1. Development and external validation of a prediction model for the transition from mild to moderate or severe form of COVID-19
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Zysman, Maéva, Asselineau, Julien, Saut, Olivier, Frison, Eric, Oranger, Mathilde, Maurac, Arnaud, Charriot, Jeremy, Achkir, Rkia, Regueme, Sophie, Klein, Emilie, Bommart, Sébastien, Bourdin, Arnaud, Dournes, Gael, Casteigt, Julien, Blum, Alain, Ferretti, Gilbert, Degano, Bruno, Berger, Patrick, Thiébaut, Rodolphe, Chabot, Francois, Laurent, Francois, Benlala, Ilyes, Centre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] (CRCTB), Université Bordeaux Segalen - Bordeaux 2-CHU Bordeaux [Bordeaux]-Institut National de la Santé et de la Recherche Médicale (INSERM), Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), CIC Bordeaux, Université Bordeaux Segalen - Bordeaux 2-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU Bordeaux [Bordeaux], Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Modélisation Mathématique pour l'Oncologie (MONC), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Statistics In System biology and Translational Medicine (SISTM), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)- Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Défaillance Cardiovasculaire Aiguë et Chronique (DCAC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Physiologie & médecine expérimentale du Cœur et des Muscles [U 1046] (PhyMedExp), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Hôpital Arnaud de Villeneuve [CHRU Montpellier], Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Chercheur indépendant, Université de Lorraine (UL), Université Grenoble Alpes (UGA), CHU Grenoble, and Université Joseph Fourier - Grenoble 1 (UJF)-CHU Grenoble
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Artificial intelligence ,[SDV.MHEP.ME]Life Sciences [q-bio]/Human health and pathology/Emerging diseases ,[SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases ,[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging ,COVID-19 ,Clinical decision rules ,[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie ,Tomography X-ray computed ,[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] - Abstract
International audience; 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.
- Published
- 2023