| Autor: |
Mitsuhiro Sakamoto, Riki Matsumoto, Akihiro Shimotake, Jumpei Togawa, Hirofumi Takeyama, Katsuya Kobayashi, Frank Leypoldt, Klaus-Peter Wandinger, Takayuki Kondo, Ryosuke Takahashi, Akio Ikeda |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Frontiers in Neurology, Vol 13 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1664-2295 |
| DOI: |
10.3389/fneur.2022.902157 |
| Popis: |
PurposeThis study aims to propose a diagnostic algorithm for autoimmune epilepsy in a retrospective cohort and investigate its clinical utility.MethodsWe 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.ResultsFourteen 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.ConclusionThe proposed algorithm could help predict the underlying autoimmune etiology of epilepsy before antineuronal antibody test results are available. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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