Development of a Point of Care System for Automated Coma Prognosis

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

Official Title
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Development of a Point of Care System for Automated Coma Prognosis - A Prospective Cohort Study

Conditions
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Coma, Disorder of Consciousness, Minimally Conscious State, Vegetative State, Neuropathology

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Patients (≥ 18 years of age) primarily admitted to the Intensive Care Units, Neurological Step Down Unit, or Coronary Care Unit at Hamilton General Hospital who are in coma with Glasgow Coma Scale (GCS) score of 3-8, or; Patients (≥ 18 years of age) who have other disorders of consciousness, primarily Minimally Conscious State (MCS) or Unresponsive Wakefulness Syndrome (UWS; also known as vegetative state). Exclusion Criteria: Severe liver failure (i.e., Child-Pugh Class C) Severe renal failure (i.e., Urea ≥ 40) Previous open-head injury Known primary and secondary central nervous system malignancy Known hearing impairment Previous intracranial pathology requiring neurosurgical interventions in the past 72 hours Anyone who is deemed medically unsuitable for this study by the attending intensivists Healthy Controls: Inclusion: ≥ 18 years of age no visual, language, learning, or hearing problems no history of neurological or psychiatric disorder not currently taking any medications that act on the central nervous system, such as antidepressants, anxiolytics, or anti-epileptics Exclusion: (During the COVID-19 pandemic only) ≥ 60 years of age have a weakened immune system have one or more of the COVID-19 high risk medical conditions, according to the government of Canada website: https://www.canada.ca/en/public-health/services/publications/diseases-conditions/people-high-risk-for-severe-illness-covid-19.html.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| DOC patients<br>Patients in coma (GCS score of 3-8) or with other disorder of consciousness, primarily Minimally Conscious State (MCS) or Unresponsive Wakefulness Syndrome (UWS; also known as vegetative state) | |
| Healthy Control<br>Matched healthy controls without current neurological diagnoses | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Change in multiple electrophysiological measures across specified time points during coma | Event-related potentials (ERP) and resting state periods will be assessed at the specified intervals as a difference between successive timepoints. The ERP measures will include amplitude and latency values of N1, P2, MMN, P3a, P3b, and N400 to assess different levels of conscious processing and presence of signs of a conscious state predictive of subsequent emergence. Also, resting EEG measures will be obtained at regular intervals. EEG/ERP data will be recorded for up to 24 consecutive hours at a maximum of 5 timepoints spanning 30 days from the date of recruitment to track the participants' progression. The date of the initial assessment will be denoted as Day 0, and the subsequent assessments will take place ideally on Day 3, Day 10, Day 20 and Day 30, unless there is a ≥ 2 point of change in the patient's GCS score. Change in all specified measures will be assessed across the up to 24-hour recordings taken at 5 different timepoints. | up to 30 days from date of recruitment | 
| Change in multiple electrophysiological measures across specified time points during MCS or UWS | Event-related potentials (ERP) and resting state periods will be assessed at the specified intervals as a difference between successive timepoints. The ERP measures will include amplitude and latency values of N1, P2, MMN, P3a, P3b, and N400 to assess different levels of conscious processing and presence of signs of a conscious state predictive of subsequent emergence. Also, resting EEG measures will be obtained at regular intervals. EEG/ERP data will be recorded for an initial period of up to 24 consecutive hours, followed by up to 2-hour long recordings that may be conducted approximately once a week until the patient either regains full consciousness, is no longer within the Hamilton Health Sciences system, or until 6 months from the date of their enrollment into the study, whichever occurs first. Change in all specified measures will be assessed across the recordings taken at each timepoint. | up to 6 months from date of recruitment | 
| Correlation between behavioral and electrophysiological measures after coma/DOC emergence | Patient emergence will be monitored using the Glasgow Outcome Scale (GOS). In the case of patient emergence, the full electrophysiological test procedures are recorded to correlate with traditional behavioral measures. The electrophysiological measures obtained at this timepoint (emergence) will be compared to the same measures obtained at the different time points during coma/DOC (Outcome 1/2) to detect both clinically relevant change and possible prognostic markers that may have been obtained at an earlier test point. | Within a 30-day time period post recruitment | 
| Sensitivity and specificity of prognostic capabilities of electrophysiological measures | Analyses will compare the electrophysiological measures as outcome predictors to traditional behaviorally-based tools. | Within a 30-day time period post recruitment | 
| Feasibility of procedure | The team will also evaluate whether the repeated EEG sessions, lasting up to 24 hours, during the coma/DOC duration is a feasible approach to predict the emergence and outcome from coma. | up to 6 months from date of recruitment | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Correlation between individual patient factors, EEG results, and outcome for coma | The study also collects demographic, medical history, injury information, and other physiological markers from the patient's health record and concurrent physiological assessment during the study period. Analyses will assess correlations between these factors and coma outcome and EEG findings. | up to 30 days from date of recruitment | 
| Correlations between individual patient factors, EEG results, and outcome for DOC | The study also collects demographic, medical history, injury information, and other physiological markers from the patient's health record and concurrent physiological assessment during the study period. Analyses will assess correlations between these factors and DOC outcome and EEG findings. | up to 6 months from date of recruitment | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Coma, Machine Learning, Prognosis, Electrophysiology, Event-related potentials, Electroencephalography, Mismatch Negativity, Auditory Brainstem Response, Disorders of Consciousness, EEG, ERP