<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/NCT04423991</url>
  </required_header>
  <id_info>
    <org_study_id>060820</org_study_id>
    <nct_id>NCT04423991</nct_id>
  </id_info>
  <brief_title>Identification of a Responsive Subpopulation to Hydroxychloroquine in COVID-19 Patients Using Machine Learning</brief_title>
  <acronym>IDENTIFY</acronym>
  <official_title>Identification of a Responsive Subpopulation to Hydroxychloroquine in COVID-19 Patients Using Machine Learning: the IDENTIFY Trial</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Dascena</agency>
      <agency_class>Industry</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Dascena</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>
      The purpose of this study was to assess the performance of a machine learning algorithm which&#xD;
      identifies patients for whom hydroxychloroquine treatment is associated with predicted&#xD;
      survival.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      In a multi-center pragmatic clinical trial, COVID-19 positive patients admitted to 6 United&#xD;
      States medical centers were enrolled between March 10 and June 4, 2020. A machine learning&#xD;
      algorithm was used to determine which patients were suitable for treatment with&#xD;
      hydroxychloroquine.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">March 10, 2020</start_date>
  <completion_date type="Actual">June 4, 2020</completion_date>
  <primary_completion_date type="Actual">June 4, 2020</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>Non-Randomized</allocation>
    <intervention_model>Parallel Assignment</intervention_model>
    <primary_purpose>Diagnostic</primary_purpose>
    <masking>None (Open Label)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>Mortality outcome</measure>
    <time_frame>Through study completion, an average of 3 months</time_frame>
    <description>Time to in-hospital death</description>
  </primary_outcome>
  <number_of_arms>1</number_of_arms>
  <enrollment type="Actual">290</enrollment>
  <condition>COVID-19</condition>
  <condition>Coronavirus</condition>
  <condition>Mortality</condition>
  <arm_group>
    <arm_group_label>Exposed group</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>All patients were exposed to the algorithm and were characterized as being likely responders to hydroxychloroquine treatment. Treatment decisions regarding the administration of hydroxychloroquine were made independently by care providers.</description>
  </arm_group>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>COViage</intervention_name>
    <description>Machine learning intervention</description>
    <arm_group_label>Exposed group</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Patient admitted to covered ward and tested positive for COVID-19&#xD;
&#xD;
          -  Patient had COViage applied to electronic health record data within four hours of&#xD;
             COVID-19 test&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Patient not admitted to covered ward or tested negative for COVID-19&#xD;
&#xD;
          -  Patient had COViage applied to electronic health record data greater than four hours&#xD;
             after COVID-19 test&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>N/A</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <location>
    <facility>
      <name>Dascena</name>
      <address>
        <city>Oakland</city>
        <state>California</state>
        <zip>94612</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <verification_date>June 2020</verification_date>
  <study_first_submitted>June 8, 2020</study_first_submitted>
  <study_first_submitted_qc>June 8, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 9, 2020</study_first_posted>
  <last_update_submitted>June 8, 2020</last_update_submitted>
  <last_update_submitted_qc>June 8, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">June 9, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>COVID-19</mesh_term>
  </condition_browse>
  <!-- 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/NCT04423991
org study id: 060820
nct id: NCT04423991
lead sponsor: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
The purpose of this study was to assess the performance of a machine learning algorithm which
      identifies patients for whom hydroxychloroquine treatment is associated with predicted
      survival.
In a multi-center pragmatic clinical trial, COVID-19 positive patients admitted to 6 United
      States medical centers were enrolled between March 10 and June 4, 2020. A machine learning
      algorithm was used to determine which patients were suitable for treatment with
      hydroxychloroquine.
allocation: Non-Randomized
intervention model: Parallel Assignment
primary purpose: Diagnostic
masking: None (Open Label)
measure: Mortality outcome
time frame: Through study completion, an average of 3 months
description: Time to in-hospital death
arm group label: Exposed group
arm group type: Experimental
description: All patients were exposed to the algorithm and were characterized as being likely responders to hydroxychloroquine treatment. Treatment decisions regarding the administration of hydroxychloroquine were made independently by care providers.
intervention type: Device
intervention name: COViage
description: Machine learning intervention
arm group label: Exposed group
criteria: 
gender: All
minimum age: N/A
maximum age: N/A
healthy volunteers: No
facility: 
country: United States
responsible party type: Sponsor
mesh term: COVID-19