Platform Session – Electroencephalography/Epilepsy: EEG for the prediction of outcome within the first five days of postanoxic coma: A prospective multicenter cohort study. (May 2018)

• Record Type: Journal Article
• Authors: Ruijter, Barry J.; Tjepkema-Cloostermans, Marleen C.; Tromp, Selma C.; van den Bergh, Walter M.; Foudraine, Norbert A.; Kornips, Francois H.; Drost, Gea; Scholten, Erik; Bosch, Frank H.; Beishuizen, Albertus; van Putten, Michel J.; Hofmeijer, Jeannette
• Is Part Of: Clinical neurophysiology. Volume 129(2018)Supplement 1
• Page Start: e231
• Page End: e232
• Publication Date: 2018-05
• Subjects: Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2018.04.595
• Languages: English
• ISSNs: 1388-2457

Abstract

Abstract : Introduction: We recently showed that the EEG at 12 and 24 h after cardiac arrest reliably predicts the outcome of postanoxic coma. In this study, we investigate whether outcome predictions could be further improved by adding unfavorable EEG categories, and by evaluation of the EEG at other time points in the first five days after cardiac arrest. We validate our findings in a larger cohort, with patients recruited from three additional centers. Methods: Data were collected in a prospective cohort study conducted at intensive care units of five hospitals in the Netherlands, between 2010 and 2017. In all comatose survivors of cardiac arrest, continuous EEG recordings were started as soon as possible and continued up to five days. Five-minute EEG epochs at 6, 12, 24, 36, 48, 72, 96, and 120 h after cardiac arrest were assessed by two independent reviewers, blinded for patients' outcome. EEG classification was based on the American Clinical Neurophysiology Society's standardized critical care EEG terminology. Patterns were categorized as favorable (continuous or nearly continuous background, with normal voltage) or unfavorable (burst-suppression with identical bursts or generalized periodic discharges (GPDs) on a suppressed background). A suppressed EEG (voltage < 10 μ V) at 12 h or later, and a low-voltage EEG (10–20 μ V) at 24 h or later, were also considered unfavorable. Outcome at 6 months after cardiac arrest was categorized as good (Cerebral Performance Category (CPC) 1–2) or poor (CPC 3–5). Results: At time of submission of this abstract, data from two participating sites (Medisch Spectrum Twente, Rijnstate) have been analyzed. In these centers, 569 patients were included, of which 46% had a good outcome. The best timing to predict either a good or poor outcome was 12 h after cardiac arrest. A favorable EEG at 12 h predicted good outcome with a sensitivity of 0.46 (95% confidence interval (CI): 0.38–0.54) at a specificity of 0.90 (95%-CI: 0.85–0.94). At 24 h or later, specificity for the prediction of good outcome dropped below 0.90. At any of the investigated time points, an unfavorable EEG pattern predicted poor outcome without false positives. At 12 h, sensitivity reached its maximum value of 0.45 (95%-CI: 0.37–0.52) at specificity of 1.00 (95%-CI: 0.98–1.00) and dropped below 0.30 at 36 h or later. Conclusion: We confirm that relevant discrimination for the prediction of good outcome after cardiac arrest with EEG is only possible up to 12 h after the event. Poor outcome can be predicted reliably up to five days, but sensitivity decreases significantly after the first 24 h. As compared to previous work, the addition of a suppressed EEG at 12 h and GPDs on a suppressed background at any time as unfavorable patterns increases sensitivity for the prediction of poor outcome. We will validate our findings on data from an additional 250 patients, recruited at the other three sites, and present these results at the ICCN 2018.