Development of a Point of Care System for Automated Coma Prognosis

McMaster University

Status: Active, not recruiting

Conditions: Neuropathology; Vegetative State; Coma; Minimally Conscious State; Disorder of Consciousness

Study type: Observational

Funder types: Other

Identifiers

NCT03826407

CHRP 523461-18 (Other Grant/Funding Number)

CPG158287 (Other Grant/Funding Number)

ComaML2018

Details and patient eligibility

About

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.

Full 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.

Enrollment: 33 patients

Sex: All

Ages: 18+ years old

Volunteers: Accepts Healthy Volunteers

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.

Trial design

33 participants in 2 patient groups

DOC patients

Description:

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

Description:

Matched healthy controls without current neurological diagnoses