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Development and validation of delirium prediction model for critically ill adults parameterized to ICU admission acuity

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  • Stephana J Cherak
  • Andrea Soo
  • Kyla N Brown
  • E Wesley Ely
  • Henry T Stelfox
  • Kirsten M Fiest

Abstract

Background: Risk prediction models allow clinicians to forecast which individuals are at a higher risk for developing a particular outcome. We developed and internally validated a delirium prediction model for incident delirium parameterized to patient ICU admission acuity. Methods: This retrospective, observational, fourteen medical-surgical ICU cohort study evaluated consecutive delirium-free adults surviving hospital stay with ICU length of stay (LOS) greater than or equal to 24 hours with both an admission APACHE II score and an admission type (e.g., elective post-surgery, emergency post-surgery, non-surgical) in whom delirium was assessed using the Intensive Care Delirium Screening Checklist (ICDSC). Risk factors included in the model were readily available in electric medical records. Least absolute shrinkage and selection operator logistic (LASSO) regression was used for model development. Discrimination was determined using area under the receiver operating characteristic curve (AUC). Internal validation was performed by cross-validation. Predictive performance was determined using measures of accuracy and clinical utility was assessed by decision-curve analysis. Results: A total of 8,878 patients were included. Delirium incidence was 49.9% (n = 4,431). The delirium prediction model was parameterized to seven patient cohorts, admission type (3 cohorts) or mean quartile APACHE II score (4 cohorts). All parameterized cohort models were well calibrated. The AUC ranged from 0.67 to 0.78 (95% confidence intervals [CI] ranged from 0.63 to 0.79). Model accuracy varied across admission types; sensitivity ranged from 53.2% to 63.9% while specificity ranged from 69.0% to 74.6%. Across mean quartile APACHE II scores, sensitivity ranged from 58.2% to 59.7% while specificity ranged from 70.1% to 73.6%. The clinical utility of the parameterized cohort prediction model to predict and prevent incident delirium was greater than preventing incident delirium by treating all or none of the patients. Conclusions: Our results support external validation of a prediction model parameterized to patient ICU admission acuity to predict a patients’ risk for ICU delirium. Classification of patients’ risk for ICU delirium by admission acuity may allow for efficient initiation of prevention measures based on individual risk profiles.

Suggested Citation

  • Stephana J Cherak & Andrea Soo & Kyla N Brown & E Wesley Ely & Henry T Stelfox & Kirsten M Fiest, 2020. "Development and validation of delirium prediction model for critically ill adults parameterized to ICU admission acuity," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-18, August.
  • Handle: RePEc:plo:pone00:0237639
    DOI: 10.1371/journal.pone.0237639
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    References listed on IDEAS

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    1. Teus H. Kappen & Yvonne Vergouwe & Wilton A. van Klei & Leo van Wolfswinkel & Cor J. Kalkman & Karel G. M. Moons, 2012. "Adaptation of Clinical Prediction Models for Application in Local Settings," Medical Decision Making, , vol. 32(3), pages 1-10, May.
    2. Richard D Riley & Jill A Hayden & Ewout W Steyerberg & Karel G M Moons & Keith Abrams & Panayiotis A Kyzas & Núria Malats & Andrew Briggs & Sara Schroter & Douglas G Altman & Harry Hemingway & for the, 2013. "Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research," PLOS Medicine, Public Library of Science, vol. 10(2), pages 1-9, February.
    3. Ewout W Steyerberg & Karel G M Moons & Danielle A van der Windt & Jill A Hayden & Pablo Perel & Sara Schroter & Richard D Riley & Harry Hemingway & Douglas G Altman & for the PROGRESS Group, 2013. "Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research," PLOS Medicine, Public Library of Science, vol. 10(2), pages 1-9, February.
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