<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT03826407</url>
  </required_header>
  <id_info>
    <org_study_id>ComaML2018</org_study_id>
    <secondary_id>CPG158287</secondary_id>
    <secondary_id>CHRP 523461-18</secondary_id>
    <nct_id>NCT03826407</nct_id>
  </id_info>
  <brief_title>Development of a Point of Care System for Automated Coma Prognosis</brief_title>
  <official_title>Development of a Point of Care System for Automated Coma Prognosis - A Prospective Cohort Study</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>McMaster University</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Canadian Institutes of Health Research (CIHR)</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Natural Sciences and Engineering Research Council, Canada</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Hamilton Health Sciences Corporation</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Brain Vision Solutions Inc.</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>McGill University</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>McMaster University</source>
  <oversight_info>
    <has_dmc>Yes</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      Electroencephalogram/event-related potentials (EEG/ERP) data will be collected from 50&#xD;
      participants in coma or other disorder of consciousness (DOC; i.e., Unresponsive Wakefulness&#xD;
      Syndrome [UWS] or Minimally Conscious State [MCS]), clinically diagnosed using the Glasgow&#xD;
      Coma Scale (GCS). For coma patients, EEG recordings will be conducted for up to 24&#xD;
      consecutive hours at a maximum of 5 timepoints, spanning 30 days from the date of&#xD;
      recruitment, to track participants' clinical state. For DOC patients, there will be an&#xD;
      initial EEG recording up to 24 hours, with possible subsequent weekly recordings up to 2&#xD;
      hours. An additional dataset from 40 healthy controls will be collected, each spanning up to&#xD;
      a 12-hour recording period in order to formulate a baseline. Collected data are to form the&#xD;
      basis for automatic analysis and detection of ERP components in DOC, using a machine learning&#xD;
      paradigm. Salient features (i.e., biomarkers) extracted from the ERPs and resting-state EEG&#xD;
      will be identified and combined in an optimal fashion to give an accurate indicator of&#xD;
      prognosis.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      The Problem: Coma is a state of unconsciousness with a variety of causes. Traditional tests&#xD;
      for coma outcome prediction are mainly based on a set of clinical observations (e.g.,&#xD;
      pupillary constriction). Recently however, event-related potentials (ERPs; which are&#xD;
      transient electroencephalogram [EEG] responses to auditory, visual, or tactile stimuli) have&#xD;
      been introduced as useful predictors of a positive coma outcome (i.e., emergence). However,&#xD;
      such tests require a skilled neurophysiologist, and such people are in short supply. Also,&#xD;
      none of the current approaches has sufficient positive and negative predictive accuracies to&#xD;
      provide definitive prognoses in the clinical setting.&#xD;
&#xD;
      Objective: The investigators will apply innovative machine learning methods to analyze&#xD;
      patient EEGs (50 patients and 40 healthy controls) to develop a simple, objective,&#xD;
      replicable, and inexpensive point of care system which can significantly improve the accuracy&#xD;
      of coma prognosis relative to current methods. The physical requirements of the proposed&#xD;
      system consist only of an EEG system (inexpensive in terms of medical equipment) and a&#xD;
      conventional laptop computer.&#xD;
&#xD;
      Methodology: The investigators intend to extend the team's newest algorithms and develop&#xD;
      machine learning tools for automatic analysis and detection of ERP components. Preliminary&#xD;
      results by the team in this respect have been very promising. The most salient features&#xD;
      (i.e., biomarkers) extracted from the ERP will be identified and combined in an optimal&#xD;
      fashion to give an accurate indicator of prognosis. Features will be extracted from resting&#xD;
      state brain networks and from network trajectories associated with the processing of ERP&#xD;
      signals.&#xD;
&#xD;
      Significance: The proposed work will enable critical care physicians to assess coma prognosis&#xD;
      with speed and accuracy. Thus, families and their health care team will be provided the most&#xD;
      accurate information possible to guide discussions of goals of care and life-sustaining&#xD;
      therapies in the context of dealing with the consequences of devastating neurological injury.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Active, not recruiting</overall_status>
  <start_date type="Actual">October 1, 2019</start_date>
  <completion_date type="Anticipated">December 2023</completion_date>
  <primary_completion_date type="Actual">August 12, 2022</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Cohort</observational_model>
    <time_perspective>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Change in multiple electrophysiological measures across specified time points during coma</measure>
    <time_frame>up to 30 days from date of recruitment</time_frame>
    <description>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.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Change in multiple electrophysiological measures across specified time points during MCS or UWS</measure>
    <time_frame>up to 6 months from date of recruitment</time_frame>
    <description>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.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Correlation between behavioral and electrophysiological measures after coma/DOC emergence</measure>
    <time_frame>Within a 30-day time period post recruitment</time_frame>
    <description>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.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Sensitivity and specificity of prognostic capabilities of electrophysiological measures</measure>
    <time_frame>Within a 30-day time period post recruitment</time_frame>
    <description>Analyses will compare the electrophysiological measures as outcome predictors to traditional behaviorally-based tools.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Feasibility of procedure</measure>
    <time_frame>up to 6 months from date of recruitment</time_frame>
    <description>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.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Correlation between individual patient factors, EEG results, and outcome for coma</measure>
    <time_frame>up to 30 days from date of recruitment</time_frame>
    <description>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.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Correlations between individual patient factors, EEG results, and outcome for DOC</measure>
    <time_frame>up to 6 months from date of recruitment</time_frame>
    <description>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.</description>
  </secondary_outcome>
  <number_of_groups>2</number_of_groups>
  <enrollment type="Actual">33</enrollment>
  <condition>Coma</condition>
  <condition>Disorder of Consciousness</condition>
  <condition>Minimally Conscious State</condition>
  <condition>Vegetative State</condition>
  <condition>Neuropathology</condition>
  <arm_group>
    <arm_group_label>DOC patients</arm_group_label>
    <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)</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Healthy Control</arm_group_label>
    <description>Matched healthy controls without current neurological diagnoses</description>
  </arm_group>
  <eligibility>
    <study_pop>
      <textblock>
        The Coma/DOC Group will include 50 patients from the Intensive Care Units, Neurological&#xD;
        Step Down Unit, or Coronary Care Unit at Hamilton General Hospital (Ontario, Canada) who&#xD;
        are in coma (GCS score =3-8), or who have other disorders of consciousness (MCS or UWS).&#xD;
&#xD;
        The Control Group of 40 matched healthy controls will be recruited primarily from the&#xD;
        Hamilton community (Ontario, Canada).&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Patients (≥ 18 years of age) primarily admitted to the Intensive Care Units,&#xD;
             Neurological Step Down Unit, or Coronary Care Unit at Hamilton General Hospital who&#xD;
             are in coma with Glasgow Coma Scale (GCS) score of 3-8, or;&#xD;
&#xD;
          -  Patients (≥ 18 years of age) who have other disorders of consciousness, primarily&#xD;
             Minimally Conscious State (MCS) or Unresponsive Wakefulness Syndrome (UWS; also known&#xD;
             as vegetative state).&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Severe liver failure (i.e., Child-Pugh Class C)&#xD;
&#xD;
          -  Severe renal failure (i.e., Urea ≥ 40)&#xD;
&#xD;
          -  Previous open-head injury&#xD;
&#xD;
          -  Known primary and secondary central nervous system malignancy&#xD;
&#xD;
          -  Known hearing impairment&#xD;
&#xD;
          -  Previous intracranial pathology requiring neurosurgical interventions in the past 72&#xD;
             hours&#xD;
&#xD;
          -  Anyone who is deemed medically unsuitable for this study by the attending intensivists&#xD;
&#xD;
        Healthy Controls:&#xD;
&#xD;
        Inclusion:&#xD;
&#xD;
          -  ≥ 18 years of age&#xD;
&#xD;
          -  no visual, language, learning, or hearing problems&#xD;
&#xD;
          -  no history of neurological or psychiatric disorder&#xD;
&#xD;
          -  not currently taking any medications that act on the central nervous system, such as&#xD;
             antidepressants, anxiolytics, or anti-epileptics&#xD;
&#xD;
        Exclusion:&#xD;
&#xD;
        (During the COVID-19 pandemic only)&#xD;
&#xD;
          -  ≥ 60 years of age&#xD;
&#xD;
          -  have a weakened immune system&#xD;
&#xD;
          -  have one or more of the COVID-19 high risk medical conditions, according to the&#xD;
             government of Canada website:&#xD;
             https://www.canada.ca/en/public-health/services/publications/diseases-conditions/peopl&#xD;
             e-high-risk-for-severe-illness-covid-19.html.&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>John F Connolly, PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>McMaster University</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Alison Fox-Robichaud, MD</last_name>
    <role>Study Chair</role>
    <affiliation>Hamilton Health Sciences - Hamilton General site</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>McMaster University Hamilton Health Sciences / Hamilton General Hospital</name>
      <address>
        <city>Hamilton</city>
        <state>Ontario</state>
        <zip>L8L 2X2</zip>
        <country>Canada</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Canada</country>
  </location_countries>
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    <citation>Jones C. Glasgow coma scale. Am J Nurs. 1979 Sep;79(9):1551-3. No abstract available.</citation>
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  <verification_date>February 2023</verification_date>
  <study_first_submitted>January 18, 2019</study_first_submitted>
  <study_first_submitted_qc>January 30, 2019</study_first_submitted_qc>
  <study_first_posted type="Actual">February 1, 2019</study_first_posted>
  <last_update_submitted>February 3, 2023</last_update_submitted>
  <last_update_submitted_qc>February 3, 2023</last_update_submitted_qc>
  <last_update_posted type="Actual">February 6, 2023</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>Coma</keyword>
  <keyword>Machine Learning</keyword>
  <keyword>Prognosis</keyword>
  <keyword>Electrophysiology</keyword>
  <keyword>Event-related potentials</keyword>
  <keyword>Electroencephalography</keyword>
  <keyword>Mismatch Negativity</keyword>
  <keyword>Auditory Brainstem Response</keyword>
  <keyword>Disorders of Consciousness</keyword>
  <keyword>EEG</keyword>
  <keyword>ERP</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Consciousness Disorders</mesh_term>
    <mesh_term>Coma</mesh_term>
    <mesh_term>Persistent Vegetative State</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>Undecided</sharing_ipd>
    <ipd_description>To be decided</ipd_description>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT03826407
org study id: ComaML2018
secondary id: CPG158287
secondary id: CHRP 523461-18
nct id: NCT03826407
lead sponsor: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
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.
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.
observational model: Cohort
time perspective: Prospective
measure: Change in multiple electrophysiological measures across specified time points during coma
time frame: up to 30 days from date of recruitment
description: 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.
measure: Change in multiple electrophysiological measures across specified time points during MCS or UWS
time frame: up to 6 months from date of recruitment
description: 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.
measure: Correlation between behavioral and electrophysiological measures after coma/DOC emergence
time frame: Within a 30-day time period post recruitment
description: 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.
measure: Sensitivity and specificity of prognostic capabilities of electrophysiological measures
time frame: Within a 30-day time period post recruitment
description: Analyses will compare the electrophysiological measures as outcome predictors to traditional behaviorally-based tools.
measure: Feasibility of procedure
time frame: up to 6 months from date of recruitment
description: 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.
measure: Correlation between individual patient factors, EEG results, and outcome for coma
time frame: up to 30 days from date of recruitment
description: 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.
measure: Correlations between individual patient factors, EEG results, and outcome for DOC
time frame: up to 6 months from date of recruitment
description: 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.
arm group label: 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)
arm group label: Healthy Control
description: Matched healthy controls without current neurological diagnoses
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: Accepts Healthy Volunteers
last name: John F Connolly, PhD
role: Principal Investigator
affiliation: McMaster University
last name: Alison Fox-Robichaud, MD
role: Study Chair
affiliation: Hamilton Health Sciences - Hamilton General site
facility: 
country: Canada
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responsible party type: Sponsor
mesh term: Consciousness Disorders
mesh term: Coma
mesh term: Persistent Vegetative State
sharing ipd: Undecided
ipd description: To be decided