Clinical trial NCT03826407: Development of a Point of Care System for Automated Coma Prognosis
Official title: Development of a Point of Care System for Automated Coma Prognosis - A Prospective Cohort Study
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Coma
Disorder of Consciousness
Minimally Conscious State
Vegetative State
Neuropathology
Keywords: Coma
Machine Learning
Prognosis
Electrophysiology
Event-related potentials
Electroencephalography
Mismatch Negativity
Auditory Brainstem Response
Disorders of Consciousness
EEG
ERP
Lead sponsor: McMaster University
Sponsor class: OTHER
Collaborators: name: Canadian Institutes of Health Research (CIHR)
name: Natural Sciences and Engineering Research Council, Canada
name: Hamilton Health Sciences Corporation
name: Brain Vision Solutions Inc.
name: McGill University
First posted: 2019-02-01
Enrollment: 33
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The Coma/DOC Group will include 50 patients from the Intensive Care Units, Neurological Step Down Unit, or Coronary Care Unit at Hamilton General Hospital (Ontario, Canada) who are in coma (GCS score =3-8), or who have other disorders of consciousness (MCS or UWS).

The Control Group of 40 matched healthy controls will be recruited primarily from the Hamilton community (Ontario, Canada).

Brief 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.

Detailed 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.

Eligibility criteria: 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.

Primary outcomes:
measure: Change in multiple electrophysiological measures across specified time points during coma
timeFrame: up to 30 days from date of recruitment
measure: Change in multiple electrophysiological measures across specified time points during MCS or UWS
timeFrame: up to 6 months from date of recruitment
measure: Correlation between behavioral and electrophysiological measures after coma/DOC emergence
timeFrame: Within a 30-day time period post recruitment
measure: Sensitivity and specificity of prognostic capabilities of electrophysiological measures
timeFrame: Within a 30-day time period post recruitment
measure: Feasibility of procedure
timeFrame: up to 6 months from date of recruitment
Secondary outcomes:
measure: Correlation between individual patient factors, EEG results, and outcome for coma
timeFrame: up to 30 days from date of recruitment
measure: Correlations between individual patient factors, EEG results, and outcome for DOC
timeFrame: up to 6 months from date of recruitment
Other outcomes:
Locations:
city: Hamilton
country: Canada
facility: McMaster University Hamilton Health Sciences / Hamilton General Hospital
state: Ontario
Study officials:
affiliation: McMaster University
name: John F Connolly, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Hamilton Health Sciences - Hamilton General site
name: Alison Fox-Robichaud, MD
role: STUDY_CHAIR
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