Early detection rate changes from a brain‐responsive neurostimulation system predict efficacy of newly added antiseizure drugs

Abstract Objective Brain‐responsive neurostimulation (RNS System, NeuroPace) is used to treat medically refractory focal epilepsy and also provides long‐term ambulatory neurophysiologic data. We sought to determine whether these data could predict the clinical response to antiseizure drugs (ASDs). Methods First, newly added medications were identified in RNS System patients followed at a single epilepsy center. Daily detection rates including “episode starts” (predominantly interictal activity) and “long episodes” (often electrographic seizures) were compared before and after ASD initiation. Efficacy was determined from documentation of clinical improvement and medication retention. Next, the analysis was repeated on an independent sample of patients from a multicenter long‐term treatment trial, using an efficacy measure of ≥50% reduction in diary‐recorded seizure frequency after 3 months. Results In the single center cohort, long episodes, but not episode starts, had a significantly greater reduction in the first week for clinically efficacious compared to inefficacious medications. In this cohort, having no long episodes in the first week was highly predictive of ASD efficacy. In the multicenter cohort, both long episodes and episode starts had a significantly greater reduction for effective medications starting in the first 1‐2 weeks. In this larger dataset, a ≥50% decrease in episode starts was 90% specific for efficacy with a positive predictive value (PPV) of 67%, and a ≥84% decrease in long episodes was 80% specific with a PPV of 48%. Conversely, a <25% decrease in long episodes (including any increase) or a <20% decrease in episode starts had a predictive value for inefficacy of >80%. Significance In RNS System patients with stable detection settings, when new ASDs are started, detection rates within the first 1‐2 weeks may provide an early, objective indication of efficacy. These data could be used to identify responses to medication trials early to allow more rapid medication adjustments than conventionally possible.