Development and Validation of a Prediction Model for the Transition From Mild to Moderate Form of COVID-19, Using Data From Chest CT

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

COMPLETED

Total Enrollment

1329 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-08-31

Study Completion Date

2021-05-04

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

Only 5% of patients infected with COVID-19 develop severe or critical Coronavirus disease 2019 (COVID-19) and there is no reliable risk stratification tool for non-severe COVID-19 patients at admission.

Finding a way to predict which patients with an initial mild to moderate presentation of COVID-19 would develop severe or critical form of COVID-19 according to CT-scan data, simple clinical and biological parameters is challenging. In this multicentric study, the study aims to construct a predictive score for early identification of cases at high risk of progression to moderate, severe or critical COVID-19 combining simple clinical and biological parameters and qualitative, quantitative or artificial intelligence (AI) data from the initial CT from non-severe patients.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

Prediction of Clinical Course in COVID19 Patients

NCT04377685

COVID 19

COMPLETED

COVID-19. CT-Scan Modeling in COVID19 Sequelae

NCT04747769

COVID-19

UNKNOWN

Assessment of Lung Inflammation With FDG PET/CT in COVID-19

NCT04441489

COVID-19 FDG PET/CT Inflammation

COMPLETED

Low-Dose Chest CT (LDCT) COVID-19 Study

NCT05031845

Covid19

COMPLETED

CT Abnormalities Evocative of Lung Infection Are Associated With Lower 18F-FDG Uptake in Confirmed COVID-19 Patients

NCT04512118

Presence of COVID-19 Compatible CT Abnormalities

COMPLETED

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

A few numbers of patients infected with Coronavirus disease 2019 (COVID-19) rapidly develop acute respiratory distress leading to respiratory failure, with high short-term mortality rates. However, only 5% of patients infected with COVID-19 are concerned by this pejorative evolution. At present, there is no reliable risk stratification tool for non-severe COVID-19 patients at admission.

Chest computed tomography (CT) is widely used for the management of COVID-19 pneumonia because of its availability and quickness. The standard of reference for confirming COVID-19 relies on microbiological tests but these tests might not be available in an emergency setting and their results are not immediately available, contrary to CT. In addition to its role for early diagnosis, CT has a prognostic role through evaluating the extent of COVID-19 lung abnormalities.

Finding a way to predict which patients with an initial mild to moderate presentation of COVID-19 would develop severe or critical form of COVID-19 according to CT-scan data, simple clinical and biological parameters is challenging. In this multicentric study, the study aims to construct a predictive score for early identification of cases at high risk of progression to moderate, severe or critical COVID-19 combining simple clinical and biological parameters and qualitative, quantitative or artificial intelligence (AI) data from the initial CT from non-severe patients. The final objective is to organize optimal patient management in the appropriate health structure.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

COVID-19

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

* First chest CT, assessed for respiratory symptoms, without injection of contrast agent for respiratory symptoms, and whose results of the CT subjective visual analysis are compatible or typical of COVID-19
* biological diagnosis of COVID-19 (RT-PCR) or clinical suspicion (cough and / or dyspnea and / or fever and / or need to use oxygen therapy as part of routine care) at the time of the examination
* Authorization of the patient for the processing of his personal data, except CNIL exemption

Exclusion Criteria

* Patient with a moderate (oxygen between 3 and 5 L / min to achieve saturation greater than 97% and a respiratory rate \<25 / min without the need for invasive ventilation), severe form (oxygen therapy\> 5L / min to obtain a SpO2\> 97%) or critical form (need to resort to ventilation and / or orotracheal intubation) at the date of the first chest CT
* Age \< 18 years old
* Patient deprived of liberty by judicial decision

Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No