The Revised Cerebral Recovery Index Improves Predictions of Neurological Outcome after Cardiac Arrest

Publication date: Available online 27 October 2018

Source: Clinical Neurophysiology

Author(s): Sunil B Nagaraj, Marleen C. Tjepkema-Cloostermans, Barry J. Ruijter, Jeannette Hofmeijer, Michel J.A.M. van Putten

Abstract

Objective

Analysis 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.

Methods

We 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).

Results

Two 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 hours 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 hours after CA, respectively.

Conclusions

Results 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.

Significance

The rCRI is a sensitive, reliable predictor of neurological outcome of comatose patients after CA.

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