• Sunil B Nagaraj, Marleen C Tjepkema-Cloostermans, Barry J Ruijter, Jeannette Hofmeijer, and van Putten Michel J A M MJAM Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, Enschede, Netherlands; Clinical Neurophysiology Group, Univers.
• Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Netherlands. Electronic address: s.belur.nagaraj@umcg.nl.
• Clin Neurophysiol. 2018 Dec 1; 129 (12): 2557-2566.

ObjectiveAnalysis of the electroencephalogram (EEG) background pattern helps predicting neurological outcome of comatose patients after cardiac arrest (CA). Visual analysis may not extract all discriminative information. We present predictive values of the revised Cerebral Recovery Index (rCRI), based on continuous extraction and combination of a large set of evolving quantitative EEG (qEEG) features and machine learning techniques.MethodsWe included 551 subsequent patients from a prospective cohort study on continuous EEG after CA in two hospitals. Outcome at six months was classified as good (Cerebral Performance Category (CPC) 1-2) or poor (CPC 3-5). Forty-four qEEG features (from time, frequency and entropy domain) were selected by the least absolute shrinkage and selection operator (LASSO) method and used in a Random Forests classification system. We trained and evaluated the system with 10-fold cross validation. For poor outcome prediction, the sensitivity at 100% specificity (Se100) and the area under the receiver operator curve (AUC) were used as performance of the prediction model. For good outcome, we used the sensitivity at 95% specificity (Se95).ResultsTwo hundred fifty-six (47%) patients had a good outcome. The rCRI predicted poor outcome with AUC = 0.94 (95% CI: 0.83-0.91), Se100 = 0.66 (0.65-0.78), and AUC = 0.88 (0.78-0.93), Se100 = 0.60 (0.51-0.75) at 12 and 24 h after CA, respectively. The rCRI predicted good outcome with Se95 = 0.72 (0.61-0.85) and 0.40 (0.30-0.51) at 12 and 24 h after CA, respectively.ConclusionsResults obtained in this study suggest that with machine learning algorithms and large set of qEEG features, it is possible to efficiently monitor patient outcome after CA. We also demonstrate the importance of selection of optimal performance metric to train a classifier model for outcome prediction.SignificanceThe rCRI is a sensitive, reliable predictor of neurological outcome of comatose patients after CA.Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

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