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

hal.structure.identifierCentre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
hal.structure.identifierBordeaux population health [BPH]
hal.structure.identifierCIC Bordeaux
dc.contributor.authorZYSMAN, Maéva
hal.structure.identifierCHU Bordeaux
dc.contributor.authorASSELINEAU, Julien
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
dc.contributor.authorSAUT, Olivier
hal.structure.identifierCHU Bordeaux
dc.contributor.authorFRISON, Eric
hal.structure.identifierCentre Hospitalier Régional Universitaire de Nancy [CHRU Nancy]
hal.structure.identifierDéfaillance Cardiovasculaire Aiguë et Chronique [DCAC]
dc.contributor.authorORANGER, Mathilde
hal.structure.identifierCentre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
hal.structure.identifierBordeaux population health [BPH]
hal.structure.identifierCIC Bordeaux
dc.contributor.authorMAURAC, Arnaud
hal.structure.identifierPhysiologie & médecine expérimentale du Cœur et des Muscles [U 1046] [PhyMedExp]
hal.structure.identifierHôpital Arnaud de Villeneuve [CHRU Montpellier]
dc.contributor.authorCHARRIOT, Jérémy
hal.structure.identifierCHU Bordeaux
dc.contributor.authorACHKIR, Rkia
hal.structure.identifierCHU Bordeaux
dc.contributor.authorREGUEME, Sophie
hal.structure.identifierCHU Bordeaux
dc.contributor.authorKLEIN, Emilie
hal.structure.identifierPhysiologie & médecine expérimentale du Cœur et des Muscles [U 1046] [PhyMedExp]
hal.structure.identifierHôpital Arnaud de Villeneuve [CHRU Montpellier]
dc.contributor.authorBOMMART, Sébastien
hal.structure.identifierPhysiologie & médecine expérimentale du Cœur et des Muscles [U 1046] [PhyMedExp]
hal.structure.identifierHôpital Arnaud de Villeneuve [CHRU Montpellier]
dc.contributor.authorBOURDIN, Arnaud
hal.structure.identifierBordeaux population health [BPH]
hal.structure.identifierCentre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
hal.structure.identifierCIC Bordeaux
dc.contributor.authorDOURNES, Gael
hal.structure.identifierChercheur indépendant
dc.contributor.authorCASTEIGT, Julien
hal.structure.identifierCentre Hospitalier Régional Universitaire de Nancy [CHRU Nancy]
hal.structure.identifierUniversité de Lorraine [UL]
dc.contributor.authorBLUM, Alain
hal.structure.identifierUniversité Grenoble Alpes [UGA]
hal.structure.identifierCentre Hospitalier Universitaire [CHU Grenoble] [CHUGA]
dc.contributor.authorFERRETTI, Gilbert
hal.structure.identifierUniversité Grenoble Alpes [UGA]
hal.structure.identifierCentre Hospitalier Universitaire [CHU Grenoble] [CHUGA]
dc.contributor.authorDEGANO, Bruno
hal.structure.identifierCentre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
hal.structure.identifierBordeaux population health [BPH]
hal.structure.identifierCIC Bordeaux
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
dc.contributor.authorTHIÉBAUT, Rodolphe
hal.structure.identifierDéfaillance Cardiovasculaire Aiguë et Chronique [DCAC]
hal.structure.identifierCentre Hospitalier Régional Universitaire de Nancy [CHRU Nancy]
dc.contributor.authorCHABOT, Francois
hal.structure.identifierCentre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
hal.structure.identifierBordeaux population health [BPH]
hal.structure.identifierCIC Bordeaux
hal.structure.identifierCHU Bordeaux
dc.contributor.authorBERGER, Patrick
hal.structure.identifierCentre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
hal.structure.identifierBordeaux population health [BPH]
hal.structure.identifierCIC Bordeaux
dc.contributor.authorLAURENT, Francois
hal.structure.identifierCentre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
hal.structure.identifierBordeaux population health [BPH]
hal.structure.identifierCIC Bordeaux
dc.contributor.authorBENLALA, Ilyes
dc.date.accessioned2024-04-04T02:33:43Z
dc.date.available2024-04-04T02:33:43Z
dc.date.issued2023-07-05
dc.identifier.issn0938-7994
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/190503
dc.description.abstractEnObjectives 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.
dc.language.isoen
dc.publisherSpringer Verlag
dc.subject.enTomography X-ray computed
dc.subject.enClinical decision rules
dc.subject.enArtificial intelligence
dc.subject.enCOVID-19
dc.title.enDevelopment and external validation of a prediction model for the transition from mild to moderate or severe form of COVID-19
dc.typeArticle de revue
dc.identifier.doi10.1007/s00330-023-09759-x
dc.subject.halSciences du Vivant [q-bio]/Médecine humaine et pathologie/Maladies infectieuses
dc.subject.halInformatique [cs]/Intelligence artificielle [cs.AI]
dc.subject.halSciences du Vivant [q-bio]/Ingénierie biomédicale/Imagerie
dc.subject.halSciences du Vivant [q-bio]/Médecine humaine et pathologie/Maladies émergentes
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologie
bordeaux.journalEuropean Radiology
bordeaux.page9262–9274
bordeaux.volume33
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-04153209
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04153209v1
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