Clinical and EEG factors associated with antiseizure medication resistance in idiopathic generalized epilepsy
Brad Kamitaki1, Haiqun Lin2, Gary Heiman3, Hyunmi Choi4
1Department of Neurology, Rutgers-Robert Wood Johnson Medical School, 2School of Nursing, 3Department of Genetics and the Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, 4Department of Neurology, Columbia University Medical Center
Objective:

We sought to determine which combination of clinical and EEG characteristics differentiate between an antiseizure medication (ASM)-resistant versus ASM-responsive outcome for patients with idiopathic generalized epilepsy (IGE).

Background:

Several factors predict ASM-resistance for patients with IGE. Catamenial epilepsy, i.e., a change in seizure frequency with the menstrual cycle, was recently found to co-occur with ASM-resistant IGE.

Design/Methods:

This was a case-control study of ASM-resistant cases and ASM-responsive controls with IGE treated at five epilepsy centers in the United States and Australia between 2002-2018. We recorded clinical characteristics and findings from the first available EEG study for each patient. We then compared characteristics of cases versus controls using multivariable logistic regression to develop a predictive model of ASM-resistant IGE.

Results:

We identified 118 ASM-resistant cases and 114 ASM-responsive controls with IGE. First, we confirmed our recent finding that catamenial epilepsy is associated with ASM-resistant IGE (OR 3.53, 95% CI 1.32-10.41). Other independent factors seen with ASM-resistance include certain seizure type combinations (absence, myoclonic, and generalized tonic-clonic seizures [OR 7.06, 95% CI 2.55-20.96]; absence and generalized tonic-clonic seizures [OR 4.45, 95% CI 1.84-11.34]), as well as EEG markers of increased generalized spike-wave discharges (GSW) in sleep (OR 3.43, 95% CI 1.12-11.36 for frequent and OR 7.21, 95% CI 1.50-54.07 for abundant discharges in sleep) and the presence of generalized polyspike trains (GPT; OR 5.49, 95% CI 1.27-38.69). The discriminative ability of our final multivariable model, as measured by area under the receiving operating characteristic curve, was 0.80.

Conclusions:

Multiple clinical and EEG characteristics independently predict ASM-resistance in IGE. To improve understanding of a patient’s prognosis, clinicians could consider asking about specific seizure type combinations and track whether they experience catamenial epilepsy. Obtaining prolonged EEG studies to record the burden of GSW in sleep and assessing for the presence of GPT may provide additional predictive value.