New prognostic models could predict the time to treatment failure and the time to 12 months of remission for patients with epilepsy, according to research published in the April issue of Lancet Neurology.
Treatment history, age, total number of seizures before randomization, and treatment were significant variables in the model for treatment failure because of inadequate seizure control. Sex, treatment history, age, seizure type, and treatment were predictors of treatment failure because of unacceptable adverse events. Factors significantly associated with time to 12 months of remission were sex, treatment history, age, time from first seizure to randomization, neurologic insult, total number of seizures before randomization, CT or MRI scan results, and treatment.
Laura Bonnett, a biostatistics research assistant at the University of Liverpool in the United Kingdom, and her colleagues used regression multivariable modeling to perform a post hoc analysis of data from patients with epilepsy in Arm A of the Standard and New Antiepileptic Drug (SANAD) trial. In this prospective trial, a heterogeneous group of 1,721 patients for whom physicians considered carbamazepine to be the first-line treatment were randomly assigned treatment with carbamazepine, gabapentin, lamotrigine, oxcarbazepine, or topiramate.
Approximately 89% of patients had focal epilepsy. A total of 1,608 patients were included in the analysis of time to treatment failure, and 1,588 were included in the analysis of time to 12 months of remission. All patients had had at least two clinically definite unprovoked epileptic seizures in the year before enrollment.
“The SANAD study is the largest randomized controlled trial in epilepsy and includes data for long-term treatment outcomes, which is essential to inform the management of this chronic condition,” observed Ms. Bonnett. The researchers’ models could “inform patient counseling and treatment decisions,” she added. “If validated, our results might improve predictions of outcome for patients and enable identification of patients more likely to have a poor treatment outcome who might need to be followed up more regularly. The models might also help identify patients “who might be eligible to participate in trials of new treatments,” concluded Ms. Bonnett.