Development of a Point of Care System for Automated Coma Prognosis
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- STATUS
- Recruiting
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- End date
- Dec 3, 2023
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- participants needed
- 90
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- sponsor
- McMaster University
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.
Description
The Problem: Coma is a state of unconsciousness with a variety of causes. Traditional tests for coma outcome prediction are mainly based on a set of clinical observations (e.g., pupillary constriction). Recently however, event-related potentials (ERPs; which are transient electroencephalogram [EEG] responses to auditory, visual, or tactile stimuli) have been introduced as useful predictors of a positive coma outcome (i.e., emergence). However, such tests require a skilled neurophysiologist, and such people are in short supply. Also, none of the current approaches has sufficient positive and negative predictive accuracies to provide definitive prognoses in the clinical setting.
Objective: The investigators will apply innovative machine learning methods to analyze patient EEGs (50 patients and 40 healthy controls) to develop a simple, objective, replicable, and inexpensive point of care system which can significantly improve the accuracy of coma prognosis relative to current methods. The physical requirements of the proposed system consist only of an EEG system (inexpensive in terms of medical equipment) and a conventional laptop computer.
Methodology: The investigators intend to extend the team's newest algorithms and develop machine learning tools for automatic analysis and detection of ERP components. Preliminary results by the team in this respect have been very promising. The most salient features (i.e., biomarkers) extracted from the ERP will be identified and combined in an optimal fashion to give an accurate indicator of prognosis. Features will be extracted from resting state brain networks and from network trajectories associated with the processing of ERP signals.
Significance: The proposed work will enable critical care physicians to assess coma prognosis with speed and accuracy. Thus, families and their health care team will be provided the most accurate information possible to guide discussions of goals of care and life-sustaining therapies in the context of dealing with the consequences of devastating neurological injury.
Details
| Condition | Coma, Traumatic coma, Persistent Vegetative State, Neuropathology, Disorder of Consciousness, minimally conscious state, comatose, vegetative state |
|---|---|
| Clinical Study Identifier | NCT03826407 |
| Sponsor | McMaster University |
| Last Modified on | 3 February 2021 |
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