Journal
EPILEPSIA
Volume 61, Issue 11, Pages 2452-2460Publisher
WILEY
DOI: 10.1111/epi.16708
Keywords
generalized paroxysmal fast activity; genetic generalized epilepsy; idiopathic generalized epilepsy; prognostic factors; treatment resistance
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Objective To assess prognostic patterns and investigate clinical and electroencephalography (EEG) variables associated with persistent treatment resistance in a population of genetic generalized epilepsy (GGE) patients with a long-term follow-up. Methods Data from GGE patients followed from 1975 to 2019 were reviewed retrospectively. Subjects with a follow-up >10 years, starting from epilepsy diagnosis, were included. Persistent treatment resistance was defined as the absence of any period of remission >= 1 year despite treatment with two appropriate and adequate antiepileptic drugs (AEDs). Results One hundred ninety-nine patients were included. The median age was 39.5 years (interquartile range [IQR] 30-49) and the median follow-up was 27 years (IQR 18-35). The most common syndrome was juvenile myoclonic epilepsy (JME), diagnosed in 44.2% of patients. During follow-up, 163 subjects (81.9%) experienced 3-year remission from any seizure type, whereas 5- and 10-year remission occurred in 141 (70.8%) and 92 (46.2%) cases, respectively. The most common prognostic pattern was a relapsing-remitting course, observed in 80 patients (40.2%), whereas 29 (14.6%) displayed persistent treatment resistance. According to multivariable logistic regression analysis, febrile seizures (FS), specific EEG patterns (namely generalized paroxysmal fast activity, GPFA) and valproate (VPA) resistance were the only variables significantly associated with persistent treatment resistance. JME was the only epilepsy syndrome statistically associated with persistent treatment resistance in univariable logistic regression analysis. Significance Persistent treatment resistance was observed in almost 15% of GGE patients followed in a tertiary epilepsy center. A worse outcome was associated with specific clinical variables (JME, FS) and EEG patterns (GPFA).
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