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The role of short-term video electroencephalogram monitoring for epilepsy and psychogenic seizures: Experience from a Latin American referral center.
Gomez-Figueroa, Enrique; Vargas-Sanchez, Ángel; Alvarado-Bolaños, Alonso; Paredes-Aragón, Elma; Alatriste-Booth, Vanessa; Moreno-Avellan, Álvaro; Fernández, Maricarmen.
Afiliação
  • Gomez-Figueroa E; Neurology Department, Instituto Nacional de Neurologia y Neurocirugia, Mexico City, Mexico. Electronic address: enrique.g.figueroa@gmail.com.
  • Vargas-Sanchez Á; Neurophysiology Department, Instituto Nacional de Neurologia y Neurocirugia, Mexico City, Mexico. Electronic address: angel.vargas.s@tec.mx.
  • Alvarado-Bolaños A; Neurology Department, Instituto Nacional de Neurologia y Neurocirugia, Mexico City, Mexico.
  • Paredes-Aragón E; Neurology Department, Instituto Nacional de Neurologia y Neurocirugia, Mexico City, Mexico.
  • Alatriste-Booth V; Neurophysiology Department, Instituto Nacional de Neurologia y Neurocirugia, Mexico City, Mexico.
  • Moreno-Avellan Á; Neurophysiology Department, Instituto Nacional de Neurologia y Neurocirugia, Mexico City, Mexico.
  • Fernández M; Neurophysiology Department, Instituto Nacional de Neurologia y Neurocirugia, Mexico City, Mexico.
J Clin Neurosci ; 82(Pt A): 105-110, 2020 Dec.
Article em En | MEDLINE | ID: mdl-33317716
ABSTRACT
Short-term VEEG represents an affordable option in limited resources environments. There are few reports on its use. Its diagnostic yield is variable (7-57%) and can be related to the differences in recording time. The present study analyzes possible predictive factors to support the indication of a short-term VEEG. We analyzed short-term VEEG studies (<24 h) throughout a period of 5 years (2013-2017). The patients were clustered according to the date of last epileptic seizure and the frequency of epileptic events per month and subcategorized depending on the frequency found. Chi square univariate analysis was performed looking for predictive variables to obtain an epileptic short-term EEG. A multivariate logistic regression analysis was performed with statistically significant variables. A total of 1092 VEEG were analyzed from 832 patients. 34.5% were reported as epileptic VEEG. In the multivariate analysis, 3 predictors of epileptic short-term VEEG were identified The use of 2 or more antiepileptic drugs (AEDs) (OR 1.67, CI 1.23-2.25, p = 0.001), the presence of an epileptic event in the last month (OR 1.53, CI 1.07-2.17, p = 0.018) and daily seizures (OR 1.84, CI 1.21-2.78, p = 0.004). Six-month seizure free subjects predict a non-epileptic VEEG (OR 0.58, CI 0.30-0.89, p = 0.013).
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Convulsões / Gravação em Vídeo / Monitorização Ambulatorial / Eletroencefalografia / Epilepsia / Monitorização Neurofisiológica Tipo de estudo: Prognostic_studies Limite: Adolescent / Adult / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Convulsões / Gravação em Vídeo / Monitorização Ambulatorial / Eletroencefalografia / Epilepsia / Monitorização Neurofisiológica Tipo de estudo: Prognostic_studies Limite: Adolescent / Adult / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2020 Tipo de documento: Article