Epileptiform patterns in electroencephalography and clinical myoclonic seizures predict unfavorable outcomes in patients after cardiac arrest: a meta-analysis protocol

Abstract Objective To accurately predict neurological outcomes among survivors after cardiac arrest (CA), we aim to analyze the experimental data of epileptiform electroencephalography (EEG) in CA survivors to explore the value of predictors of epileptiform EEG. We also aim to refine the timeline of EEG examination, thus providing a foundation for the prediction of the neurological outcomes of CA survivors in clinical practice. Methods The PubMed, Embase, Web of Science, and Cochrane Library databases will be searched from 2010 to 2025 to identify relevant literature based on previously established search terms. We will include observational studies that reported epileptiform EEG patterns and neurological outcomes in comatose survivors of CA. Studies involving patients under 18 years of age or with a prior history of epilepsy will be excluded. Methodological quality was assessed using quality assessment of diagnostic accuracy studies 2 (QUADAS-2). The GRADE framework will be used to assess the quality of evidence of the included studies. Meta-analysis will be performed using Stata 18 software. Discussion EEG is widely used to predict neurological recovery after CA, yet the prognostic value of epileptiform patterns remains debated regarding its time dependency and optimal use in multimodal approaches. This meta-analysis will address these gaps by assessing how predictive accuracy varies across EEG recording time windows (≤ 24, 24–48, > 48 h) to define optimal timing, and by synthesizing data on multiple EEG markers to inform multimodal prognostication. Our findings aim to refine outcome prediction for CA survivors. Systemic review registration PROSPERO 2023 CRD42023468222. Available from https://www.crd.york.ac.uk/prospero/display_record.php ? ID = CRD42023468222