Abstracts

Use of Data Mining and a Generalized Linear Mixed Model to Identify Optimal Vagus Nerve Stimulation and Titration for Patients with Drug-Resistant Epilepsy

Abstract number : 3.155
Submission category : 3. Neurophysiology / 3E. Brain Stimulation
Year : 2021
Submission ID : 1826472
Source : www.aesnet.org
Presentation date : 12/6/2021 12:00:00 PM
Published date : Nov 22, 2021, 06:54 AM

Authors :
Ryan Verner, PhD - LivaNova USA Inc.; Lennart Kann - Biostatistician, Clinical and Medical Affairs, LivaNova PLC (or a subsidiary); Firas Fahoum, MD MSc - Neurological Institute - Tel Aviv Sourasky Medical Center; Michal Tzadok, MD - Pediatric Neurology - Edmond and Lily Safra Children's Hospital, Sheba Medical Center; Massimiliano Boffini, PhD - Senior Medical Science Liaison Manager, Clinical and Medical Affairs, LivaNova PLC (or a subsidiary); Charles Gordon - Director, Biostatistics, Clinical and Medical Affairs, LivaNova PLC (or a subsidiary); Riëm El Tahry, MD - Neurology - Cliniques Universitaires Saint-Luc

Rationale: Experience with vagus nerve stimulation (VNS) as an electroceutical has evolved over time for drug resistant epilepsy (DRE). Titration of VNS intensity comprises amplitude, frequency, pulse width, and duty cycle for DRE and has been fundamentally empiric. However, successive clinical studies and clinical experience over the past 25 years have amassed large amounts of data that can be utilized for modeling and potentially refining stimulation parameters. We have embarked on large-scale mining of this data and utilization of multivariate models to identify dosing and titration strategies.

Methods: A de-identified patient-level database has been compiled for 8,879 patients implanted with a VNS Therapy System® between 1990 and 2018. A subset of these subjects (n=1023) have detailed programming history information and all subject level demographics were selected for initial analysis. A generalized linear mixed model (GLMM) was developed to assess the programming settings for output current, pulse width, signal frequency, and duty cycle that were associated with patient response, defined as the proportion of patients having a 50% reduction from baseline in seizure frequency. Time to the VNS output current associated with a patient response and the time to patient response were evaluated.

Results: The GLMM associated an output current of 1.73mA (closest programmable setting 1.75mA) with the highest probability of patient response. Duration of epilepsy and age at implant were statistically significant, and while higher age has a positive effect in this model, earlier time to treatment regardless of age yielded a higher likelihood of response. A limited amount of other signal frequencies, pulse widths, and duty cycles were available for modeling and did not appear to have a significant effect on response probability. A small proportion of patients were titrated to 1.75mA within 3 months of VNS System implantation. Most patients were titrated over 6 or more months to reach a similar level. A time-to-response analysis associated titration within 3 months with a significantly increased likelihood of response and faster time-to-response than titration that occurred over 6 or more months.

Conclusions: Using GLMM, VNS output current near 1.75 mA is associated with the highest patient response to VNS for DRE. Completing titration to this VNS intensity within 3 months was associated with a significantly faster time-to-response than titration completed over 6 or more months. These findings should be taken as general considerations as they are based on data that was not prospectively collected in all cases. A risk-benefit analysis that incorporates side effects during VNS titration is future work that will also be informative for physicians utilizing VNS to treat DRE.

Funding: Please list any funding that was received in support of this abstract.: This work was funded by LivaNova USA Inc.

Neurophysiology