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

0543276 - ÚPT 2022 RIV CH eng J - Journal Article
Koriťáková, E. - Doležalová, I. - Chládek, Jan - Jurková, T. - Chrastina, J. - Plešinger, Filip - Roman, R. - Pail, M. - Jurák, Pavel - Shaw, D. J. - Brázdil, M.
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.
Frontiers in Neuroscience. Roč. 15, 11 May (2021), č. článku 635787. E-ISSN 1662-453X
R&D Projects: GA MZd(CZ) NV19-04-00343
Institutional support: RVO:68081731
Keywords : vagal nerve stimulation * neurostimulation * epilepsy * efficacy prediction * EEG reactivity * epilepsy treatment
OECD category: Neurosciences (including psychophysiology
Impact factor: 5.152, year: 2021
Method of publishing: Open access
https://www.frontiersin.org/articles/10.3389/fnins.2021.635787/full

Background: Identifying patients with intractable epilepsy who would benefit from therapeutic chronic vagal nerve stimulation (VNS) preoperatively remains a major clinical challenge. We have developed a statistical model for predicting VNS efficacy using only routine preimplantation electroencephalogram (EEG) recorded with the TruScan EEG device (Brazdil et al., 2019). 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 preimplantation 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 preoperative EEG, is easily applied and financially undemanding and presents great potential for real-world clinical use.
Permanent Link: http://hdl.handle.net/11104/0320540