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Difficulties in setting up the study
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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Patients positive for SARS-CoV-2 |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Imaging by thoracic scanner | Diagnostic Test | Low-dose computed tomography |
|
| Measure | Description | Time Frame |
|---|---|---|
| Vital status | Dead/alive | Day 8 |
| Patient requiring more than 3 liters of oxygen to maintain a saturation >95% (intensive care unit or resuscitation department) | Yes/no | Day 8 |
| Percentage of lung affected on CT | % ground glass and condensation calculated by deep learning | Day 0 |
| Percentage of lung affected by ground glass opacity on scan | % calculated by deep learning | Day 0 |
| Percentage of lung affected by condensation on scan | % calculated by deep learning | Day 0 |
| Measure | Description | Time Frame |
|---|---|---|
| Vital status | Dead/alive | Day 16 |
| Vital status | Dead/alive | Day 30 |
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Inclusion Criteria:
Exclusion Criteria:
• Patients opposing the retrospective use of their data
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Patients hospitalized for Covid-19 confirmed by RT-PCR and undergoing CT scan
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| Name | Affiliation | Role |
|---|---|---|
| Julien Frandon | CHU Nimes | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| CHU la Timone | Marseille | France | ||||
| CHU Montpellier |
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| ID | Term |
|---|---|
| D000086382 | COVID-19 |
| ID | Term |
|---|---|
| D011024 | Pneumonia, Viral |
| D011014 | Pneumonia |
| D012141 | Respiratory Tract Infections |
| D007239 | Infections |
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| Length of hospitalization |
Days |
| Maximum 30 days |
| rehospitalization | Yes/no | Day 30 |
| Duration of intubation | Days | Day 30 |
| Percentage of lung affected on CT | % ground glass and condensation calculated by deep learning | Day 16 |
| Percentage of lung affected by ground glass opacity on scan | % calculated by deep learning | Day 16 |
| Percentage of lung affected by condensation on scan | % calculated by deep learning | Day 16 |
| Software operating time | Speed of image loading and image processing depending of brand of scanner | End of study (August 2020) |
| C-reactive protein levels | mg/L | Admission Day 0 |
| lactate dehydrogenase | U/L | Admission Day 0 |
| lymphocytemia | g/L | Admission Day 0 |
| D Dimers level | µg/L | Admission Day 0 |
| Time until onset of symptoms | Days | Admission Day 0 |
| Time between RT-PCR positive results and first scan | Hours | Admission Day 0 |
| Age | Years | Admission Day 0 |
| BMI> 30 | Yes/no: | Admission Day 0 |
| Medical history of cardiovascular disease | Yes/no: hypertension, coronary artery disease, congestive heart failure, cardiac arrhythmia | Admission Day 0 |
| Diabetes | Yes/no | Admission Day 0 |
| Medical history of respiratory disease | Yes/no: Chronic obstructive pulmonary disease, chronic respiratory failure | Admission Day 0 |
| Medical history of immunosuppressed condition | Yes/no: steroid use, pre-existing immunological condition, current chemotherapy for cancer | Admission Day 0 |
| Current or previous history of smoking | Yes/no: | Admission Day 0 |
| Calculate a prognostic score from clinical, biological and CT parameters | Deep learning algorithm | Day 8 |
| Calculate a prognostic score from clinical and biological parameters only | Deep learning algorithm | Day 8 |
| Compare receiver operating curves of prognostic scores with and without CT parameters | Day 8 |
| Montpellier |
| France |
| CHU de Nimes | Nîmes | France |
| CHU Poitiers | Poitiers | France |
| CHU Strasbourg | Strasbourg | France |
| CHU Martinique | Fort-de-France | Martinique |
| D014777 |
| Virus Diseases |
| D018352 | Coronavirus Infections |
| D003333 | Coronaviridae Infections |
| D030341 | Nidovirales Infections |
| D012327 | RNA Virus Infections |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |