Prognostic assessment of comatose patients after cardiac arrest: construction of a simplified monitoring EEG risk score across different time windows.

OBJECTIVES: To develop a Simplified EEG Risk Score (SERS) for accurate and time-robust prognostic assessment in comatose patients after cardiac arrest. METHODS: This retrospective cohort study enrolled comatose patients after cardiac arrest admitted to the ICU of the First Affiliated Hospital of Chongqing Medical University between January 2020 and December 2024. Univariate logistic regression was used to derive β-coefficients for EEG features, including background amplitude, dominant frequency, continuity, reactivity, and sleep elements, which were incorporated into the SERS. Multivariate logistic regression identified independent predictors of poor neurological outcome. Prognostic performance was evaluated at Day 1, Days 2-5, and >5 days after cardiac arrest using receiver operating characteristic curves, with assessment of the area under the curve (AUC), accuracy, sensitivity, and specificity. RESULTS: A total of 251 comatose patients after cardiac arrest were included; 45 (18%) had good outcomes and 206 (82%) had poor outcomes. Abnormal background amplitude, slow dominant frequency (δ/θ), discontinuous background, absent reactivity, and absent sleep waveforms were all significantly associated with poor prognosis (P < 0.001). The AUCs of SERS were 0.912, 0.893, and 0.881 at Day 1, Days 2-5, and >5 days, respectively, outperforming individual EEG features, commonly used EEG scores, and traditional clinical predictors. Risk stratification demonstrated clear separation of prognosis across low-, medium-, and high-risk groups at all time windows. CONCLUSIONS: SERS shows high predictive accuracy and temporal stability, enabling reliable risk stratification for poor neurological outcomes in comatose patients after cardiac arrest, supporting its potential clinical utility.