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@article{1783538,
author = {Koriťáková, Eva and Doležalová, Irena and Chládek, Jan and Jurková, Tereza and Chrastina, Jan and Plesinger, Filip and Roman, Robert and Pail, Martin and Jurak, Pavel and Shaw, Daniel Joel and Brázdil, Milan},
article_location = {Lausanne},
article_number = {May},
doi = {http://dx.doi.org/10.3389/fnins.2021.635787},
keywords = {vagal nerve stimulation; neurostimulation; epilepsy; efficacy prediction; EEG reactivity; epilepsy treatment},
language = {eng},
issn = {1662-453X},
journal = {Frontiers in Neuroscience},
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},
url = {https://www.frontiersin.org/articles/10.3389/fnins.2021.635787/full},
volume = {15},
year = {2021}
}
TY - JOUR
ID - 1783538
AU - Koriťáková, Eva - Doležalová, Irena - Chládek, Jan - Jurková, Tereza - Chrastina, Jan - Plesinger, Filip - Roman, Robert - Pail, Martin - Jurak, Pavel - Shaw, Daniel Joel - Brázdil, Milan
PY - 2021
TI - 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
JF - Frontiers in Neuroscience
VL - 15
IS - May
SP - 1-6
EP - 1-6
PB - Frontiers Media S.A.
SN - 1662453X
KW - vagal nerve stimulation
KW - neurostimulation
KW - epilepsy
KW - efficacy prediction
KW - EEG reactivity
KW - epilepsy treatment
UR - https://www.frontiersin.org/articles/10.3389/fnins.2021.635787/full
N2 - 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.
ER -
KORIŤÁKOVÁ, Eva, Irena DOLEŽALOVÁ, Jan CHLÁDEK, Tereza JURKOVÁ, Jan CHRASTINA, Filip PLESINGER, Robert ROMAN, Martin PAIL, Pavel JURAK, Daniel Joel SHAW and Milan BRÁZDIL. 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. \textit{Frontiers in Neuroscience}. Lausanne: Frontiers Media S.A., 2021, vol.~15, May, p.~1-6. ISSN~1662-453X. Available from: https://dx.doi.org/10.3389/fnins.2021.635787.
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