<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/NCT04481620</url>
  </required_header>
  <id_info>
    <org_study_id>CHUBX 2020/23</org_study_id>
    <nct_id>NCT04481620</nct_id>
  </id_info>
  <brief_title>Development and Validation of a Prediction Model for the Transition From Mild to Moderate Form of COVID-19, Using Data From Chest CT</brief_title>
  <acronym>PREDICTCovid19</acronym>
  <official_title>Evelopment and Validation of a Prediction Model for the Transition From Mild to Moderate Form of COVID-19, Using Data From Chest CT</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>University Hospital, Bordeaux</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Programme Hospitalier de Recherche Clinique Inter-Régionale (PHRC-I)</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>University Hospital, Bordeaux</source>
  <oversight_info>
    <has_dmc>No</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>
      Only 5% of patients infected with COVID-19 develop severe or critical Coronavirus disease&#xD;
      2019 (COVID-19) and there is no reliable risk stratification tool for non-severe COVID-19&#xD;
      patients at admission.&#xD;
&#xD;
      Finding a way to predict which patients with an initial mild to moderate presentation of&#xD;
      COVID-19 would develop severe or critical form of COVID-19 according to CT-scan data, simple&#xD;
      clinical and biological parameters is challenging. In this multicentric study, the study aims&#xD;
      to construct a predictive score for early identification of cases at high risk of progression&#xD;
      to moderate, severe or critical COVID-19 combining simple clinical and biological parameters&#xD;
      and qualitative, quantitative or artificial intelligence (AI) data from the initial CT from&#xD;
      non-severe patients.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      A few numbers of patients infected with Coronavirus disease 2019 (COVID-19) rapidly develop&#xD;
      acute respiratory distress leading to respiratory failure, with high short-term mortality&#xD;
      rates. However, only 5% of patients infected with COVID-19 are concerned by this pejorative&#xD;
      evolution. At present, there is no reliable risk stratification tool for non-severe COVID-19&#xD;
      patients at admission.&#xD;
&#xD;
      Chest computed tomography (CT) is widely used for the management of COVID-19 pneumonia&#xD;
      because of its availability and quickness. The standard of reference for confirming COVID-19&#xD;
      relies on microbiological tests but these tests might not be available in an emergency&#xD;
      setting and their results are not immediately available, contrary to CT. In addition to its&#xD;
      role for early diagnosis, CT has a prognostic role through evaluating the extent of COVID-19&#xD;
      lung abnormalities.&#xD;
&#xD;
      Finding a way to predict which patients with an initial mild to moderate presentation of&#xD;
      COVID-19 would develop severe or critical form of COVID-19 according to CT-scan data, simple&#xD;
      clinical and biological parameters is challenging. In this multicentric study, the study aims&#xD;
      to construct a predictive score for early identification of cases at high risk of progression&#xD;
      to moderate, severe or critical COVID-19 combining simple clinical and biological parameters&#xD;
      and qualitative, quantitative or artificial intelligence (AI) data from the initial CT from&#xD;
      non-severe patients. The final objective is to organize optimal patient management in the&#xD;
      appropriate health structure.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">August 31, 2020</start_date>
  <completion_date type="Actual">May 4, 2021</completion_date>
  <primary_completion_date type="Actual">May 4, 2021</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>Retrospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>occurrence of significant clinical degradation</measure>
    <time_frame>Day 30 following the initial chest CT</time_frame>
    <description>The primary outcome is defined by the occurrence of significant clinical degradation within 30 days following the initial chest CT.&#xD;
Significant clinical degradation is defined by the transition from the mild to the moderate form of COVID-19, i.e., according to the WHO criteria, the requirement of oxygen between 3 and 5 L / min to achieve saturation greater than 97% and a respiratory rate &lt;25 / min without the need for invasive ventilation.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>occurrence of a severe form</measure>
    <time_frame>Day 30 following the initial chest CT</time_frame>
    <description>the occurrence of a severe form, defined by the need for oxygen therapy greater than 5L / min to obtain a percutaneous oxygen saturation greater than 97%, within 30 days following the initial chest CT</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>occurrence of an orotracheal intubation</measure>
    <time_frame>Day 30 following the initial chest CT</time_frame>
    <description>the occurrence of an orotracheal intubation within 30 days following the initial chest CT (binary: yes/no)</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>occurrence of an Acute Respiratory Distress Syndrom</measure>
    <time_frame>Day 30 following the initial chest CT</time_frame>
    <description>the occurrence of an Acute Respiratory Distress Syndrom according to the Berlin criteria (JAMA 2012) within 30 days following the initial chest CT (binary: yes/no)</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>average length of stay in hospital</measure>
    <time_frame>Month 1</time_frame>
    <description>the average length of stay in hospital (days)</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>mortality</measure>
    <time_frame>Day 30 following the initial chest CT</time_frame>
    <description>mortality within 30 days following the initial chest CT (binary: yes/no)</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>evolution of the imaging parameters</measure>
    <time_frame>Day 30 following the initial chest CT</time_frame>
    <description>evolution of the imaging parameters of the successive thoracic CT scans in the acute phase of COVID-19, in patients with a positive diagnosis of COVID-19 (positive RT-PCR or positive serology)</description>
  </secondary_outcome>
  <enrollment type="Actual">1329</enrollment>
  <condition>COVID-19</condition>
  <eligibility>
    <study_pop>
      <textblock>
        Patients with suspicion of Covid-19&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  First chest CT, assessed for respiratory symptoms, without injection of contrast agent&#xD;
             for respiratory symptoms, and whose results of the CT subjective visual analysis are&#xD;
             compatible or typical of COVID-19&#xD;
&#xD;
          -  biological diagnosis of COVID-19 (RT-PCR) or clinical suspicion (cough and / or&#xD;
             dyspnea and / or fever and / or need to use oxygen therapy as part of routine care) at&#xD;
             the time of the examination&#xD;
&#xD;
          -  Authorization of the patient for the processing of his personal data, except CNIL&#xD;
             exemption&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Patient with a moderate (oxygen between 3 and 5 L / min to achieve saturation greater&#xD;
             than 97% and a respiratory rate &lt;25 / min without the need for invasive ventilation),&#xD;
             severe form (oxygen therapy&gt; 5L / min to obtain a SpO2&gt; 97%) or critical form (need to&#xD;
             resort to ventilation and / or orotracheal intubation) at the date of the first chest&#xD;
             CT&#xD;
&#xD;
          -  Age &lt; 18 years old&#xD;
&#xD;
          -  Patient deprived of liberty by judicial decision&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <location>
    <facility>
      <name>CHU Bordeaux</name>
      <address>
        <city>Bordeaux</city>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Clinique Bordeaux Nord</name>
      <address>
        <city>Bordeaux</city>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Clinique Saint Augustin</name>
      <address>
        <city>Bordeaux</city>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>CHU de Grenoble Alpes</name>
      <address>
        <city>Grenoble</city>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Hôpital Arnaud-de-Villeneuve CHU de Montpellier</name>
      <address>
        <city>Montpellier</city>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Hôpitaux de Brabois CHU de Nancy</name>
      <address>
        <city>Nancy</city>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Hôpital de la Milétrie CHU de Poitiers</name>
      <address>
        <city>Poitiers</city>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>France</country>
  </location_countries>
  <verification_date>April 2022</verification_date>
  <study_first_submitted>July 20, 2020</study_first_submitted>
  <study_first_submitted_qc>July 21, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">July 22, 2020</study_first_posted>
  <last_update_submitted>April 11, 2022</last_update_submitted>
  <last_update_submitted_qc>April 11, 2022</last_update_submitted_qc>
  <last_update_posted type="Actual">April 12, 2022</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>COVID-19</keyword>
  <keyword>SARS-CoV-2</keyword>
  <keyword>CT</keyword>
  <keyword>prediction score</keyword>
  <keyword>prognosis</keyword>
  <keyword>mild-to moderate form</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>COVID-19</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </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/NCT04481620
org study id: CHUBX 2020/23
nct id: NCT04481620
lead sponsor: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
Only 5% of patients infected with COVID-19 develop severe or critical Coronavirus disease
      2019 (COVID-19) and there is no reliable risk stratification tool for non-severe COVID-19
      patients at admission.

      Finding a way to predict which patients with an initial mild to moderate presentation of
      COVID-19 would develop severe or critical form of COVID-19 according to CT-scan data, simple
      clinical and biological parameters is challenging. In this multicentric study, the study aims
      to construct a predictive score for early identification of cases at high risk of progression
      to moderate, severe or critical COVID-19 combining simple clinical and biological parameters
      and qualitative, quantitative or artificial intelligence (AI) data from the initial CT from
      non-severe patients.
A few numbers of patients infected with Coronavirus disease 2019 (COVID-19) rapidly develop
      acute respiratory distress leading to respiratory failure, with high short-term mortality
      rates. However, only 5% of patients infected with COVID-19 are concerned by this pejorative
      evolution. At present, there is no reliable risk stratification tool for non-severe COVID-19
      patients at admission.

      Chest computed tomography (CT) is widely used for the management of COVID-19 pneumonia
      because of its availability and quickness. The standard of reference for confirming COVID-19
      relies on microbiological tests but these tests might not be available in an emergency
      setting and their results are not immediately available, contrary to CT. In addition to its
      role for early diagnosis, CT has a prognostic role through evaluating the extent of COVID-19
      lung abnormalities.

      Finding a way to predict which patients with an initial mild to moderate presentation of
      COVID-19 would develop severe or critical form of COVID-19 according to CT-scan data, simple
      clinical and biological parameters is challenging. In this multicentric study, the study aims
      to construct a predictive score for early identification of cases at high risk of progression
      to moderate, severe or critical COVID-19 combining simple clinical and biological parameters
      and qualitative, quantitative or artificial intelligence (AI) data from the initial CT from
      non-severe patients. The final objective is to organize optimal patient management in the
      appropriate health structure.
observational model: Cohort
time perspective: Retrospective
measure: occurrence of significant clinical degradation
time frame: Day 30 following the initial chest CT
description: The primary outcome is defined by the occurrence of significant clinical degradation within 30 days following the initial chest CT.
Significant clinical degradation is defined by the transition from the mild to the moderate form of COVID-19, i.e., according to the WHO criteria, the requirement of oxygen between 3 and 5 L / min to achieve saturation greater than 97% and a respiratory rate <25 / min without the need for invasive ventilation.
measure: occurrence of a severe form
time frame: Day 30 following the initial chest CT
description: the occurrence of a severe form, defined by the need for oxygen therapy greater than 5L / min to obtain a percutaneous oxygen saturation greater than 97%, within 30 days following the initial chest CT
measure: occurrence of an orotracheal intubation
time frame: Day 30 following the initial chest CT
description: the occurrence of an orotracheal intubation within 30 days following the initial chest CT (binary: yes/no)
measure: occurrence of an Acute Respiratory Distress Syndrom
time frame: Day 30 following the initial chest CT
description: the occurrence of an Acute Respiratory Distress Syndrom according to the Berlin criteria (JAMA 2012) within 30 days following the initial chest CT (binary: yes/no)
measure: average length of stay in hospital
time frame: Month 1
description: the average length of stay in hospital (days)
measure: mortality
time frame: Day 30 following the initial chest CT
description: mortality within 30 days following the initial chest CT (binary: yes/no)
measure: evolution of the imaging parameters
time frame: Day 30 following the initial chest CT
description: evolution of the imaging parameters of the successive thoracic CT scans in the acute phase of COVID-19, in patients with a positive diagnosis of COVID-19 (positive RT-PCR or positive serology)
study pop: 
sampling method: Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
facility: 
facility: 
facility: 
facility: 
facility: 
facility: 
facility: 
country: France
responsible party type: Sponsor
mesh term: COVID-19
sharing ipd: No