There is a newer version of the record available.

Published 2023 | Version v1
Journal article

Development and external validation of a prediction model for the transition from mild to moderate or severe form of COVID-19

  • 1. Centre de Recherche Cardio-Thoracique de Bordeaux (U1045), Centre d'Investigation Clinique, INSERM, Bordeaux Population Health (U1219) CIC-P 1401, 33600, Pessac (France)
  • 2. University Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, 33600, Bordeaux (France)
  • 3. CHU Bordeaux, 33600, Pessac (France)
  • 4. MONC Team & SISTM Team, INRIA Bordeaux Sud-Ouest, 200 Av Vieille Tour, 33400, Talence (France)
  • 5. Instititut de Mathématiques de Bordeaux" (IMB), UMR5251, CNRS, University of Bordeaux, 351 Cours Libération, 33400, Talence (France)
  • 6. Faculté de Médecine de Nancy, Université de Lorraine, Institut National de La Santé Et de La Recherche Médicale (INSERM) Unité Médicale de Recherche (UMR), S 1116, Vandoeuvre-Lès-Nancy (France)
  • 7. Pôle Des Spécialités Médicales/Département de Pneumologie, Université de Lorraine, Centre Hospitalier Régional Universitaire (CHRU) Nancy, Service de Radiologie Et d'Imagerie, Nancy (France)
  • 8. PhyMedExp, University of Montpellier, INSERM U1046, CEDEX 5, 34295, Montpellier (France)
  • 9. Department of Respiratory Diseases, Arnaud de Villeneuve Hospital, Montpellier University Hospital, CEDEX 5, 34295, Montpellier (France)
  • 10. Pneumology Clinic, St Médard en Jalles (France)
  • 11. France Service de Radiologie Diagnostique Et Interventionnelle, Université Grenoble Alpes, CHU Grenoble-Alpes, Grenoble (France)

Description

COVID-19 pandemic seems to be under control. However, despite the vaccines, 5 to 10% of the patients with mild disease develop moderate to critical forms with potential lethal evolution. In addition to assess lung infection spread, chest CT helps to detect complications. Developing a prediction model to identify at-risk patients of worsening from mild COVID-19 combining simple clinical and biological parameters with qualitative or quantitative data using CT would be relevant to organizing optimal patient management. Four French hospitals were used for model training and internal validation. External validation was conducted in two independent hospitals. We used easy-to-obtain clinical (age, gender, smoking, symptoms' onset, cardiovascular comorbidities, diabetes, chronic respiratory diseases, immunosuppression) and biological parameters (lymphocytes, CRP) with qualitative or quantitative data (including radiomics) from the initial CT in mild COVID-19 patients. Qualitative CT scan with clinical and biological parameters can predict which patients with an initial mild presentation would develop a moderate to critical form of COVID-19, with a c-index of 0.70 (95% CI 0.63; 0.77). CT scan quantification improved the performance of the prediction up to 0.73 (95% CI 0.67; 0.79) and radiomics up to 0.77 (95% CI 0.71; 0.83). Results were similar in both validation cohorts, considering CT scans with or without injection. Adding CT scan quantification or radiomics to simple clinical and biological parameters can better predict which patients with an initial mild COVID-19 would worsen than qualitative analyses alone. This tool could help to the fair use of healthcare resources and to screen patients for potential new drugs to prevent a pejorative evolution of COVID-19. CT scan quantification or radiomics analysis is superior to qualitative analysis, when used with simple clinical and biological parameters, to determine which patients with an initial mild presentation of COVID-19 would worsen to a moderate to critical form.

Additional details

Identifiers

Publishing Information

Journal Title
European Radiology (Internet)
Journal Volume
33
Journal Issue
12
Journal Page Range
p. 9262-9274
ISSN
1432-1084
CODEN
EURAE3