ArticleJournal of neurology2026
Structured EEG report complements the prognostic stratification of status epilepticus.
Article in Journal of neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundStatus epilepticus (SE) is a critical neurological emergency with mortality rates reaching 15-25%. While several clinical scores exist for risk stratification of SE, they tend to perform poorly in a real-world scenario, where the prognostic role of electroencephalography (EEG) remains secondary.
objectivesThis study aimed to evaluate the clinical utility of existing prognostic scales and to determine if a simplified, structured EEG reporting framework provides incremental value in predicting mortality and length of stay (LOS).
methodsWe retrospectively analyzed 182 adult SE patients (mean age 67 ± 15.9 years) between 2018 and 2024. We collected clinical scores and structured EEG features (based on Salzburg criteria and nomenclature from the American Clinical Neurophysiology Society position paper on critical care EEG). Outcomes included 30-day in-hospital mortality and LOS, analyzed using logistic regression, ROC curves, and Cox proportional-hazards models.
resultsIn-hospital mortality was 23.1%, and mean LOS was 26.1 ± 29.8 days. Clinical scores alone demonstrated acceptable predictive value for mortality (AUC 0.60-0.76), but suffered from low specificity. Adding structured EEG features significantly improved mortality prediction (AUC = 0.88). Notably, plasma markers of systemic involvement primarily drove mortality prediction, whereas EEG patterns-specifically Salzburg criteria and spatio-temporal evolution-were strong predictors of LOS.
conclusionsCurrent clinical scores demonstrate limited accuracy in the risk stratification of SE due to high etiological variability. In contrast, structured EEG interpretation significantly enhances prognostic specificity and provides distinct predictive value for LOS. Future efforts should focus on integrating EEG parameters with existing clinical scores to harmonize and improve risk stratification models.
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