CT Biomarkers Identification by Artificial Intelligence for COVID-19 Prognosis

Study Overview
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Brief Summary
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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.

Official Title
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Identification of Thoracic CT Scan Biomarkers by Deep Learning for Evaluating the Prognosis of Patients With COVID-19 Disease

Conditions
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Covid-19

Intervention / Treatment
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* Diagnostic Test: Imaging by thoracic scanner


Participation Criteria
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Eligibility Criteria
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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
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Patients positive for SARS-CoV-2<br> | Diagnostic Test: Imaging by thoracic scanner<br>* Low-dose computed tomography<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | 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 | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Vital status | Dead/alive | Day 16 | 
| Vital status | Dead/alive | Day 30 | 
| 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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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low dose CT scan, biomarkers, artificial intelligence