The spectrum of epilepsy with eyelid myoclonia: delineation of disease subtypes from a large multicenter study.

Abstract

Epilepsy with eyelid myoclonia (EEM) has been associated with marked clinical heterogeneity. Early epilepsy onset has been recently linked to lower chances of achieving sustained remission and to a less favorable neuropsychiatric outcome. However, much work is still needed to better delineate this epilepsy syndrome. In this multicenter retrospective cohort study, we included 267 EEM patients from 9 countries. Data about electroclinical and demographic features, intellectual functioning, migraine with or without aura, family history of epilepsy and epilepsy syndromes in relatives were collected in each patient. The impact of age at epilepsy onset (AEO) on EEM clinical features was investigated, along with the distinctive clinical characteristics of patients showing sporadic myoclonia over body regions other than eyelids (body-MYO). Kernel density estimation revealed a trimodal distribution of AEO and Fisher-Jenks optimization disclosed three EEM subgroups: early-onset (EO-EEM), intermediate-onset (IO-EEM) and late-onset subgroup (LO-EEM). EO-EEM was associated with the highest rate of intellectual disability, antiseizure medication refractoriness and psychiatric comorbidities and with the lowest rate of family history of epilepsy. LO-EEM was associated with the highest proportion of body-MYO and generalized tonic-clonic seizures (GTCS), whereas IO-EEM had the lowest observed rate of additional findings. A family history of EEM was significantly more frequent in IO-EEM and LO-EEM compared with EO-EEM. In the subset of patients with body-MYO (58/267), we observed a significantly higher rate of migraine and GTCS but no relevant differences in other electroclinical features and seizure outcome. Based on AEO, we identified consistent EEM subtypes characterized by distinct electroclinical and familial features. Our observations shed new light on the spectrum of clinical features of this generalized epilepsy syndrome and may help clinicians towards a more accurate classification and prognostic profiling of EEM patients.