Afficher la notice abrégée

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

dc.rights.licenseopenen_US
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorZYSMAN, Maeva
dc.contributor.authorASSELINEAU, Julien
dc.contributor.authorSAUT, Olivier
dc.contributor.authorFRISON, Eric
dc.contributor.authorORANGER, Mathilde
dc.contributor.authorMAURAC, Arnaud
dc.contributor.authorCHARRIOT, Jeremy
dc.contributor.authorACHKIR, Rkia
dc.contributor.authorREGUEME, Sophie
dc.contributor.authorKLEIN, Emilie
dc.contributor.authorBOMMART, Sébastien
dc.contributor.authorBOURDIN, Arnaud
dc.contributor.authorDOURNES, Gael
dc.contributor.authorCASTEIGT, Julien
dc.contributor.authorBLUM, Alain
dc.contributor.authorFERRETTI, Gilbert
dc.contributor.authorDEGANO, Bruno
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorTHIEBAUT, Rodolphe
dc.contributor.authorCHABOT, Francois
dc.contributor.authorBERGER, Patrick
dc.contributor.authorLAURENT, Francois
dc.contributor.authorBENLALA, Ilyes
dc.date.accessioned2023-10-17T09:38:17Z
dc.date.available2023-10-17T09:38:17Z
dc.date.issued2023-07-05
dc.identifier.issn1432-1084en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/184453
dc.description.abstractEnCOVID-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. NCT04481620. 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. • Qualitative CT scan analyses with simple clinical and biological parameters can predict which patients with an initial mild COVID-19 and respiratory symptoms would worsen with a c-index of 0.70. • Adding CT scan quantification improves the performance of the clinical prediction model to an AUC of 0.73. • Radiomics analyses slightly improve the performance of the model to a c-index of 0.77.
dc.language.isoENen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subject.enArtificial intelligence
dc.subject.enCOVID-19
dc.subject.enClinical decision rules
dc.subject.enTomography
dc.subject.enX-ray computed
dc.title.enDevelopment and external validation of a prediction model for the transition from mild to moderate or severe form of COVID-19
dc.title.alternativeEur Radiolen_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1007/s00330-023-09759-xen_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed37405504en_US
bordeaux.journalEuropean Radiologyen_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.institutionINRIAen_US
bordeaux.teamSISTMen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.import.sourcepubmed
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exportfalse
workflow.import.sourcepubmed
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=European%20Radiology&rft.date=2023-07-05&rft.eissn=1432-1084&rft.issn=1432-1084&rft.au=ZYSMAN,%20Maeva&ASSELINEAU,%20Julien&SAUT,%20Olivier&FRISON,%20Eric&ORANGER,%20Mathilde&rft.genre=article


Fichier(s) constituant ce document

Thumbnail
Nom:
BPH_EurRadiol_2023_Zysman.pdf
Taille:
1.816Mo
Format:
PDF
Voir/Ouvrir
Thumbnail
Nom:
license_rdf
Taille:
914octets
Format:
application/rdf+xml
Voir/Ouvrir

Ce document figure dans la(les) collection(s) suivante(s)

Afficher la notice abrégée