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

Completed

• Conditions: COVID-19

• Registration Number: NCT04481620
• Lead Sponsor: University Hospital, Bordeaux

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.

Study Timeline

• Study First Submitted: 2020-07-20
• Study First Submitted QC: 2020-07-21
• Study First Posted: 2020-07-22
• Study Start: 2020-08-31
• Primary Completion: 2021-05-04
• Study Completion: 2021-05-04
• Status Verified: 2022-04
• Last Update Submitted: 2022-04-11
• Last Update Posted: 2022-04-12

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 1329

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
occurrence of significant clinical degradation	Day 30 following the initial chest CT	The primary outcome is defined by the occurrence of significant clinical degradation within 30 days following the initial chest CT.; Significant clinical degradation is defined by the transition from the mild to the moderate form of COVID-19, i.e., according to the WHO criteria, the requirement of 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.

Secondary Outcome Measures

Name	Time	Method
average length of stay in hospital	Month 1	the average length of stay in hospital (days)
mortality	Day 30 following the initial chest CT	mortality within 30 days following the initial chest CT (binary: yes/no)
occurrence of an Acute Respiratory Distress Syndrom	Day 30 following the initial chest CT	the occurrence of an Acute Respiratory Distress Syndrom according to the Berlin criteria (JAMA 2012) within 30 days following the initial chest CT (binary: yes/no)
occurrence of a severe form	Day 30 following the initial chest CT	the occurrence of a severe form, defined by the need for oxygen therapy greater than 5L / min to obtain a percutaneous oxygen saturation greater than 97%, within 30 days following the initial chest CT
occurrence of an orotracheal intubation	Day 30 following the initial chest CT	the occurrence of an orotracheal intubation within 30 days following the initial chest CT (binary: yes/no)
evolution of the imaging parameters	Day 30 following the initial chest CT	evolution of the imaging parameters of the successive thoracic CT scans in the acute phase of COVID-19, in patients with a positive diagnosis of COVID-19 (positive RT-PCR or positive serology)

Trial Locations

Locations (7)

CHU Bordeaux; 🇫🇷 Bordeaux, France

Clinique Bordeaux Nord; 🇫🇷 Bordeaux, France

Clinique Saint Augustin; 🇫🇷 Bordeaux, France

CHU de Grenoble Alpes; 🇫🇷 Grenoble, France

Hôpital Arnaud-de-Villeneuve CHU de Montpellier; 🇫🇷 Montpellier, France

Hôpitaux de Brabois CHU de Nancy; 🇫🇷 Nancy, France

Hôpital de la Milétrie CHU de Poitiers; 🇫🇷 Poitiers, France