Trial document




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  DRKS00023913

Trial Description

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Title

Multicenter, exploratory, retrospective observational study to identify optimal CT imaging biomarkers in combination with clinical markers and PCR-RT for the diagnosis and assessment of the therapeutic response of COVID-19 using artificial intelligence

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Trial Acronym

ExploreCOVID

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URL of the Trial

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Brief Summary in Lay Language

SARS-CoV-2 infection is currently diagnosed using a special molecular biological technique, the so-called polymerase chain reaction (PCR). In order to be able to assess the severity and development of pneumonia in COVID-19 disease, computed tomography (CT) of the lungs is traditionally performed. However, the PCR does not give an exact indication of the stage of the disease.
Previous findings allow the conclusion that there are mutual relationships between the stage of the disease and the appearance on CT.
As part of the study, a procedure for the exact and uniform assessment of COVID-19 diseases is being developed in order to find risk factors. For this purpose, the radiological findings and the clinical course between COVID-19 patients and patients with suspected pneumonia of another cause or patients without lung disease are compared.
Artificial intelligence (AI) is also used in the study as an aid to assess the proposed catalog of criteria.

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Brief Summary in Scientific Language

Currently, the diagnosis of SARS-CoV-2 infection is usually made with a real-time polymerase chain reaction (RT-PCR). The extent and dynamics of pneumonia in COVID-19 is classically assessed using computed tomography (CT) of the thorax. While the RT-PCR method detects the virus, it does not provide quantitative data on the stage of the disease.
Findings so far allow the conclusion that there are correlations between the stage of the disease and the radiological appearance.
As part of the study, a procedure for the quantitative and standardised assessment and documentation of COVID-19 diseases is being developed in order to identify risk factors. For this purpose, the radiological findings and the clinical course will be compared between COVID-19 patients and patients with suspicion of pneumonias of other causes or patients without lung disease.
Artificial intelligence (AI) is also used in the study as an aid to assess a proposed catalogue of criteria.

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Do you plan to share individual participant data with other researchers?

No

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Description IPD sharing plan:

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Organizational Data

  •   DRKS00023913
  •   2020/12/21
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  •   yes
  •   Approved
  •   S-813/2020, Ethik-Kommission I der Medizinischen Fakultät Heidelberg
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Secondary IDs

  •   U1111-1262-7515 
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Health Condition or Problem studied

  •   COVID-19
  •   U07.1 -  Emergency use of U07.1
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Interventions/Observational Groups

  •   Assessment of the existing chest CT and collection of clinical data in the course -Patients with confirmed COVID-19 infection (PCR test positive)
  •   - Patients with suspected COVID-19 associated respiratory syndrome with negative PCR testing
  •   - Healthy patients without symptoms
  •   Patients with pneumonia without suspicion of COVID-19
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Characteristics

  •   Non-interventional
  •   Observational study
  •   Non-randomized controlled trial
  •   Open (masking not used)
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  •   Other
  •   Prognosis
  •   Other
  •   N/A
  •   N/A
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Primary Outcome

1. Detection of imaging biomarkers for data-based diagnosis and treatment of COVID-19 patients

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Secondary Outcome

2. Correlation of radiological findings with RT-PCR, clinical biomarkers and the course of the COVID-19 disease
3. Development of a clinical guideline and definition of clinically relevant imaging biomarkers for diagnosis, risk stratification and treatment response of COVID-19 patients
4. Development of an evidence-based guideline for the stratification and use of high-risk patients with the provision of an open access database.

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Countries of Recruitment

  •   Germany
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Locations of Recruitment

  • University Medical Center 
  • University Medical Center 
  • Medical Center 
  • University Medical Center 
  • University Medical Center 
  • University Medical Center 
  • University Medical Center 
  • University Medical Center 
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Recruitment

  •   Actual
  •   2020/12/22
  •   1000
  •   Multicenter trial
  •   National
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Inclusion Criteria

  •   Both, male and female
  •   18   Years
  •   no maximum age
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Additional Inclusion Criteria

Study population:
• Patients with confirmed COVID-19 infection
• Patients with suspected COVID-19 associated respiratory syndrome with negative PCR testing
• Existing CT images of the chest
• Age ≥ 18 years

Control cohort:
Healthy patients without symptoms and patients with lung diseases without suspicion of COVID-19

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Exclusion Criteria

Age < 18 years

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Addresses

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    • Universitätsklinikum Heidelberg
    • Im Neuenheimer Feld 672
    • 69120  Heidelberg
    • Germany
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    • Klinik für Diagnostische und Interventionelle Radiologie
    • Im Neuenheimer Feld 420
    • 69120  Heidelberg
    • Germany
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    • Diagnostische und Interventionelle Radiologie mit Nuklearmedizin Thoraxklinik
    • Röntgenstr.1
    • 69126  Heidelberg
    • Germany
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    • Klinik für Allgemein-, Viszeral- und Transplantationschirurgie, Universitätsklinik Heidelberg
    • INF 420
    • 69120  Heidelberg
    • Germany
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    • Deutsches Krebsforschungszentrum, Stiftung des öffentlichen Rechts,
    • Im Neuenheimer Feld 280
    • 69120  Heidelberg
    • Germany
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    • Mint Medical GmbH
    • Burgstr. 61
    • 69121   Heidelberg
    • Germany
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    • Klinik für Allgemein-, Viszeral- und Transplantationschirurgie
    • Mr.  Prof. Dr. med.  Pascal  Probst 
    • Im Neuenheimer Feld 420
    • 69120  Heidelberg
    • Germany
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    • Klinik für Allgemein-, Viszeral- und Transplantationschirurgie
    • Mr.  Prof. Dr. med.  Pascal  Probst 
    • Im Neuenheimer Feld 420
    • 69120  Heidelberg
    • Germany
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Sources of Monetary or Material Support

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    • Bundesministerium für Bildung und Forschung Dienstsitz Berlin
    • Friedrichstraße 130 B
    • 10117  Berlin
    • Germany
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Status

  •   Recruiting ongoing
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Trial Publications, Results and other Documents

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