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| ID | Type | Description | Link |
|---|---|---|---|
| IRBN652020/CHUSTE | Other Identifier | Comité d'éthique du CHU de Saint-Etienne |
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| Name | Class |
|---|---|
| INSA Rennes | OTHER |
| Institut National de la Santé Et de la Recherche Médicale, France | OTHER_GOV |
| Centre National pour le Recherche Scientifique (CNRS) | UNKNOWN |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| COVID19 patients | Patient tested positive for SARS-CoV-2 who had a CT scan |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| CT-Scan | Other | Chest CT scan on admission to the hospital |
|
| Measure | Description | Time Frame |
|---|---|---|
| diagnostic of COVID disease composite | The diagnostoc of COVID disease is composite of:
Analysis of CT-Scan results. | On admission to the hospital |
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Inclusion Criteria:
Exclusion Criteria:
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patient admitted to the emergency room of COVID-19 confirmed by RT-PCR
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| Name | Affiliation | Role |
|---|---|---|
| Pierre CROISILLE, PhD | Centre Hospitalier Universitaire de Saint Etienne | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Chu Saint-Etienne | Saint-Etienne | 42100 | France |
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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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| Université de Lyon |
| UNKNOWN |
| Jean Monnet University | OTHER |
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| D014777 |
| Virus Diseases |
| D018352 | Coronavirus Infections |
| D003333 | Coronaviridae Infections |
| D030341 | Nidovirales Infections |
| D012327 | RNA Virus Infections |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |