Prediction of Clinical Course in COVID19 Patients

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.

Completed

Looking for future studies?

Notify Me

Key information

Conditions

COVID 19 COVID-19 Coronaviridae Infections Coronavirus Infections Infections Lung Diseases Nidovirales Infections Pneumonia Pneumonia, Viral RNA Virus Infections Respiratory Tract Diseases Respiratory Tract Infections Virus Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Chu Saint-Etienne

Saint-Etienne, 42100, France

About this study

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.

Who can participate

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

CT-scan

Other

Chest CT scan on admission to the hospital

Primary outcomes

diagnostic of COVID disease composite

Time frame: On admission to the hospital
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.

Sponsors and collaborators

Lead sponsor

Centre Hospitalier Universitaire de Saint Etienne

Other

Collaborators

Centre National pour le Recherche Scientifique (CNRS)
INSA Rennes
Institut National de la Santé Et de la Recherche Médicale, France
Jean Monnet University
Université de Lyon

Registry information

Official study 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

Acronym: COVID-CTPRED

Important dates

Study start: 2020
Primary completion: 2020
Study completion: 2020
First posted: May 6, 2020
Registry last updated: Nov 17, 2021

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed