Development and external validation of a prediction model for the transition from mild to moderate or severe form of COVID-19
Creators
- Zysman, Maéva1, 2, 3
- Maurac, Arnaud1, 2, 3
- Dournes, Gael1, 2, 3
- Berger, Patrick1, 2, 3
- Laurent, Francois1, 2, 3
- Benlala, Ilyes1, 2, 3
- Asselineau, Julien3
- Frison, Eric3
- Achkir, Rkia3
- Regueme, Sophie3
- Klein, Emilie3
- Saut, Olivier4, 5
- Oranger, Mathilde6, 7
- Chabot, Francois6, 7
- Charriot, Jeremy8, 9
- Bommart, Sébastien8, 9
- Bourdin, Arnaud8, 9
- Casteigt, Julien10
- Blum, Alain7
- Ferretti, Gilbert11
- Degano, Bruno11
- Thiébaut, Rodolphe4, 1, 2, 3
- 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
INIS
- Country of Publication
- Germany
- Country of Input or Organization
- Germany
- INIS RN
- 55019755
- Subject category
- S62: RADIOLOGY AND NUCLEAR MEDICINE;
- Descriptors DEI
- AGE DEPENDENCE; ARTIFICIAL INTELLIGENCE; CHEST; CLASSIFICATION; COMPUTERIZED TOMOGRAPHY; CORONAVIRUSES; DISEASE INCIDENCE; DISEASES; FRANCE; HOSPITALS; IMMUNOSUPPRESSION; LUNGS; LYMPHOCYTES; RADIOMICS; SEX DEPENDENCE; SYMPTOMS; TOBACCO SMOKES; TRAINING; VACCINES; VALIDATION
- Descriptors DEC
- AEROSOLS; ANIMAL CELLS; BIOLOGICAL MATERIALS; BLOOD; BLOOD CELLS; BODY; BODY FLUIDS; BUILDINGS; COLLOIDS; CONNECTIVE TISSUE CELLS; DEVELOPED COUNTRIES; DIAGNOSTIC TECHNIQUES; DISEASES; DISPERSIONS; EDUCATION; EUROPE; INFECTIOUS DISEASES; LEUKOCYTES; MATERIALS; MEDICAL ESTABLISHMENTS; MEDICINE; MICROORGANISMS; NUCLEAR MEDICINE; ORGANS; PARASITES; RADIOLOGY; RESIDUES; RESPIRATORY SYSTEM; SMOKES; SOLS; SOMATIC CELLS; TESTING; TOMOGRAPHY; VIRAL DISEASES; VIRUSES; WESTERN EUROPE; ZOONOTIC DISEASES