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Prognostic significance of specific EEG patterns after cardiac arrest in a Lisbon Cohort
Beatriz Guedes1,2, Manuel Manita1, Ana Rita Peralta3,4,2
1Área de Neurociências, Unidade de Neurofisiologia Clínica, Hospital de São José, Centro Hospitalar Universitário de Lisboa Central, Lisboa, Portugal.
Insights
Highly malignant electroencephalogram (EEG) patterns strongly predict poor neurological outcomes and death in cardiac arrest survivors. Benign patterns indicate good recovery, highlighting EEG
Area of Science:
- Neurology
- Critical Care Medicine
- Neurophysiology
Background:
- Predicting neurological outcome after cardiac arrest is crucial for patient management.
- Electroencephalogram (EEG) patterns offer potential prognostic value.
Purpose of the Study:
- To determine if highly malignant EEG patterns reliably predict poor neurological outcome and death in cardiac arrest (CA) patients.
- To assess the prognostic accuracy of different EEG classifications.
Main Methods:
- Retrospective analysis of EEGs from 106 cardiac arrest patients at two Lisbon teaching hospitals.
- EEG classification into highly malignant, malignant, and benign groups.
- Neurological outcome assessed at 6 months using the Cerebral Performance Categories (CPC) scale.
Main Results:
- All 37 patients with highly malignant EEGs had poor neurological outcomes and increased mortality.
- Malignant EEG patterns did not correlate with poor neurological outcomes.
- Benign EEG patterns were significantly associated with good neurological recovery (p < 0.0001).
Conclusions:
- Highly malignant EEG patterns are strong predictors of poor neurological outcome and death in cardiac arrest survivors.
- EEG is a valuable tool for outcome prediction in post-cardiac arrest care.
Objective:
To evaluate if EEG patterns considered highly malignant are reliable predictors not only of poor neurological outcome but also reliable predictors of death.
Methods:
Retrospectively, 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.
Results:
We 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).
Conclusion:
Highly 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.
