Lhatoo, Samden

Abstract Aim To assess the long‐term efficacy of vagus nerve stimulation (VNS) in Dravet syndrome and identify key factors influencing treatment response. Method We conducted a two‐center, retrospective cohort study of 15 patients (six females, nine males; median age at seizure onset 4 months [range 2–12 months]) with Dravet syndrome who underwent VNS implantation. Seizure frequency was evaluated over 12 months and up to 10 years after implantation. Linear mixed‐effects regression was used to analyze the impact of antiseizure medications (ASMs) and VNS parameters on seizure outcomes. Results A 50% or greater reduction in seizure frequency was observed in 93.3% (14 of 15) of patients at 12 months and 86.7% (13 of 15) of patients across the 10‐year follow‐up. This seizure reduction was independent of ASM use; the VNS duty cycle was a significant predictor of seizure reduction. Two individuals (13.3%) experienced minor adverse events. Interpretation VNS provides durable seizure reduction in Dravet syndrome, independent of ASM adjustments. These findings highlight the importance of early VNS initiation and parameter optimization in managing genetically defined epilepsy syndromes.

AbstractEpilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data.