Prediction of Clinical Course in COVID19 Patients (COVID-CTPRED)

Centre Hospitalier Universitaire de Saint Etienne

Status: Completed

Conditions: COVID 19

Treatments: Other: CT-Scan

Study type: Observational

Funder types: Other

Identifiers

NCT04377685

20CH109

IRBN652020/CHUSTE (Other Identifier)

Details and patient eligibility

About

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.

Full description

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.

Enrollment: 826 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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

Trial design

826 participants in 1 patient group

COVID19 patients

Description:

Patient tested positive for SARS-CoV-2 who had a CT scan

Treatment:

Other: CT-Scan