Development of a Point of Care System for Automated Coma Prognosis
NCT03826407 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 33
Last updated 2023-02-06
Summary
Electroencephalogram/event-related potentials (EEG/ERP) data will be collected from 50 participants in coma or other disorder of consciousness (DOC; i.e., Unresponsive Wakefulness Syndrome \[UWS\] or Minimally Conscious State \[MCS\]), clinically diagnosed using the Glasgow Coma Scale (GCS). For coma patients, EEG recordings will be conducted for up to 24 consecutive hours at a maximum of 5 timepoints, spanning 30 days from the date of recruitment, to track participants' clinical state. For DOC patients, there will be an initial EEG recording up to 24 hours, with possible subsequent weekly recordings up to 2 hours. An additional dataset from 40 healthy controls will be collected, each spanning up to a 12-hour recording period in order to formulate a baseline. Collected data are to form the basis for automatic analysis and detection of ERP components in DOC, using a machine learning paradigm. Salient features (i.e., biomarkers) extracted from the ERPs and resting-state EEG will be identified and combined in an optimal fashion to give an accurate indicator of prognosis.
Conditions
- Coma
- Disorder of Consciousness
- Minimally Conscious State
- Vegetative State
- Neuropathology
Sponsors & Collaborators
-
Canadian Institutes of Health Research (CIHR)
collaborator OTHER_GOV -
Natural Sciences and Engineering Research Council, Canada
collaborator OTHER -
Hamilton Health Sciences Corporation
collaborator OTHER -
Brain Vision Solutions Inc.
collaborator UNKNOWN - collaborator OTHER
- lead OTHER
Principal Investigators
-
John F Connolly, PhD · McMaster University
-
Alison Fox-Robichaud, MD · Hamilton Health Sciences - Hamilton General site
Eligibility
- Min Age
- 18 Years
- Sex
- ALL
- Healthy Volunteers
- Yes
Timeline & Regulatory
- Start
- 2019-10-01
- Primary Completion
- 2022-08-12
- Completion
- 2023-12-31
Countries
- Canada
Study Locations
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