AUTHOR=Korit́áková Eva , Doležalová Irena , Chládek Jan , Jurková Tereza , Chrastina Jan , Plešinger Filip , Roman Robert , Pail Martin , Jurák Pavel , Shaw Daniel J. , Brázdil Milan 

TITLE=A Novel Statistical Model for Predicting the Efficacy of Vagal Nerve Stimulation in Patients With Epilepsy (Pre-X-Stim) Is Applicable to Different EEG Systems

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 15 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2021.635787

DOI=10.3389/fnins.2021.635787

ISSN=1662-453X

ABSTRACT=Background: Identifying patients with intractable epilepsy who would benefit from therapeutic chronic vagal nerve stimulation (VNS) pre-operatively remains a major clinical challenge. We have developed a statistical model for predicting VNS efficacy, using only routine pre-implantation EEG recorded with the TruScan EEG device [Brazdil et al. EEG reactivity predicts individual efficacy of vagal nerve stimulation in intractable epileptics. Front Neurol (2019) 10:392]. It remains to be seen, however, if this model can be applied in different clinical settings.
Objective: To validate our model using EEG data acquired with a different recording system.
Methods: We identified a validation cohort of eight patients implanted with VNS, whose pre-implantation EEG was recorded on the BrainScope device and who underwent the EEG recording according to the protocol. The classifier developed in our earlier work, named Pre-X-Stim, was then employed to classify these patients as predicted responders or non-responders based on the dynamics in EEG power spectra. Predicted and real-world outcomes were compared to establish the applicability of this classifier. In total, two validation experiments were performed using two different validation approaches (single classifier or classifier voting).
Results: The classifier achieved 75% accuracy, 67% sensitivity, and 100% specificity. Only two patients, both real-life responders, were classified incorrectly in both validation experiments. 
Conclusion: We have validated the Pre-X-Stim model on EEGs from a different recording system, which indicates its application under different technical conditions. Our approach, based on pre-operative EEG, is easily applied, financially undemanding, and presents great potential for real-world clinical use.