Development and validation of a predictive model of drug-resistant genetic generalized epilepsy

Abstract

ObjectiveTo develop and validate a clinical prediction model for antiepileptic drug (AED)–resistant genetic generalized epilepsy (GGE).MethodWe performed a case-control study of patients with and without drug-resistant GGE, nested within ongoing longitudinal observational studies of AED response at 2 tertiary epilepsy centers. Using a validation dataset, we tested the predictive performance of 3 candidate models, developed from a training dataset. We then tested the candidate models' predictive ability on an external testing dataset.ResultsOf 5,189 patients in the ongoing longitudinal study, 121 met criteria for AED-resistant GGE and 468 met criteria for AED-responsive GGE. There were 66 patients with GGE in the external dataset, of whom 17 were cases. Catamenial epilepsy, history of a psychiatric condition, and seizure types were strongly related with drug-resistant GGE case status. Compared to women without catamenial epilepsy, women with catamenial epilepsy had about a fourfold increased risk for AED resistance. The calibration of 3 models, assessing the agreement between observed outcomes and predictions, was adequate. Discriminative ability, as measured with area under the receiver operating characteristic curve (AUC), ranged from 0.58 to 0.65.ConclusionCatamenial epilepsy, history of a psychiatric condition, and the seizure type combination of generalized tonic clonic, myoclonic, and absence seizures are negative prognostic factors of drug-resistant GGE. The AUC of 0.6 is not consistent with truly effective separation of the groups, suggesting other unmeasured variables may need to be considered in future studies to improve predictability.