• Annachiara Marra, Pratik P Pandharipande, Matthew S Shotwell, Rameela Chandrasekhar, Timothy D Girard, Ayumi K Shintani, Linda M Peelen, Moons Karl G M KGM Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands., Robert S Dittus, E Wesley Ely, and Eduard E Vasilevskis.
• Division of Allergy, Pulmonary, and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN; Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples, Federico II, Naples, Italy.
• Chest. 2018 Aug 1; 154 (2): 293-301.

BackgroundThe goal of this study was to develop and validate a dynamic risk model to predict daily changes in acute brain dysfunction (ie, delirium and coma), discharge, and mortality in ICU patients.MethodsUsing data from a multicenter prospective ICU cohort, a daily acute brain dysfunction-prediction model (ABD-pm) was developed by using multinomial logistic regression that estimated 15 transition probabilities (from one of three brain function states [normal, delirious, or comatose] to one of five possible outcomes [normal, delirious, comatose, ICU discharge, or died]) using baseline and daily risk factors. Model discrimination was assessed by using predictive characteristics such as negative predictive value (NPV). Calibration was assessed by plotting empirical vs model-estimated probabilities. Internal validation was performed by using a bootstrap procedure.ResultsData were analyzed from 810 patients (6,711 daily transitions). The ABD-pm included individual risk factors: mental status, age, preexisting cognitive impairment, baseline and daily severity of illness, and daily administration of sedatives. The model yielded very high NPVs for "next day" delirium (NPV: 0.823), coma (NPV: 0.892), normal cognitive state (NPV: 0.875), ICU discharge (NPV: 0.905), and mortality (NPV: 0.981). The model demonstrated outstanding calibration when predicting the total number of patients expected to be in any given state across predicted risk.ConclusionsWe developed and internally validated a dynamic risk model that predicts the daily risk for one of three cognitive states, ICU discharge, or mortality. The ABD-pm may be useful for predicting the proportion of patients for each outcome state across entire ICU populations to guide quality, safety, and care delivery activities.Copyright © 2018 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

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