Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2025-12-25 @ 4:26 AM
Ignite Modification Date:
2025-12-25 @ 4:26 AM
NCT ID:
NCT04481620
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:
NCT04481620
Study Brief:
Protocol Section:
NCT04481620