Covid-19
Conditions
Keywords
low dose CT scan, biomarkers, artificial intelligence
Brief summary
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.
Interventions
DIAGNOSTIC_TESTImaging by thoracic scanner
Low-dose computed tomography
Sponsors
Centre Hospitalier Universitaire de Nīmes
Study design
Observational model
CASE_ONLY
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
* Patients positive for SARS-CoV-2 according to RT-PCR test between 1st March and 31st May 2020 * Patients undergoing low dose CT scan to establish Covid-19 lung damage * Available for at least 8 days follow-up
Exclusion criteria
• Patients opposing the retrospective use of their data
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Vital status | Day 8 | Dead/alive |
| Patient requiring more than 3 liters of oxygen to maintain a saturation >95% (intensive care unit or resuscitation department) | Day 8 | Yes/no |
| Percentage of lung affected on CT | Day 0 | % ground glass and condensation calculated by deep learning |
| Percentage of lung affected by ground glass opacity on scan | Day 0 | % calculated by deep learning |
| Percentage of lung affected by condensation on scan | Day 0 | % calculated by deep learning |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Percentage of lung affected on CT | Day 16 | % ground glass and condensation calculated by deep learning |
| Percentage of lung affected by ground glass opacity on scan | Day 16 | % calculated by deep learning |
| Percentage of lung affected by condensation on scan | Day 16 | % calculated by deep learning |
| Software operating time | End of study (August 2020) | Speed of image loading and image processing depending of brand of scanner |
| C-reactive protein levels | Admission Day 0 | mg/L |
| lactate dehydrogenase | Admission Day 0 | U/L |
| lymphocytemia | Admission Day 0 | g/L |
| D Dimers level | Admission Day 0 | µg/L |
| Time until onset of symptoms | Admission Day 0 | Days |
| Current or previous history of smoking | Admission Day 0 | Yes/no: |
| Age | Admission Day 0 | Years |
| BMI> 30 | Admission Day 0 | Yes/no: |
| Medical history of cardiovascular disease | Admission Day 0 | Yes/no: hypertension, coronary artery disease, congestive heart failure, cardiac arrhythmia |
| Diabetes | Admission Day 0 | Yes/no |
| Medical history of respiratory disease | Admission Day 0 | Yes/no: Chronic obstructive pulmonary disease, chronic respiratory failure |
| Medical history of immunosuppressed condition | Admission Day 0 | Yes/no: steroid use, pre-existing immunological condition, current chemotherapy for cancer |
| Calculate a prognostic score from clinical, biological and CT parameters | Day 8 | Deep learning algorithm |
| Calculate a prognostic score from clinical and biological parameters only | Day 8 | Deep learning algorithm |
| Compare receiver operating curves of prognostic scores with and without CT parameters | Day 8 | — |
| Time between RT-PCR positive results and first scan | Admission Day 0 | Hours |
| Vital status | Day 16 | Dead/alive |
| Length of hospitalization | Maximum 30 days | Days |
| rehospitalization | Day 30 | Yes/no |
| Duration of intubation | Day 30 | Days |
Countries
France, Martinique
Outcome results
None listed