The revised Cerebral Recovery Index improves predictions of neurological outcome after cardiac arrest

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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)

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 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. 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.

Original languageEnglish
Pages (from-to)2557-2566
Number of pages10
JournalClinical neurophysiology
Volume129
Issue number12
DOIs
Publication statusPublished - 1 Dec 2018

Keywords

  • Machine learning
  • Medical informatics
  • Post anoxic Coma
  • Quantitative EEG
  • Cardiac arrest

Fingerprint Dive into the research topics of 'The revised Cerebral Recovery Index improves predictions of neurological outcome after cardiac arrest'. Together they form a unique fingerprint.

Cite this