Prediction of Clinical Course in COVID19 Patients

Study Overview
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Brief Summary
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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
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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.

Official Title
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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

Conditions
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COVID 19

Intervention / Treatment
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* Other: CT-Scan


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: age ≥ 18 years clinical suspicion of COVID-19 confirmed by RT-PCR CT scan at ER admission RT-PCR sampling Exclusion Criteria: CT scan failure or loss of CT data RT-PCR initial results unavailable

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| COVID19 patients<br>Patient tested positive for SARS-CoV-2 who had a CT scan | Other: CT-Scan<br>* Chest CT scan on admission to the hospital<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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. | On admission to the hospital | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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COVID 19, chest CT scan