Literature DB >> 30884437

Reliability of reported peri-ictal behavior to identify psychogenic nonepileptic seizures.

Wesley T Kerr1, Andrea M Chau2, Emily A Janio2, Chelsea T Braesch2, Justine M Le3, Jessica M Hori2, Akash B Patel2, Norma L Gallardo2, Janar Bauirjan2, Corinne H Allas2, Amir H Karimi2, Eric S Hwang2, Emily C Davis2, Albert Buchard2, David Torres-Barba2, Shannon D'Ambrosio2, Mona Al Banna2, Andrew Y Cho2, Jerome Engel4, Mark S Cohen5, John M Stern6.   

Abstract

PURPOSE: Differentiating psychogenic non-epileptic seizures (PNES) from epileptic seizures (ES) can be difficult, even when expert clinicians have video recordings of seizures. Moreover, witnesses who are not trained observers may provide descriptions that differ from the expert clinicians', which often raises concern about whether the patient has both ES and PNES. As such, quantitative, evidence-based tools to help differentiate ES from PNES based on patients' and witnesses' descriptions of seizures may assist in the early, accurate diagnosis of patients.
METHODS: Based on patient- and observer-reported data from 1372 patients with diagnoses documented by video-elect roencephalography (vEEG), we used logistic regression (LR) to compare specific peri-ictal behaviors and seizure triggers in five mutually exclusive groups: ES, PNES, physiologic non-epileptic seizure-like events, mixed PNES plus ES, and inconclusive monitoring. To differentiate PNES-only from ES-only, we retrospectively trained multivariate LR and a forest of decision trees (DF) to predict the documented diagnoses of 246 prospective patients.
RESULTS: The areas under the receiver operating characteristic curve (AUCs) of the DF and LR were 75% and 74%, respectively (empiric 95% CI of chance 37-62%). The overall accuracy was not significantly higher than the naïve assumption that all patients have ES (accuracy DF 71%, LR 70%, naïve 68%, p > 0.05).
CONCLUSIONS: Quantitative analysis of patient- and observer-reported peri-ictal behaviors objectively changed the likelihood that a patient's seizures were psychogenic, but these reports were not reliable enough to be diagnostic in isolation. Instead, our scores may identify patients with "probable" PNES that, in the right clinical context, may warrant further diagnostic assessment.
Copyright © 2019 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Clinical decision making; Decision trees; Dissociative seizures; Machine learning; Multiple imputation; Screening

Mesh:

Year:  2019        PMID: 30884437      PMCID: PMC6526032          DOI: 10.1016/j.seizure.2019.02.021

Source DB:  PubMed          Journal:  Seizure        ISSN: 1059-1311            Impact factor:   3.184


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