Literature DB >> 33461042

Prevalence of benign epileptiform variants from an EEG laboratory in India and frequency of their misinterpretation.

Chaturbhuj Rathore1, Sanjay Prakash2, Kaushik Rana2, Prayag Makwana2.   

Abstract

OBJECTIVE: To prospectively study the prevalence of benign epileptiform variants (BEVs) and their impact on epilepsy misdiagnosis.
METHODS: Consecutive patients, older than one year, who underwent EEG from January 2016 to December 2019 were prospectively studied for the presence of BEVs. We used descriptions of Klass and Westmoreland (1985) to categorize the BEVs. We reviewed old EEG reports and records in patients with BEV to determine whether they were interpreted as abnormal.
RESULTS: Of the 1862 subjects included, 1474 (79 %) patients had epilepsy while 388 (21 %) had other diagnoses. The mean age of the subjects was 23.1 ± 15.3 years and 1111 (60 %) were males. BEVs were noted in 223 (12 %) subjects undergoing EEG. The most common BEVs were wicket waves (n = 127, 6.8 %) and small sharp spikes (n = 69, 3.7 %) while 6 Hz spike-wave discharges (0.9 %), 14 and 6 Hz positive spikes (0.6 %), rhythmic mid-temporal theta burst of drowsiness (0.4 %) and subclinical rhythmic epileptiform discharges in adults (0.2 %) were less common. Patients with BEVs were older and were more likely to have normal EEG (68.2 % vs. 55.8 %; p < 0.001). BEVs were not mentioned in any of the 282 previous EEG reports. BEVS were considered to be over-interpreted as epileptiform abnormalities in 31 of 101 (30 %) records available for review.
CONCLUSION: BEVs are present in 12 % of subjects undergoing EEG. BEVS are largely unrecognized and are misdiagnosed as epileptiform discharges in one third of the patients by the general neurologists.
Copyright © 2021 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  14 and 6 Hz positive spikes; Benign epileptiform variants; Phantom spikes; RMTD; SREDA; Small sharp spikes; Wicket waves

Mesh:

Year:  2021        PMID: 33461042     DOI: 10.1016/j.eplepsyres.2020.106539

Source DB:  PubMed          Journal:  Epilepsy Res        ISSN: 0920-1211            Impact factor:   3.045


  3 in total

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Review 3.  Moving the field forward: detection of epileptiform abnormalities on scalp electroencephalography using deep learning-clinical application perspectives.

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  3 in total

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