Assessing both early and late EEG patterns improves prediction of outcome after cardiac arrest

Objective: Previously proposed “synchronous EEG patterns” predict poor outcome within 24 h after cardiac arrest (CA). We investigate the prognostic performance of these early EEG predictors in addition to the late EEG predictors (>24 h) recommended in the European post-resuscitation guidelines. Methods: Observational substudy of the TTM2-trial including consecutive comatose resuscitated patients. Continuous EEG-monitoring (cEEG) was blindly assessed using the American Clinical Neurophysiology Societýs standardised EEG terminology and categorised into early EEG predictors (burst-suppression with identical or highly epileptiform bursts, or suppression with generalised periodic discharges) and late EEG predictors (heterogenous burst-suppression or suppression). Poor outcome was defined as modified Rankin Scale 4–6 at six months. Results: Of 191 included patients, 53 % had poor outcome. Early EEG predictors had 100 %[CI 96–100] specificity at all time-points and maximal sensitivity 30 %[CI 21–40] before 24 h. Late EEG predictors had 100 %[CI 96–100] specificity beyond 24 h with maximal sensitivity 32 %[CI 21–43]. Using both early and late EEG predictors, and gradually adding cEEG-information from consecutive time-epochs, sensitivity increased to 49 %[CI 39–59] up to 36 h after CA (p = 0.001). A continuous background within 12 h predicted good outcome (sensitivity 61 %[CI 50–71]; specificity 87 %[CI 79–93]). Conclusion: Searching for both early EEG predictors (e.g. identical burst-suppression) and late EEG predictors (e.g. heterogenous burst-suppression > 24 h) significantly improved sensitivity of poor outcome prediction without false positive survivors in this cohort. A self-fulfilling prophecy may have affected our results. cEEG during the first two days after CA identified half of the patients with a long-term poor outcome and half of the patients with a good outcome.