Clinical trial NCT04481620: Development and Validation of a Prediction Model for the Transition From Mild to Moderate Form of COVID-19, Using Data From Chest CT
Official title: Evelopment and Validation of a Prediction Model for the Transition From Mild to Moderate Form of COVID-19, Using Data From Chest CT
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: COVID-19
Keywords: COVID-19
SARS-CoV-2
CT
prediction score
prognosis
mild-to moderate form
Lead sponsor: University Hospital, Bordeaux
Sponsor class: OTHER
Collaborators: name: Programme Hospitalier de Recherche Clinique Inter-Régionale (PHRC-I)
First posted: 2020-07-22
Enrollment: 1329
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients with suspicion of Covid-19

Brief summary: 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.

Detailed description: 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.

Eligibility criteria: 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

Primary outcomes:
measure: occurrence of significant clinical degradation
timeFrame: Day 30 following the initial chest CT
Secondary outcomes:
measure: occurrence of a severe form
timeFrame: Day 30 following the initial chest CT
measure: occurrence of an orotracheal intubation
timeFrame: Day 30 following the initial chest CT
measure: occurrence of an Acute Respiratory Distress Syndrom
timeFrame: Day 30 following the initial chest CT
measure: average length of stay in hospital
timeFrame: Month 1
measure: mortality
timeFrame: Day 30 following the initial chest CT
measure: evolution of the imaging parameters
timeFrame: Day 30 following the initial chest CT
Other outcomes:
Locations:
city: Bordeaux
country: France
facility: CHU Bordeaux
city: Bordeaux
country: France
facility: Clinique Bordeaux Nord
city: Bordeaux
country: France
facility: Clinique Saint Augustin
city: Grenoble
country: France
facility: CHU de Grenoble Alpes
city: Montpellier
country: France
facility: Hôpital Arnaud-de-Villeneuve CHU de Montpellier
city: Nancy
country: France
facility: Hôpitaux de Brabois CHU de Nancy
city: Poitiers
country: France
facility: Hôpital de la Milétrie CHU de Poitiers
Study officials:
References: