium prediction models
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.
Adult, Clinical prediction, Critical illness, Delirium, Intensive care unit
1-9
Wassenaar, Annelies
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Schoonhoven, Lisette
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Devlin, John W.
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van Haren, Frank M.P.
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Slooter, Arjen J.C.
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Jorens, Philippe G.
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van der Jagt, Mathieu
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Simons, Koen S.
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Egerod, Ingrid
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Burry, Lisa D.
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Beishuizen, Albertus
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Matos, Joaquim
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Donders, A. Rogier T.
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Pickkers, Peter
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van den Boogaard, Mark
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5 May 2018
Wassenaar, Annelies
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Schoonhoven, Lisette
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Devlin, John W.
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van Haren, Frank M.P.
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Slooter, Arjen J.C.
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Jorens, Philippe G.
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van der Jagt, Mathieu
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Simons, Koen S.
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Egerod, Ingrid
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Burry, Lisa D.
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Beishuizen, Albertus
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Matos, Joaquim
e18c38e7-109a-4a00-9a9c-9c02f7cabfea
Donders, A. Rogier T.
dd864788-b402-459b-b4e7-ecd7ebaf1cad
Pickkers, Peter
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van den Boogaard, Mark
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Wassenaar, Annelies, Schoonhoven, Lisette, Devlin, John W., van Haren, Frank M.P., Slooter, Arjen J.C., Jorens, Philippe G., van der Jagt, Mathieu, Simons, Koen S., Egerod, Ingrid, Burry, Lisa D., Beishuizen, Albertus, Matos, Joaquim, Donders, A. Rogier T., Pickkers, Peter and van den Boogaard, Mark
(2018)
Delirium prediction in the intensive care unit: comparison of two delirium prediction models.
Critical Care, 22 (1), , [114].
(doi:10.1186/s13054-018-2037-6).
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.
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Delirium prediction in the intensive care unit: comparison of two delirium prediction models
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Accepted/In Press date: 13 April 2018
e-pub ahead of print date: 5 May 2018
Published date: 5 May 2018
Keywords:
Adult, Clinical prediction, Critical illness, Delirium, Intensive care unit
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Local EPrints ID: 420860
URI: http://eprints.soton.ac.uk/id/eprint/420860
ISSN: 1364-8535
PURE UUID: 93aa19ca-2264-4f23-b79e-f3e6aadadb6d
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Date deposited: 17 May 2018 16:30
Last modified: 16 Apr 2024 01:44
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Contributors
Author:
Annelies Wassenaar
Author:
John W. Devlin
Author:
Frank M.P. van Haren
Author:
Arjen J.C. Slooter
Author:
Philippe G. Jorens
Author:
Mathieu van der Jagt
Author:
Koen S. Simons
Author:
Ingrid Egerod
Author:
Lisa D. Burry
Author:
Albertus Beishuizen
Author:
Joaquim Matos
Author:
A. Rogier T. Donders
Author:
Peter Pickkers
Author:
Mark van den Boogaard
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