COVID 19COVID-CTPREDNCT04377685
Prediction of Clinical Course in COVID19 Patients
An observational study of COVID 19, run by Centre Hospitalier Universitaire de Saint Etienne at 1 site in France.
Trial summary
In the context of the COVID19 pandemic and containment, chest CT is currently frequently performed on admission, looking for suggestive signs and basic abnormalities of COVID19 compatible viral pneumonitis pending confirmation of identification of viral RNA by reverse-transcription polymerase chain reaction(PCR), with… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- COVID 19
- What is being tested?
- CT-Scan
- How many people
- 826 participants
- Where
- 1 siteFrance
- Study dates
- 1 Mar 2020to 26 Dec 2020
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Adult, Older adult
Study population: patient admitted to the emergency room of COVID-19 confirmed by RT-PCR
Who may be able to join
- age ≥ 18 years
- clinical suspicion of COVID-19 confirmed by RT-PCR
- CT scan at ER admission
- RT-PCR sampling
Who cannot join
- CT scan failure or loss of CT data
- RT-PCR initial results unavailable
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Prediction of Clinical Course in COVID19 Patients Using Unsupervised Classification Approaches of Clinical, Biological and the Multiparametric Signature of the Chest CT Scan Performed at Admission
In the context of the COVID19 pandemic and containment, chest CT is currently frequently performed on admission, looking for suggestive signs and basic abnormalities of COVID19 compatible viral pneumonitis pending confirmation of identification of viral RNA by reverse-transcription polymerase chain reaction(PCR), with a reported sensitivity of 56-88% in the first few days, slightly higher than PCR (60%) (1). Nevertheless, currently established radiological abnormalities are not specific for COVID19 and the specificity of the chest CT is ~25% when PCR is used as a reference (1). Deconfinement and its consequences will complicate the triage of COVID patients and the role of the scanner, with the expected impact of a decrease in the prevalence of infection in the emergency department and an increase in the number of "all-round" patients, including patients with non-COVID viral infiltrates or pneumopathies.
In addition, there are currently no imaging criteria to complement the clinical and biological data that can predict the progression of lung disease from the initial data.
Read the detailed description
In image processing, computational medical imaging has demonstrated its ability to predict a therapeutic response or a particular evolution after extracting relevant anatomical, functional or even non-visually perceptible information from the volume of images, making it possible to construct a powerful radiomic signature or to use robust anatomical/functional measurements to provide estimates of ventilation or vascular state. By combining these data extracted from the scanner with the standard clinical-biological data produced at admission during triage, our ambition is to build a predictive model using unsupervised classification approaches capable of helping predict clinical evolution with the aim of optimizing the management of the resource.
- Conditions
- COVID 19
- Being tested
- CT-Scan
- Keywords
- COVID 19, chest CT scan
- Medical subject headings
- COVID-19
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
CT-ScanOther
Chest CT scan on admission to the hospital
| Study group | Role | What they receive |
|---|---|---|
| COVID19 patients | Patient tested positive for SARS-CoV-2 who had a CT scan Receives: Other: CT-Scan |
What is being measured
The primary outcome is the main question the study is designed to answer.
diagnostic of COVID disease composite
The diagnostoc of COVID disease is composite of:
- CT features wich will include presence/location/laterality of morphological CT abonormal densities (ground glass opacities, consolidations, reticulations),
- pulmonary vessels size,
- distribution and abnormalities,
- local / global CT-ventilation index (CT-VI) severity,
- radiomic features (shape features, 1st-order and 2nd order statistics)
Analysis of CT-Scan results.
Measured: On admission to the hospital
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 May 2020
- Study start1 Mar 2020
- Primary completion28 Nov 2020
- Study completion26 Dec 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for COVID 19Studies of the same condition, open and under way.
- More trials run by Centre Hospitalier Universitaire de Saint EtienneThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 826 participants (actual)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- INSA Rennes, Institut National de la Santé Et de la Recherche Médicale, France, Centre National pour le Recherche Scientifique (CNRS), Université de Lyon, Jean Monnet University
- Responsible party
- Sponsor
- Investigators
- Pierre CROISILLE, PhD, Principal investigator, Centre Hospitalier Universitaire de Saint Etienne
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04377685
- Sponsor's protocol ID
- 20CH109
- Other IDs
- IRBN652020/CHUSTE
- Acronym
- COVID-CTPRED
- Status last verified
- Nov 2021
- First submitted
- 5 May 2020
- Last updated
- 17 Nov 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 17 Nov 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction of Clinical Course in COVID19 Patients Using Unsupervised Classification Approaches of Clinical, Biological and the Multiparametric Signature of the Chest CT Scan Performed at Admission
- ClinicalTrials.gov ID
- NCT04377685
- Acronym / sponsor ID
- COVID-CTPRED / 20CH109
- Conditions
- COVID 19
- Keywords
- COVID 19, chest CT scan
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 826 participants (actual)
- Lead sponsor
- Centre Hospitalier Universitaire de Saint EtienneUniversity, hospital or other
- Collaborators
- INSA Rennes, Institut National de la Santé Et de la Recherche Médicale, France, Centre National pour le Recherche Scientifique (CNRS), Université de Lyon, Jean Monnet University
- Study start
- 1 Mar 2020 (actual)
- Primary completion
- 28 Nov 2020 (actual)
- Study completion
- 26 Dec 2020 (actual)
- First posted
- 6 May 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
In the context of the COVID19 pandemic and containment, chest CT is currently frequently performed on admission, looking for suggestive signs and basic abnormalities of COVID19 compatible viral pneumonitis pending confirmation of identification of viral RNA by reverse-transcription polymerase chain reaction(PCR), with a reported sensitivity of 56-88% in the first few days, slightly higher than PCR (60%) (1). Nevertheless, currently established radiological abnormalities are not specific for COVID19 and the specificity of the chest CT is ~25% when PCR is used as a reference (1). Deconfinement and its consequences will complicate the triage of COVID patients and the role of the scanner, with the expected impact of a decrease in the prevalence of infection in the emergency department and an increase in the number of "all-round" patients, including patients with non-COVID viral infiltrates or pneumopathies.
In addition, there are currently no imaging criteria to complement the clinical and biological data that can predict the progression of lung disease from the initial data.
Detailed description
In image processing, computational medical imaging has demonstrated its ability to predict a therapeutic response or a particular evolution after extracting relevant anatomical, functional or even non-visually perceptible information from the volume of images, making it possible to construct a powerful radiomic signature or to use robust anatomical/functional measurements to provide estimates of ventilation or vascular state. By combining these data extracted from the scanner with the standard clinical-biological data produced at admission during triage, our ambition is to build a predictive model using unsupervised classification approaches capable of helping predict clinical evolution with the aim of optimizing the management of the resource.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| COVID19 patients | – | Other: CT-ScanPatient tested positive for SARS-CoV-2 who had a CT scan |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- patient admitted to the emergency room of COVID-19 confirmed by RT-PCR
Key inclusion criteria (4 listed)
- age ≥ 18 years
- clinical suspicion of COVID-19 confirmed by RT-PCR
- CT scan at ER admission
- RT-PCR sampling
Key exclusion criteria (2 listed)
- CT scan failure or loss of CT data
- RT-PCR initial results unavailable
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| CT-ScanChest CT scan on admission to the hospital | Other | COVID19 patients |
Primary outcome
diagnostic of COVID disease composite
Time frame: On admission to the hospital
The diagnostoc of COVID disease is composite of: * CT features wich will include presence/location/laterality of morphological CT abonormal densities (ground glass opacities, consolidations, reticulations), * pulmonary vessels size, * distribution and abnormalities, * local / global CT-ventilation index (CT-VI) severity, * radiomic features (shape features, 1st-order and 2nd order statistics) Analysis of CT-Scan results.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Nov 2021.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Pierre CROISILLE, PhD principal investigator
Centre Hospitalier Universitaire de Saint Etienne
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of COVID 19, run by Centre Hospitalier Universitaire de Saint Etienne at 1 site in France.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 17 Nov 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in France. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
diagnostic of COVID disease composite (time frame: On admission to the hospital). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Nov 2021.