A prediction model integrating synchronization biomarkers and clinical features to identify responders to vagus nerve stimulation among pediatric patients with drug-resistant epilepsy.

ABSTRACT:

Aims

Vagus nerve stimulation (VNS) is a neuromodulation therapy for children with drug-resistant epilepsy (DRE). The efficacy of VNS is heterogeneous. A prediction model is needed to predict the efficacy before implantation.

Methods

We collected data from children with DRE who underwent VNS implantation and received regular programming for at least 1 year. Preoperative clinical information and scalp video electroencephalography (EEG) were available in 88 children. Synchronization features, including phase lag index (PLI), weighted phase lag index (wPLI), and phase-locking value (PLV), were compared between responders and non-responders. We further adapted a support vector machine (SVM) classifier selected from 25 clinical and 18 synchronization features to build a prediction

SUBMITTER: Ma J 

PROVIDER: S-EPMC9532924 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature