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Malignant Features on EEG Accurately Predict Prognosis after Cardiac Arrest

Updated: Oct 7, 2022

According to a study published by Westhall et al. in Neurology in Apr 2016, certain features of EEG can predict the neurological prognosis after cardiac arrest and were classified into highly malignant and malignant features.


Highly malignant EEG included any of the following features

  1. Suppressed background without discharges (see accompanying image).

  2. Suppressed background with continuous periodic discharges

  3. Burst-suppression background with or without discharges

Malignant Features on EEG Accurately Predict Prognosis after Cardiac Arrest

Malignant EEG included any of

  1. Malignant periodic or rhythmic patterns (abundant periodic discharges; abundant rhythmic polyspike-/spike-/sharp-and-wave; unequivocal electrographic seizure)

  2. Malignant background (discontinuous background; low-voltage background; reversed anterior-posterior gradient)

  3. Unreactive EEG (absence of background reactivity or only stimulus-induced discharges)

Any malignant EEG feature had a low specificity to predict poor prognosis (48%) but if 2 malignant EEG features were present specificity increased to 96% (p < 0.001).


Standardized EEG interpretation accurately predicts prognosis after cardiac arrest

Erik Westhall, et al. Neurology Apr 2016, 86 (16) 1482-1490; DOI: 10.1212/WNL.0000000000002462


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