<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/NCT04418245</url>
  </required_header>
  <id_info>
    <org_study_id>NIMAO/2020/COVID 19-IA/JF-01</org_study_id>
    <nct_id>NCT04418245</nct_id>
  </id_info>
  <brief_title>CT Biomarkers Identification by Artificial Intelligence for COVID-19 Prognosis</brief_title>
  <acronym>COVID 19-IA</acronym>
  <official_title>Identification of Thoracic CT Scan Biomarkers by Deep Learning for Evaluating the Prognosis of Patients With COVID-19 Disease</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Centre Hospitalier Universitaire de Nīmes</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Centre Hospitalier Universitaire de Nīmes</source>
  <oversight_info>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      The study hypothesis is that low-dose computed tomography (LDCT) coupled with artificial&#xD;
      intelligence by deep learning would generate imaging biomarkers linked to the patient's&#xD;
      short- and medium-term prognosis.&#xD;
&#xD;
      The purpose of this study is to rapidly make available an early decision-making tool (from&#xD;
      the first hospital consultation of the patient with symptoms related to SARS-CoV-2) based on&#xD;
      the integration of several biomarkers (clinical, biological, imaging by thoracic scanner)&#xD;
      allowing both personalized medicine and better anticipation of the patient's evolution in&#xD;
      terms of care organization.&#xD;
    </textblock>
  </brief_summary>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Active, not recruiting</last_known_status>
  <start_date type="Actual">March 1, 2020</start_date>
  <completion_date type="Anticipated">September 2021</completion_date>
  <primary_completion_date type="Anticipated">September 2021</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Case-Only</observational_model>
    <time_perspective>Retrospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Vital status</measure>
    <time_frame>Day 8</time_frame>
    <description>Dead/alive</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Patient requiring more than 3 liters of oxygen to maintain a saturation &gt;95% (intensive care unit or resuscitation department)</measure>
    <time_frame>Day 8</time_frame>
    <description>Yes/no</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Percentage of lung affected on CT</measure>
    <time_frame>Day 0</time_frame>
    <description>% ground glass and condensation calculated by deep learning</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Percentage of lung affected by ground glass opacity on scan</measure>
    <time_frame>Day 0</time_frame>
    <description>% calculated by deep learning</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Percentage of lung affected by condensation on scan</measure>
    <time_frame>Day 0</time_frame>
    <description>% calculated by deep learning</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Vital status</measure>
    <time_frame>Day 16</time_frame>
    <description>Dead/alive</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Vital status</measure>
    <time_frame>Day 30</time_frame>
    <description>Dead/alive</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Length of hospitalization</measure>
    <time_frame>Maximum 30 days</time_frame>
    <description>Days</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>rehospitalization</measure>
    <time_frame>Day 30</time_frame>
    <description>Yes/no</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Duration of intubation</measure>
    <time_frame>Day 30</time_frame>
    <description>Days</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Percentage of lung affected on CT</measure>
    <time_frame>Day 16</time_frame>
    <description>% ground glass and condensation calculated by deep learning</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Percentage of lung affected by ground glass opacity on scan</measure>
    <time_frame>Day 16</time_frame>
    <description>% calculated by deep learning</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Percentage of lung affected by condensation on scan</measure>
    <time_frame>Day 16</time_frame>
    <description>% calculated by deep learning</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Software operating time</measure>
    <time_frame>End of study (August 2020)</time_frame>
    <description>Speed of image loading and image processing depending of brand of scanner</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>C-reactive protein levels</measure>
    <time_frame>Admission Day 0</time_frame>
    <description>mg/L</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>lactate dehydrogenase</measure>
    <time_frame>Admission Day 0</time_frame>
    <description>U/L</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>lymphocytemia</measure>
    <time_frame>Admission Day 0</time_frame>
    <description>g/L</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>D Dimers level</measure>
    <time_frame>Admission Day 0</time_frame>
    <description>µg/L</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Time until onset of symptoms</measure>
    <time_frame>Admission Day 0</time_frame>
    <description>Days</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Time between RT-PCR positive results and first scan</measure>
    <time_frame>Admission Day 0</time_frame>
    <description>Hours</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Age</measure>
    <time_frame>Admission Day 0</time_frame>
    <description>Years</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>BMI&gt; 30</measure>
    <time_frame>Admission Day 0</time_frame>
    <description>Yes/no:</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Medical history of cardiovascular disease</measure>
    <time_frame>Admission Day 0</time_frame>
    <description>Yes/no: hypertension, coronary artery disease, congestive heart failure, cardiac arrhythmia</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Diabetes</measure>
    <time_frame>Admission Day 0</time_frame>
    <description>Yes/no</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Medical history of respiratory disease</measure>
    <time_frame>Admission Day 0</time_frame>
    <description>Yes/no: Chronic obstructive pulmonary disease, chronic respiratory failure</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Medical history of immunosuppressed condition</measure>
    <time_frame>Admission Day 0</time_frame>
    <description>Yes/no: steroid use, pre-existing immunological condition, current chemotherapy for cancer</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Current or previous history of smoking</measure>
    <time_frame>Admission Day 0</time_frame>
    <description>Yes/no:</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Calculate a prognostic score from clinical, biological and CT parameters</measure>
    <time_frame>Day 8</time_frame>
    <description>Deep learning algorithm</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Calculate a prognostic score from clinical and biological parameters only</measure>
    <time_frame>Day 8</time_frame>
    <description>Deep learning algorithm</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Compare receiver operating curves of prognostic scores with and without CT parameters</measure>
    <time_frame>Day 8</time_frame>
  </secondary_outcome>
  <number_of_groups>1</number_of_groups>
  <enrollment type="Anticipated">1000</enrollment>
  <condition>Covid-19</condition>
  <arm_group>
    <arm_group_label>Patients positive for SARS-CoV-2</arm_group_label>
  </arm_group>
  <intervention>
    <intervention_type>Diagnostic Test</intervention_type>
    <intervention_name>Imaging by thoracic scanner</intervention_name>
    <description>Low-dose computed tomography</description>
    <arm_group_label>Patients positive for SARS-CoV-2</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Patients hospitalized for Covid-19 confirmed by RT-PCR and undergoing CT scan&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Patients positive for SARS-CoV-2 according to RT-PCR test between 1st March and 31st&#xD;
             May 2020&#xD;
&#xD;
          -  Patients undergoing low dose CT scan to establish Covid-19 lung damage&#xD;
&#xD;
          -  Available for at least 8 days follow-up&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
        • Patients opposing the retrospective use of their data&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>N/A</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Julien Frandon</last_name>
    <role>Principal Investigator</role>
    <affiliation>CHU Nimes</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>CHU la Timone</name>
      <address>
        <city>Marseille</city>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>CHU Montpellier</name>
      <address>
        <city>Montpellier</city>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>CHU de Nimes</name>
      <address>
        <city>Nîmes</city>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>CHU Poitiers</name>
      <address>
        <city>Poitiers</city>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>CHU Strasbourg</name>
      <address>
        <city>Strasbourg</city>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>CHU Martinique</name>
      <address>
        <city>Fort-de-France</city>
        <country>Martinique</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>France</country>
    <country>Martinique</country>
  </location_countries>
  <verification_date>August 2021</verification_date>
  <study_first_submitted>June 4, 2020</study_first_submitted>
  <study_first_submitted_qc>June 4, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 5, 2020</study_first_posted>
  <last_update_submitted>August 6, 2021</last_update_submitted>
  <last_update_submitted_qc>August 6, 2021</last_update_submitted_qc>
  <last_update_posted type="Actual">August 9, 2021</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>low dose CT scan</keyword>
  <keyword>biomarkers</keyword>
  <keyword>artificial intelligence</keyword>
  <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/NCT04418245
org study id: NIMAO/2020/COVID 19-IA/JF-01
nct id: NCT04418245
lead sponsor: 
is fda regulated drug: No
is fda regulated device: No
The study hypothesis is that low-dose computed tomography (LDCT) coupled with artificial
      intelligence by deep learning would generate imaging biomarkers linked to the patient's
      short- and medium-term prognosis.

      The purpose of this study is to rapidly make available an early decision-making tool (from
      the first hospital consultation of the patient with symptoms related to SARS-CoV-2) based on
      the integration of several biomarkers (clinical, biological, imaging by thoracic scanner)
      allowing both personalized medicine and better anticipation of the patient's evolution in
      terms of care organization.
observational model: Case-Only
time perspective: Retrospective
measure: Vital status
time frame: Day 8
description: Dead/alive
measure: Patient requiring more than 3 liters of oxygen to maintain a saturation >95% (intensive care unit or resuscitation department)
time frame: Day 8
description: Yes/no
measure: Percentage of lung affected on CT
time frame: Day 0
description: % ground glass and condensation calculated by deep learning
measure: Percentage of lung affected by ground glass opacity on scan
time frame: Day 0
description: % calculated by deep learning
measure: Percentage of lung affected by condensation on scan
time frame: Day 0
description: % calculated by deep learning
measure: Vital status
time frame: Day 16
description: Dead/alive
measure: Vital status
time frame: Day 30
description: Dead/alive
measure: Length of hospitalization
time frame: Maximum 30 days
description: Days
measure: rehospitalization
time frame: Day 30
description: Yes/no
measure: Duration of intubation
time frame: Day 30
description: Days
measure: Percentage of lung affected on CT
time frame: Day 16
description: % ground glass and condensation calculated by deep learning
measure: Percentage of lung affected by ground glass opacity on scan
time frame: Day 16
description: % calculated by deep learning
measure: Percentage of lung affected by condensation on scan
time frame: Day 16
description: % calculated by deep learning
measure: Software operating time
time frame: End of study (August 2020)
description: Speed of image loading and image processing depending of brand of scanner
measure: C-reactive protein levels
time frame: Admission Day 0
description: mg/L
measure: lactate dehydrogenase
time frame: Admission Day 0
description: U/L
measure: lymphocytemia
time frame: Admission Day 0
description: g/L
measure: D Dimers level
time frame: Admission Day 0
description: µg/L
measure: Time until onset of symptoms
time frame: Admission Day 0
description: Days
measure: Time between RT-PCR positive results and first scan
time frame: Admission Day 0
description: Hours
measure: Age
time frame: Admission Day 0
description: Years
measure: BMI> 30
time frame: Admission Day 0
description: Yes/no:
measure: Medical history of cardiovascular disease
time frame: Admission Day 0
description: Yes/no: hypertension, coronary artery disease, congestive heart failure, cardiac arrhythmia
measure: Diabetes
time frame: Admission Day 0
description: Yes/no
measure: Medical history of respiratory disease
time frame: Admission Day 0
description: Yes/no: Chronic obstructive pulmonary disease, chronic respiratory failure
measure: Medical history of immunosuppressed condition
time frame: Admission Day 0
description: Yes/no: steroid use, pre-existing immunological condition, current chemotherapy for cancer
measure: Current or previous history of smoking
time frame: Admission Day 0
description: Yes/no:
measure: Calculate a prognostic score from clinical, biological and CT parameters
time frame: Day 8
description: Deep learning algorithm
measure: Calculate a prognostic score from clinical and biological parameters only
time frame: Day 8
description: Deep learning algorithm
measure: Compare receiver operating curves of prognostic scores with and without CT parameters
time frame: Day 8
arm group label: Patients positive for SARS-CoV-2
intervention type: Diagnostic Test
intervention name: Imaging by thoracic scanner
description: Low-dose computed tomography
arm group label: Patients positive for SARS-CoV-2
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: N/A
maximum age: N/A
healthy volunteers: No
last name: Julien Frandon
role: Principal Investigator
affiliation: CHU Nimes
facility: 
facility: 
facility: 
facility: 
facility: 
facility: 
country: France
country: Martinique
responsible party type: Sponsor
mesh term: COVID-19