Cargando…

Risk factors that predict delayed seizure detection on continuous electroencephalogram (cEEG) in a large sample size of critically ill patients

OBJECTIVE: Majority of seizures are detected within 24 hours on continuous EEG (cEEG). Some patients have delayed seizure detection after 24 hours. The purpose of this research was to identify risk factors that predict delayed seizure detection and to determine optimal cEEG duration for various pati...

Descripción completa

Detalles Bibliográficos
Autores principales: Zawar, Ifrah, Briskin, Isaac, Hantus, Stephen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8886063/
https://www.ncbi.nlm.nih.gov/pubmed/34913615
http://dx.doi.org/10.1002/epi4.12572
_version_ 1784660568859213824
author Zawar, Ifrah
Briskin, Isaac
Hantus, Stephen
author_facet Zawar, Ifrah
Briskin, Isaac
Hantus, Stephen
author_sort Zawar, Ifrah
collection PubMed
description OBJECTIVE: Majority of seizures are detected within 24 hours on continuous EEG (cEEG). Some patients have delayed seizure detection after 24 hours. The purpose of this research was to identify risk factors that predict delayed seizure detection and to determine optimal cEEG duration for various patient subpopulations. METHODS: We retrospectively identified all patients ≥18 years of age who underwent cEEG at Cleveland clinic during calendar year 2016. Clinical and EEG data for all patients and time to seizure detection for seizure patients were collected. RESULTS: Twenty‐four hundred and two patients met inclusion criteria. Of these, 316 (13.2%) had subclinical seizures. Sixty‐five (20.6%) patients had delayed seizures detection after 24 hours. Seizure detection increased linearly till 36 hours of monitoring, and odds of seizure detection increased by 46% for every additional day of monitoring. Delayed seizure risk factors included stupor (13.2% after 48 hours, P = .031), lethargy (25.9%, P = .013), lateralized (LPDs) (27.7%, P = .029) or generalized periodic discharges (GPDs) (33.3%, P = .022), acute brain insults (25.5%, P = .036), brain bleeds (32.8%, P = .014), especially multiple concomitant bleeds (61.1%, P < .001), altered mental status (34.7%, P = .001) as primary cEEG indication, and use of antiseizure medications (27.8%, P < .001) at cEEG initiation. SIGNIFICANCE: Given the linear seizure detection trend, 36 hours of standard monitoring appears more optimal than 24 hours especially for high‐risk patients. For awake patients without epileptiform discharges, <24 hours of monitoring appears sufficient. Previous studies have shown that coma and LPDs predict delayed seizure detection. We found that stupor and lethargy were also associated with delayed seizure detection. LPDs and GPDs were associated with delayed seizures. Other delayed seizure risk factors included acute brain insults, brain bleeds especially multiple concomitant bleeds, altered mental status as primary cEEG indication, and use of ASMs at cEEG initiation. Longer cEEG (≥48 hours) is suggested for these high‐risk patients.
format Online
Article
Text
id pubmed-8886063
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-88860632022-03-04 Risk factors that predict delayed seizure detection on continuous electroencephalogram (cEEG) in a large sample size of critically ill patients Zawar, Ifrah Briskin, Isaac Hantus, Stephen Epilepsia Open Original Articles OBJECTIVE: Majority of seizures are detected within 24 hours on continuous EEG (cEEG). Some patients have delayed seizure detection after 24 hours. The purpose of this research was to identify risk factors that predict delayed seizure detection and to determine optimal cEEG duration for various patient subpopulations. METHODS: We retrospectively identified all patients ≥18 years of age who underwent cEEG at Cleveland clinic during calendar year 2016. Clinical and EEG data for all patients and time to seizure detection for seizure patients were collected. RESULTS: Twenty‐four hundred and two patients met inclusion criteria. Of these, 316 (13.2%) had subclinical seizures. Sixty‐five (20.6%) patients had delayed seizures detection after 24 hours. Seizure detection increased linearly till 36 hours of monitoring, and odds of seizure detection increased by 46% for every additional day of monitoring. Delayed seizure risk factors included stupor (13.2% after 48 hours, P = .031), lethargy (25.9%, P = .013), lateralized (LPDs) (27.7%, P = .029) or generalized periodic discharges (GPDs) (33.3%, P = .022), acute brain insults (25.5%, P = .036), brain bleeds (32.8%, P = .014), especially multiple concomitant bleeds (61.1%, P < .001), altered mental status (34.7%, P = .001) as primary cEEG indication, and use of antiseizure medications (27.8%, P < .001) at cEEG initiation. SIGNIFICANCE: Given the linear seizure detection trend, 36 hours of standard monitoring appears more optimal than 24 hours especially for high‐risk patients. For awake patients without epileptiform discharges, <24 hours of monitoring appears sufficient. Previous studies have shown that coma and LPDs predict delayed seizure detection. We found that stupor and lethargy were also associated with delayed seizure detection. LPDs and GPDs were associated with delayed seizures. Other delayed seizure risk factors included acute brain insults, brain bleeds especially multiple concomitant bleeds, altered mental status as primary cEEG indication, and use of ASMs at cEEG initiation. Longer cEEG (≥48 hours) is suggested for these high‐risk patients. John Wiley and Sons Inc. 2021-12-23 /pmc/articles/PMC8886063/ /pubmed/34913615 http://dx.doi.org/10.1002/epi4.12572 Text en © 2021 The Authors. Epilepsia Open published by Wiley Periodicals LLC on behalf of International League Against Epilepsy https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Original Articles
Zawar, Ifrah
Briskin, Isaac
Hantus, Stephen
Risk factors that predict delayed seizure detection on continuous electroencephalogram (cEEG) in a large sample size of critically ill patients
title Risk factors that predict delayed seizure detection on continuous electroencephalogram (cEEG) in a large sample size of critically ill patients
title_full Risk factors that predict delayed seizure detection on continuous electroencephalogram (cEEG) in a large sample size of critically ill patients
title_fullStr Risk factors that predict delayed seizure detection on continuous electroencephalogram (cEEG) in a large sample size of critically ill patients
title_full_unstemmed Risk factors that predict delayed seizure detection on continuous electroencephalogram (cEEG) in a large sample size of critically ill patients
title_short Risk factors that predict delayed seizure detection on continuous electroencephalogram (cEEG) in a large sample size of critically ill patients
title_sort risk factors that predict delayed seizure detection on continuous electroencephalogram (ceeg) in a large sample size of critically ill patients
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8886063/
https://www.ncbi.nlm.nih.gov/pubmed/34913615
http://dx.doi.org/10.1002/epi4.12572
work_keys_str_mv AT zawarifrah riskfactorsthatpredictdelayedseizuredetectiononcontinuouselectroencephalogramceeginalargesamplesizeofcriticallyillpatients
AT briskinisaac riskfactorsthatpredictdelayedseizuredetectiononcontinuouselectroencephalogramceeginalargesamplesizeofcriticallyillpatients
AT hantusstephen riskfactorsthatpredictdelayedseizuredetectiononcontinuouselectroencephalogramceeginalargesamplesizeofcriticallyillpatients