Prediction of Vagal Nerve Stimulation Efficacy in Drug-Resistant Epilepsy (PRECISE): Prospective Study for Pre-implantation Prediction/Study Design
Irena Dolezalova, Eva Koritakova, Lenka Souckova, Jan Chrastina, Jan Chladek, Radka Stepanova and Milan Brazdil
Front. Neurol., 21 March 2022
https://doi.org/10.3389/fneur.2022.839163
Have a look at the study design of the upcoming PRediction of vagal nerve stimulation EfficaCy In drug-reSistant Epilepsy (PRECISE) study, by Dolezalova et al. 2022, that was recently published in frontiers in Neurology. The study aims to evaluate the possibility of using preoperative standard electroencephalography (#EEG) recordings as predictors for vagus nerve stimulation (#VNS) efficacy in adults with drug resistant epilepsy (#DRE).
Study design:
This European #multicenter study will prospectively recruit adult patients undergoing VNS Therapy (planned number of patients: 140), who will be categorized as predicted responders (≥50% reduction in seizure frequency) or predicted non-responders (<50% reduction in seizure frequency) based on preoperative EEG recordings analyzed by a statistical classification model called Pre-X-Stim. The predicted response will then be compared with the actual response of VNS Therapy (determined at two different follow-up sessions, one- and two-years post VNS implantation, respectively) in order to determine the #accuracy, #sensitivity, and #specificity of the model. The main objective is to determine the #precision of the prediction model. Both patients and health care professionals will be unaware of the predicted result determined by the Pre-X-Stim.
Patient recruitment was initiated in January 2022.
To conclude, the current study was developed to verify the accuracy and validity of the Pre-X-Stim model for predicting VNS outcomes based on preoperative EEG recordings. The mathematical model has the potential to facilitate, and improve, the identification of patients with refractory epilepsy that would significantly benefit from VNS Therapy. Thus, optimizing patient selection.
