Computational Decision Support in Epilepsy Using Retrospective EEG

NCT05384782 | Completed

• 2 other identifiers: Version 10260729
• Study Type: observational
• Target: 825
• Locations: 1 country
• Sites: 1

Brief Summary

The primary aim is to validate a set of computational biomarkers as potential decision support in epilepsy on a large cohort of study participants that were diagnosed with epilepsy and controls that ended up with another diagnosis (such as syncope or non-epileptic seizures). The goal is to examine if the methodology works robustly on this large cohort, and can theoretically contribute to the reduction of misdiagnosis rates. The secondary aim is to examine whether the computational biomarkers could contribute to reducing the waiting time and the number of clinical appointments needed before a final diagnosis is made.

Conditions

Epilepsy

Keywords

Epilepsy

Outcome Measures

Primary Outcomes (1)

• To validate a set of computational biomarkers as potential decision support in epilepsy on a large cohort of study participants that were diagnosed with epilepsy and controls that ended up with another diagnosis

  To each EEG recording, we apply an algorithm that automatically detects relevant segments to our analysis (free of artefacts). By combining the individually derived network structure with the mathematical model, we simulate a computer-generated EEG, which serves as a proxy for the original segment derived from the study participant. We then examine this computer-generated EEG by calculating two biomarkers: 1. A global marker that quantifies how easy it is for the entire network to make the transition to seizure activity in the model 2. A local marker that quantifies whether there are particular regions in the network that are particular prone to generating or participating in seizure activity in the model.

  31/12/2022

Secondary Outcomes (1)

• To examine whether the computational biomarkers could contribute to reducing the waiting time and the number of clinical appointments needed before a final diagnosis is made.

  31/12/2022

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Data will be collected across multiple sites within the NHS. At each site the local direct care team within the Neurology clinics will be performing the participant identification

You may qualify if:

• Subject was suspected of having had a seizure or epilepsy (fits, faints or funny turns), and as part of the diagnostic process one or more EEGs was recorded The subject ended up with a confirmed diagnosis of epilepsy or of the differential diagnosis such as syncope, or psychogenic seizures (diagnosis must have been at least 1 year ago, and not changed since)
• For each subject identified we would like to have all the available EEG files within the centre, with the following metadata:
• Primary meta-data (crucial):
• Age at the subject at time of each available EEG Treatment status at the time of each available EEG (including drug-load) Gender of the individual Ethnicity of the individual Confirmed diagnosis: details on the exact diagnosis made (syndrome and or condition)
• Secondary meta-data (optional):
• Aim of each available EEG at the time Information on whether any other conditions are present such as Alzheimer's disease, schizophrenia, Intellectual Disability If available: information on when the diagnosis was made If available: interpretation of each available EEG
• Specifics for the EEG recordings:
• Montage (10-20 preferred) Number of channels (minimum 19 channels) Referencing method (common average preferred) Format of the file (EDF preferred) Consistent channel labels for all EEGs provided from each centre Information concerning the time of day during the recording Information on the sampling frequency Faulty channels (not more than 2 preferred, all should be indicated though) Pre-processing details (information as to whether any filters were used, for example)

Sponsors & Collaborators

• Cornwall Partnership NHS Foundation Trust: lead
• Neuronostics Ltd: collaborator

Study Sites (1)

Cornwall Partnership NHS Foundation Trust

Bodmin, Cornwall, PL31 2QN, United Kingdom

Location

MeSH Terms

Conditions

Epilepsy

Condition Hierarchy (Ancestors)

Brain Diseases; Central Nervous System Diseases; Nervous System Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: NETWORK
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 4, 2022
• First Posted: May 20, 2022
• Study Start: December 1, 2019
• Primary Completion: December 31, 2021
• Study Completion: March 31, 2022
• Last Updated: May 20, 2022

Record last verified: 2022-05

Locations

GB (1)