ArticleClinical neurophysiology practice2020
Prognostic significance of specific EEG patterns after cardiac arrest in a Lisbon Cohort.
Article in Clinical neurophysiology practice, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Prognostic Significance of EEG Patterns in Post-Cardiac Arrest Coma: A Multicenter Retrospective Cohort Study of 1,000 Patients.Neurology open access · 2026Article
- Prognostic value of auditory evoked potentials in disorders of consciousness: a systematic literature review.Clinical neurophysiology practice · 2026Review
- Electroencephalography (EEG) for Neurological Prognostication in Post-Anoxic Coma Following Cardiac Arrest and Its Relationship to Outcome.Brain sciences · 2024Article
- Simplified frontal EEG in adults under veno-arterial extracorporeal membrane oxygenation.Annals of intensive care · 2021Article
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Authors and funding
6 authors.
Funding
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Abstract
objectiveTo evaluate if EEG patterns considered highly malignant are reliable predictors not only of poor neurological outcome but also reliable predictors of death.
methodsRetrospectively, EEGs from Cardiac Arrest (CA) patients of two teaching hospitals in Lisbon were classified into 3 groups: highly malignant, malignant, and benign groups. Outcome was assessed at 6 months after CA by CPC (Cerebral Performance Categories) scale. We evaluated the accuracy of these patterns to predict poor neurological outcome and death.
resultsWe included 106 patients for analysis. All patients with a highly malignant EEG (n = 37) presented a poor neurological outcome. Those patterns were also associated with death. Malignant EEG patterns were not associated with poor neurological outcome. Benign EEG patterns were associated with good neurological recovery (p < 0.0001).
conclusionHighly malignant EEG patterns were strongly associated with poor neurological outcome and can be considered to be predictors of death. SIGNIFICANCE: This study increased the knowledge about the value of EEG as a tool in outcome prediction of patients after cardiac arrest.
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