Development and external validation of a prediction model for the transition from mild to moderate or severe form of COVID-19
ZYSMAN, Maeva
Centre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
Bordeaux population health [BPH]
CIC Bordeaux
Centre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
Bordeaux population health [BPH]
CIC Bordeaux
SAUT, Olivier
Institut de Mathématiques de Bordeaux [IMB]
Modélisation Mathématique pour l'Oncologie [MONC]
Statistics In System biology and Translational Medicine [SISTM]
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Institut de Mathématiques de Bordeaux [IMB]
Modélisation Mathématique pour l'Oncologie [MONC]
Statistics In System biology and Translational Medicine [SISTM]
ZYSMAN, Maeva
Centre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
Bordeaux population health [BPH]
CIC Bordeaux
Centre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
Bordeaux population health [BPH]
CIC Bordeaux
SAUT, Olivier
Institut de Mathématiques de Bordeaux [IMB]
Modélisation Mathématique pour l'Oncologie [MONC]
Statistics In System biology and Translational Medicine [SISTM]
Institut de Mathématiques de Bordeaux [IMB]
Modélisation Mathématique pour l'Oncologie [MONC]
Statistics In System biology and Translational Medicine [SISTM]
ORANGER, Mathilde
Centre Hospitalier Régional Universitaire de Nancy [CHRU Nancy]
Défaillance Cardiovasculaire Aiguë et Chronique [DCAC]
Centre Hospitalier Régional Universitaire de Nancy [CHRU Nancy]
Défaillance Cardiovasculaire Aiguë et Chronique [DCAC]
MAURAC, Arnaud
Centre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
Bordeaux population health [BPH]
CIC Bordeaux
Centre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
Bordeaux population health [BPH]
CIC Bordeaux
CHARRIOT, Jérémy
Physiologie & médecine expérimentale du Cœur et des Muscles [U 1046] [PhyMedExp]
Hôpital Arnaud de Villeneuve [CHU Montpellier]
Physiologie & médecine expérimentale du Cœur et des Muscles [U 1046] [PhyMedExp]
Hôpital Arnaud de Villeneuve [CHU Montpellier]
BOMMART, Sébastien
Physiologie & médecine expérimentale du Cœur et des Muscles [U 1046] [PhyMedExp]
Hôpital Arnaud de Villeneuve [CHU Montpellier]
Physiologie & médecine expérimentale du Cœur et des Muscles [U 1046] [PhyMedExp]
Hôpital Arnaud de Villeneuve [CHU Montpellier]
BOURDIN, Arnaud
Physiologie & médecine expérimentale du Cœur et des Muscles [U 1046] [PhyMedExp]
Hôpital Arnaud de Villeneuve [CHU Montpellier]
Physiologie & médecine expérimentale du Cœur et des Muscles [U 1046] [PhyMedExp]
Hôpital Arnaud de Villeneuve [CHU Montpellier]
DOURNES, Gael
Bordeaux population health [BPH]
Centre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
CIC Bordeaux
Bordeaux population health [BPH]
Centre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
CIC Bordeaux
BLUM, Alain
Centre Hospitalier Régional Universitaire de Nancy [CHRU Nancy]
Université de Lorraine [UL]
Centre Hospitalier Régional Universitaire de Nancy [CHRU Nancy]
Université de Lorraine [UL]
FERRETTI, Gilbert
Université Grenoble Alpes [UGA]
Centre Hospitalier Universitaire [CHU Grenoble] [CHUGA]
Université Grenoble Alpes [UGA]
Centre Hospitalier Universitaire [CHU Grenoble] [CHUGA]
DEGANO, Bruno
Université Grenoble Alpes [UGA]
Centre Hospitalier Universitaire [CHU Grenoble] [CHUGA]
Université Grenoble Alpes [UGA]
Centre Hospitalier Universitaire [CHU Grenoble] [CHUGA]
THIÉBAUT, Rodolphe
Centre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
Bordeaux population health [BPH]
CIC Bordeaux
Modélisation Mathématique pour l'Oncologie [MONC]
Statistics In System biology and Translational Medicine [SISTM]
Centre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
Bordeaux population health [BPH]
CIC Bordeaux
Modélisation Mathématique pour l'Oncologie [MONC]
Statistics In System biology and Translational Medicine [SISTM]
CHABOT, Francois
Défaillance Cardiovasculaire Aiguë et Chronique [DCAC]
Centre Hospitalier Régional Universitaire de Nancy [CHRU Nancy]
Défaillance Cardiovasculaire Aiguë et Chronique [DCAC]
Centre Hospitalier Régional Universitaire de Nancy [CHRU Nancy]
BERGER, Patrick
Centre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
Bordeaux population health [BPH]
CIC Bordeaux
Centre Hospitalier Universitaire de Bordeaux [CHU Bordeaux]
Centre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
Bordeaux population health [BPH]
CIC Bordeaux
Centre Hospitalier Universitaire de Bordeaux [CHU Bordeaux]
LAURENT, Francois
Centre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
Bordeaux population health [BPH]
CIC Bordeaux
Centre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
Bordeaux population health [BPH]
CIC Bordeaux
BENLALA, Ilyes
Centre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
Bordeaux population health [BPH]
CIC Bordeaux
< Réduire
Centre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
Bordeaux population health [BPH]
CIC Bordeaux
Langue
en
Article de revue
Ce document a été publié dans
European Radiology. 2023-07-05, vol. 33, p. 9262–9274
Springer Verlag
Résumé en anglais
Objectives 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 ...Lire la suite >
Objectives 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.Methods 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.Results 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.Conclusion 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.Clinical Trial Registration NCT04481620. Clinical relevance statement 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.< Réduire
Mots clés en anglais
Tomography X-ray computed
Clinical decision rules
Artificial intelligence
COVID-19
Origine
Importé de halUnités de recherche