Open Access
CC BY-NC-ND 4.0 · Arq Neuropsiquiatr 2021; 79(04): 278-282
DOI: 10.1590/0004-282X-ANP-2019-0171
ARTICLE

Usefulness of a new semiological classification for characterizing psychogenic nonepileptic seizures

Utilidad de una nueva clasificación semiológica para caracterización de eventos paroxísticos no epilépticos de origen psicógeno
1   Hospital Italiano de Buenos Aires, Buenos Aires, Argentina.
,
1   Hospital Italiano de Buenos Aires, Buenos Aires, Argentina.
,
1   Hospital Italiano de Buenos Aires, Buenos Aires, Argentina.
,
1   Hospital Italiano de Buenos Aires, Buenos Aires, Argentina.
› Institutsangaben
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ABSTRACT

Background: Nonepileptic events misdiagnosed as epilepsy lead to a risk of iatrogenic morbidity, which increases health costs. Among the patients affected by nonepileptic events, 11-46% are psychogenic nonepileptic seizures (PNESs). Objective: To investigate the usefulness of the semiological classification of PNESs among patients diagnosed by means of video electroencephalograms (vEEGs). Methods: This was a retrospective review of the medical records of patients admitted to the adult vEEG unit between April 2007 and December 2016, who were diagnosed with PNES that was confirmed through vEEG. Analysis on demographic and clinical data and classification of PNESs according to the Magaudda classification were performed. Results: We identified 143 patients, among whom 31.5% had also epilepsy. According to the Magaudda classification, the events were: hypermotor (58%); subjective symptoms (21.7%); akinetic (14.7%) and focal motor (5.6%). Hypermotor predominated in both genders, followed by subjective symptoms in women (23.9%) and akinetic in men (19.2%). The mean number of antiepileptic drugs (AEDs) prescribed per patient was 2.3. Thirty-two patients (22.4%) required at least one hospitalization for PNESs. 48.3% of the patients had psychiatric comorbidities. Conclusion: The proposed semiological classification of PNESs is a relevant tool that general neurologists can use to characterize these events in their daily practice. Correct use of this classification, together with vEEG and appropriate clinical suspicion, makes it possible to reach an accurate early diagnosis, thus reducing morbidity and, possibly, the high costs associated with PNESs

RESUMEN

Introducción: Los eventos no epilépticos diagnosticados erróneamente como epilepsia conducen a un riesgo de morbilidad iatrogénica que aumenta los costes en salud. Entre los pacientes afectados por eventos no epilépticos, un 11-46% son de origen psicógeno (PNES). Objetivos: Evaluar la utilidad de la clasificación semiológica de PNES en pacientes diagnosticados por video electroencefalograma (vEEG). Métodos: Revisión retrospectiva de los registros médicos de pacientes ingresados en la unidad de adultos de vEEG entre 04-2007 y 12-2016, que fueron diagnosticados con PNES confirmado por vEEG. Se realizó un análisis de los datos demográficos y clínicos, y la clasificación de los PNES según la clasificación de Magaudda. Resultados: Identificamos 143 pacientes, el 31,5% de los cuales también tenía epilepsia. Según la clasificación de Magaudda, los eventos fueron: hipermotor 58%; síntomas subjetivos 21,7%; akinética 14,7% y motor focal 5,6%. El hipermotor predominó en ambos los sexos, seguido de síntomas subjetivos en las mujeres (23,9%) y akinéticos en los hombres (19,2%). La cantidad media de fármacos antiepilépticos (FAE) recetados por paciente fue 2.3. Un total de 32 pacientes (22.4%) requirieron al menos una hospitalización por PNES. El 48,3% de los pacientes tenía comorbilidad psiquiátrica. Conclusión: La clasificación semiológica de los PNES propuesta es una herramienta relevante que los neurólogos generales pueden usar para caracterizar esos eventos en su práctica diaria. El uso correcto de esta clasificación, vEEG y una sospecha clínica adecuada permite llegar a un diagnóstico preciso y temprano, reduciendo así la morbilidad y, posiblemente, los altos costes asociados con las PNES.

Authors’ contributions:

BIR: Conceived and designed the analysis, collected the data and wrote the paper. JCA: Conceived and designed the analysis, collected the data and performed the analysis. AGB: Contributed data or analysis tools and wrote the paper. MCG: Conceived and designed the analysis and contributed data or analysis tools.




Publikationsverlauf

Eingereicht: 13. Mai 2019

Angenommen: 18. August 2020

Artikel online veröffentlicht:
01. Juni 2023

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