Prediction of Clinical Course in COVID19 Patients

COVID 19 Clinical Trial

— COVID-CTPRED  

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04377685
• Other study ID #: 20CH109
• Secondary ID: IRBN652020/CHUST
• Status: Completed
• Start date: March 1, 2020
• Est. completion date: December 26, 2020

Study information

• Verified date: November 2021
• Source: Centre Hospitalier Universitaire de Saint Etienne
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 826
• Est. completion date: December 26, 2020
• Est. primary completion date: November 28, 2020
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• COVID 19
• COVID-19

Intervention

Other:
• CT-Scan
Chest CT scan on admission to the hospital

Locations

Country	Name	City	State
France	Chu Saint-Etienne	Saint-Étienne

Sponsors (6)

Lead Sponsor	Collaborator
Centre Hospitalier Universitaire de Saint Etienne	Centre National pour le Recherche Scientifique (CNRS), Institut National de la Santé Et de la Recherche Médicale, France, Institut National des Sciences Appliquées (INSA), Université de Lyon, Université Jean Monnet

Country where clinical trial is conducted

France, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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