A Novel Relationship Between Interictal Epileptiform Discharge Burden and Gross Motor Developmental Delay in SCN2A Developmental and Epileptic Encephalopathy
Abstract number :
3.091
Submission category :
2. Translational Research / 2C. Biomarkers
Year :
2023
Submission ID :
663
Source :
www.aesnet.org
Presentation date :
12/4/2023 12:00:00 AM
Published date :
Authors :
Presenting Author: Patricia Fogerson, PhD – Beacon Biosignals
Melina Tsitsiklis, PhD – Beacon Biosignals; Elise Brimble, MS – Invitae Corp.; Alex Arslan, MS – Beacon Biosignals; Jayne Nerrie, MA – Beacon Biosignals; Kim Laberinto, BSc – Beacon Biosignals; Jay Pathmanathan, MD, PhD – Beacon Biosignals; Brandon Westover, MD, PhD – Beth Israel Deaconess Medical Center; Beacon Biosignals; Nasha Fitter, MBA – Invitae Corp.; Jacob Donoghue, MD, PhD – Beacon Biosignals
Rationale: Interictal epileptiform discharges (IEDs) are indicative of epilepsy and clearly associated with seizure risk, but their potential relationship to developmental delay is poorly understood. The use of IEDs as a biomarker to predict delayed development could be a powerful clinical trial endpoint for targeted therapies for SCN2A-DEE. Here, we evaluate the utility of machine learning data segmentation to identify IEDs as a predictive biomarker of SCN2A-DEE gross motor developmental outcome.
Methods: EEG recordings and corresponding medical record data were collected for patients with SCN2A variants by the Invitae Ciitizen® platform, resulting in a longitudinal, retrospective dataset of 592 EEGs and accompanying clinical data. A total of 279 EEG recordings from 21 patients al status by relative date and analyzed via the Beacon Platform. A machine learning model trained and evaluated on EEG segments labeled by eight epileptologists was used to identify one second windows containing IEDs, and IED burden, or percent time containing IEDs, was computed using a 20-second rolling window. SCN2A phenotype was identified for each patient as early [<
Translational Research