ArticleFrontiers in neurology2022
Diagnostic value of an algorithm for autoimmune epilepsy in a retrospective cohort.
Article in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Higher incidence of acute symptomatic seizures in probable antibody-negative pediatric autoimmune encephalitis than in major antibody-positive autoimmune encephalitis.Frontiers in neurology · 2024Article
- Neuroinflammatory mediators in acquired epilepsy: an update.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2023Review
- Intermittent hypoxia: linkage between OSAS and epilepsy.Frontiers in pharmacology · 2023Review
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11 authors.
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Abstract
Purpose: This study aims to propose a diagnostic algorithm for autoimmune epilepsy in a retrospective cohort and investigate its clinical utility. Methods: We reviewed 60 patients with focal epilepsy with a suspected autoimmune etiology according to board-certified neurologists and epileptologists. To assess the involvement of the autoimmune etiology, we used the patients' sera or cerebrospinal fluid (CSF) samples to screen for antineuronal antibodies using rat brain immunohistochemistry. Positive samples were analyzed for known antineuronal antibodies. The algorithm applied to assess the data of all patients consisted of two steps: evaluation of clinical features suggesting autoimmune epilepsy and evaluation using laboratory and imaging findings (abnormal CSF findings, hypermetabolism on fluorodeoxyglucose-positron emission tomography, magnetic resonance imaging abnormalities, and bilateral epileptiform discharges on electroencephalography). Patients were screened during the first step and classified into five groups according to the number of abnormal laboratory findings. The significant cutoff point of the algorithm was assessed using a receiver-operating characteristic curve analysis. Results: Fourteen of the 60 patients (23.3%) were seropositive for antineuronal antibodies using rat brain immunohistochemistry. Ten patients had antibodies related to autoimmune epilepsy/encephalitis. The cutoff analysis of the number of abnormal laboratory and imaging findings showed that the best cutoff point was two abnormal findings, which yielded a sensitivity of 78.6%, a specificity of 76.1%, and an area under the curve of 0.81. Conclusion: The proposed algorithm could help predict the underlying autoimmune etiology of epilepsy before antineuronal antibody test results are available.
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