Observational studyEpilepsia open2024
Prevalence of non-convulsive seizures and electroencephalographic abnormalities in critically ill patients-A retrospective observational study.
Observational study in Epilepsia open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 4 citations in OpenAlex.
- Lightweight deep learning model for nonconvulsive status epilepticus diagnosis using EEG time-frequency analysis.Scientific reports · 2026Article
- EEG for bedside monitoring: the intensivist's point of view.Critical care (London, England) · 2026Review
- The Role of Artificial Intelligence in the Diagnosis and Management of Non-convulsive Seizures: A Narrative Review.Cureus · 2025Review
- Post-traumatic epilepsy: bridging pathogenesis, diagnosis, and pharmacotherapeutic strategies.Frontiers in pharmacology · 2025Review
- Prevalence of non-convulsive seizures and electroencephalographic abnormalities in critically ill patients-A retrospective observational study.Epilepsia open · 2024Observational
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveElectroencephalographic (EEG) abnormalities especially non-convulsive status epilepticus (NCSE) have been found to be associated with worse outcomes in critically ill patients. We aimed to assess the prevalence of non-convulsive seizures and electroencephalographic abnormalities in critically ill patients. Furthermore, we aimed to investigate any association between the type of EEG abnormality and outcomes including ICU mortality and successful ICU discharge.
methodsThis was a cross-sectional observational study carried out among critically ill patients in a mixed medical-surgical ICU from January 1, 2018 to May 15, 2020. A total of 178 records of 30 min bedside EEG records were found. EEG findings were grouped as normal, non-convulsive seizures (NCS), non-convulsive status epilepticus (NCSE), and other abnormalities. Descriptive analytical tools were used to characterize the case details in terms of the type of EEG abnormalities. Chi square test was used to describe the EEG abnormalities in terms of mortality. The status epilepticus severity scores (STESS) were further calculated for records with NCSE. These data were then analyzed for any association between STESS and mortality for cases with NCSE.
resultsThe prevalence of EEG abnormality in our cohort of all critically ill patients was found to be 7.3% (170/2234). Among the patients with altered sensorium in whom EEG was done, 42.9% had non-conclusive seizure activity with 25.2% in NCSE. Though the study was not adequately powered, there was a definite trend towards a lower proportion of successful ICU discharge rates seen among patients with higher STESS (>2) with only 33.3% being discharged for patients with a STESS of 6 versus 92.9% for those with STESS 3. SIGNIFICANCE: When combined with a strong clinical suspicion, even a 30-min bedside EEG can result in detection of EEG abnormalities including NCS and NCSE. Hence, EEG should be regularly included in the evaluation of critically ill patients with altered sensorium. PLAIN LANGUAGE SUMMARY: Electroencephalographic (EEG) abnormalities and seizures can have high prevalence in critically ill patients. These abnormalities notably, non-convulsive status epilepticus (NCSE) has been found to be associated with poor patient outcomes. This was a retrospective observational study analyzing 178 EEG records, from a mixed medical-surgical ICU. The indication for obtaining an EEG was based solely on the clinical suspicion of the treating physician. The study found a high prevalence of EEG abnormalities in 96.5% in whom it was obtained with 42.9% having any seizure activity and 28.8% having NCSE. The study was not powered for detection of association of the EEG abnormalities with clinical outcomes. However, a definite trend towards decreased chances of successful discharge from the ICU was seen. This study used strong clinical suspicion in patients with altered sensorium to obtain an EEG. High detection rates of EEG abnormalities were recorded in this study. Hence, combination of clinical judgement and EEG can improve detection of EEG abnormalities and NCSE.
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