Interrater variability of EEG interpretation in comatose cardiac arrest patients. Issue 12 (December 2015)

• Record Type: Journal Article
• Authors: Westhall, Erik; Rosén, Ingmar; Rossetti, Andrea O.; van Rootselaar, Anne-Fleur; Wesenberg Kjaer, Troels; Friberg, Hans; Horn, Janneke; Nielsen, Niklas; Ullén, Susann; Cronberg, Tobias
• Is Part Of: Clinical neurophysiology. Volume 126:Issue 12(2015:Dec.)
• Page Start: 2397
• Page End: 2404
• Publication Date: 2015-12
• Subjects: ACNS American Clinical Neurophysiology Society -- TTM Target Temperature Management; EEG -- Prognosis -- Interrater variability -- Brain injury -- Cardiac arrest -- Hypothermia; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2015.03.017
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: We used a standardized EEG terminology to assess post cardiac arrest EEGs. A highly malignant EEG was identified with high reliability by four interpreters. The interrater agreement for identifying an unreactive EEG was only fair. Abstract: Objective: EEG is widely used to predict outcome in comatose cardiac arrest patients, but its value has been limited by lack of a uniform classification. We used the EEG terminology proposed by the American Clinical Neurophysiology Society (ACNS) to assess interrater variability in a cohort of cardiac arrest patients included in the Target Temperature Management trial. The main objective was to evaluate if malignant EEG-patterns could reliably be identified. Methods: Full-length EEGs from 103 comatose cardiac arrest patients were interpreted by four EEG-specialists with different nationalities who were blinded for patient outcome. Percent agreement and kappa ( κ ) for the categories in the ACNS EEG terminology and for prespecified malignant EEG-patterns were calculated. Results: There was substantial interrater agreement ( κ 0.71) for highly malignant patterns and moderate agreement ( κ 0.42) for malignant patterns. Substantial agreement was found for malignant periodic or rhythmic patterns ( κ 0.72) while agreement for identifying an unreactive EEG was fair ( κ 0.26). Conclusions: The ACNS EEG terminology can be used to identify highly malignant EEG-patterns in post cardiac arrest patients in an international context with high reliability. Significance: The establishment of strict criteria with high transferability between interpreters will increase the usefulness of routine EEG to assess neurological prognosis after cardiac arrest.