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

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

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.

Study Overview

• Status: Completed
• Conditions: COVID 19
• Intervention / Treatment: Other: CT-Scan

Detailed 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.

• Study Type: Observational
• Enrollment (Actual): 826

Contacts and Locations

Study Locations

• France
  • Saint-Étienne, France, 42100
    • CHU SAINT-ETIENNE

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: N/A
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

patient admitted to the emergency room of COVID-19 confirmed by RT-PCR

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
COVID19 patients; Patient tested positive for SARS-CoV-2 who had a CT scan	Other: CT-Scan; Chest CT scan on admission to the hospital

What is the study measuring?

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

Collaborators and Investigators

• Sponsor: Centre Hospitalier Universitaire de Saint Etienne
• Collaborators: Institut National de la Santé Et de la Recherche Médicale, France; INSA Rennes; Centre National pour le Recherche Scientifique (CNRS); Université de Lyon; Université Jean Monnet
• Investigators: Principal Investigator: Pierre CROISILLE, PhD, CHU SAINT-ETIENNE

Study record dates

Study Major Dates

• Study Start (Actual): March 1, 2020
• Primary Completion (Actual): November 28, 2020
• Study Completion (Actual): December 26, 2020

Study Registration Dates

• First Submitted: May 5, 2020
• First Submitted That Met QC Criteria: May 5, 2020
• First Posted (Actual): May 6, 2020

Study Record Updates

• Last Update Posted (Actual): November 17, 2021
• Last Update Submitted That Met QC Criteria: November 16, 2021
• Last Verified: November 1, 2021

More Information

Terms related to this study

• Keywords: COVID 19; chest CT scan
• Additional Relevant MeSH Terms: Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; RNA Virus Infections; Virus Diseases; Infections; Respiratory Tract Infections; Respiratory Tract Diseases; Pneumonia, Viral; Pneumonia; Lung Diseases; COVID-19
• Other Study ID Numbers: 20CH109; IRBN652020/CHUSTE (Other Identifier: Comité d'éthique du CHU de Saint-Etienne)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No