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Paroxysmal slow wave events predict epilepsy following a first seizure
OBJECTIVE: Management of a patient presenting with a first seizure depends on the risk of additional seizures. In clinical practice, the recurrence risk is estimated by the treating physician using the neurological examination, brain imaging, a thorough history for risk factors, and routine scalp el...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9298770/ https://www.ncbi.nlm.nih.gov/pubmed/34750812 http://dx.doi.org/10.1111/epi.17110 |
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author | Zelig, Daniel Goldberg, Ilan Shor, Oded Ben Dor, Shira Yaniv‐Rosenfeld, Amit Milikovsky, Dan Z. Ofer, Jonathan Imtiaz, Hamza Friedman, Alon Benninger, Felix |
author_facet | Zelig, Daniel Goldberg, Ilan Shor, Oded Ben Dor, Shira Yaniv‐Rosenfeld, Amit Milikovsky, Dan Z. Ofer, Jonathan Imtiaz, Hamza Friedman, Alon Benninger, Felix |
author_sort | Zelig, Daniel |
collection | PubMed |
description | OBJECTIVE: Management of a patient presenting with a first seizure depends on the risk of additional seizures. In clinical practice, the recurrence risk is estimated by the treating physician using the neurological examination, brain imaging, a thorough history for risk factors, and routine scalp electroencephalogram (EEG) to detect abnormal epileptiform activity. The decision to use antiseizure medication can be challenging when objective findings are missing. There is a need for new biomarkers to better diagnose epilepsy following a first seizure. Recently, an EEG‐based novel analytical method was reported to detect paroxysmal slowing in the cortical network of patients with epilepsy. The aim of our study is to test this method's sensitivity and specificity to predict epilepsy following a first seizure. METHODS: We analyzed interictal EEGs of 70 patients admitted to the emergency department of a tertiary referral center after a first seizure. Clinical data from a follow‐up period of at least 18 months were available. EEGs of 30 healthy controls were also analyzed and included. For each EEG, we applied an automated algorithm to detect paroxysmal slow wave events (PSWEs). RESULTS: Of patients presenting with a first seizure, 40% had at least one additional recurring seizure and were diagnosed with epilepsy. Sixty percent did not report additional seizures. A significantly higher occurrence of PSWEs was detected in the first interictal EEG test of those patients who were eventually diagnosed with epilepsy. Conducting the EEG test within 72 h after the first seizure significantly increased the likelihood of detecting PSWEs and the predictive value for epilepsy up to 82%. SIGNIFICANCE: The quantification of PSWEs by an automated algorithm can predict epilepsy and help the neurologist in evaluating a patient with a first seizure. |
format | Online Article Text |
id | pubmed-9298770 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92987702022-07-21 Paroxysmal slow wave events predict epilepsy following a first seizure Zelig, Daniel Goldberg, Ilan Shor, Oded Ben Dor, Shira Yaniv‐Rosenfeld, Amit Milikovsky, Dan Z. Ofer, Jonathan Imtiaz, Hamza Friedman, Alon Benninger, Felix Epilepsia Research Article OBJECTIVE: Management of a patient presenting with a first seizure depends on the risk of additional seizures. In clinical practice, the recurrence risk is estimated by the treating physician using the neurological examination, brain imaging, a thorough history for risk factors, and routine scalp electroencephalogram (EEG) to detect abnormal epileptiform activity. The decision to use antiseizure medication can be challenging when objective findings are missing. There is a need for new biomarkers to better diagnose epilepsy following a first seizure. Recently, an EEG‐based novel analytical method was reported to detect paroxysmal slowing in the cortical network of patients with epilepsy. The aim of our study is to test this method's sensitivity and specificity to predict epilepsy following a first seizure. METHODS: We analyzed interictal EEGs of 70 patients admitted to the emergency department of a tertiary referral center after a first seizure. Clinical data from a follow‐up period of at least 18 months were available. EEGs of 30 healthy controls were also analyzed and included. For each EEG, we applied an automated algorithm to detect paroxysmal slow wave events (PSWEs). RESULTS: Of patients presenting with a first seizure, 40% had at least one additional recurring seizure and were diagnosed with epilepsy. Sixty percent did not report additional seizures. A significantly higher occurrence of PSWEs was detected in the first interictal EEG test of those patients who were eventually diagnosed with epilepsy. Conducting the EEG test within 72 h after the first seizure significantly increased the likelihood of detecting PSWEs and the predictive value for epilepsy up to 82%. SIGNIFICANCE: The quantification of PSWEs by an automated algorithm can predict epilepsy and help the neurologist in evaluating a patient with a first seizure. John Wiley and Sons Inc. 2021-11-09 2022-01 /pmc/articles/PMC9298770/ /pubmed/34750812 http://dx.doi.org/10.1111/epi.17110 Text en © 2021 The Authors. Epilepsia 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 | Research Article Zelig, Daniel Goldberg, Ilan Shor, Oded Ben Dor, Shira Yaniv‐Rosenfeld, Amit Milikovsky, Dan Z. Ofer, Jonathan Imtiaz, Hamza Friedman, Alon Benninger, Felix Paroxysmal slow wave events predict epilepsy following a first seizure |
title | Paroxysmal slow wave events predict epilepsy following a first seizure |
title_full | Paroxysmal slow wave events predict epilepsy following a first seizure |
title_fullStr | Paroxysmal slow wave events predict epilepsy following a first seizure |
title_full_unstemmed | Paroxysmal slow wave events predict epilepsy following a first seizure |
title_short | Paroxysmal slow wave events predict epilepsy following a first seizure |
title_sort | paroxysmal slow wave events predict epilepsy following a first seizure |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9298770/ https://www.ncbi.nlm.nih.gov/pubmed/34750812 http://dx.doi.org/10.1111/epi.17110 |
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