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

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

doi:10.1371/journal.pone.0237639

Abstract

BackgroundRisk 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.MethodsThis 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.ResultsA 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.ConclusionsOur 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.

Highlights

Delirium is a serious and distressing neuropsychiatric syndrome [1] frequently experienced by patients in the intensive care unit (ICU) [2]
The dynamic Acute Brain Dysfunction-prediction model (ABD-pm) [15], has been internally validated using the Confusion Assessment Method-ICU (CAM-ICU) to predict day Development and validation of delirium prediction models for critically ill adults based on admission acuity status among critically ill adults including those with delirium at time of ICU admission
Development and validation of delirium prediction models for critically ill adults based on admission acuity assessments, and number of Intensive Care Delirium Screening Checklist (ICDSC) assessments 4), or if an Acute Physiology and Chronic Health Evaluation (APACHE) II score was not reported

Summary

Introduction

Delirium is a serious and distressing neuropsychiatric syndrome [1] frequently experienced by patients in the intensive care unit (ICU) [2]. Risk prediction models allow clinicians to forecast which individuals are at a higher risk for developing a particular outcome [6], and to implement interventions specific to the patients’ individual risk profile. An accurate delirium prediction model is regarded as a powerful tool for an ICU clinician to facilitate early implementation of prevention measures [7]. Characterizing the risk profile associated with ICU delirium incidence for patients at their particular level of acuity at ICU admission might inform efforts to improve efficiency, value, and quality of ICU patient care. 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

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