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Early electroencephalography measures contribute to outcome prediction of comatose patients after cardiac arrest. We present predictive values of a new cerebral recovery index, based on a combination of quantitative electroencephalography measures, extracted every hour, and combined by the use of a random forest classifier.Prospective observational cohort study.Medical ICU of two large teaching hospitals in the Netherlands.Two hundred eighty-three consecutive comatose patients after cardiac arrest.None.Continuous electroencephalography was recorded during the first 3 days. Outcome at 6 months was dichotomized as good (Cerebral Performance Category 1–2, no or moderate disability) or poor (Cerebral Performance Category 3–5, severe disability, comatose, or death). Nine quantitative electroencephalography measures were extracted. Patients were randomly divided over a training and validation set. Within the training set, a random forest classifier was fitted for each hour after cardiac arrest. Diagnostic accuracy was evaluated in the validation set. The relative contributions of resuscitation parameters and patient characteristics were evaluated. The cerebral recovery index ranges from 0 (prediction of death) to 1 (prediction of full recovery). Poor outcome could be predicted at a threshold of 0.34 without false positives at a sensitivity of 56% at 12 hours after cardiac arrest. At 24 hours, sensitivity of 65% with a false positive rate of 6% was obtained. Good neurologic outcome could be predicted with sensitivities of 63% and 58% at a false positive rate of 6% and 7% at 12 and 24 hours, respectively. Adding patient characteristics was of limited additional predictive value.A cerebral recovery index based on a combination of intermittently extracted, optimally combined quantitative electroencephalography measures provides unequalled prognostic value for comatose patients after cardiac arrest and enables bedside EEG interpretation of unexperienced readers.