Development and external validation of a prediction model for the transition from mild to moderate or severe form of COVID-19
| dc.rights.license | open | en_US |
| hal.structure.identifier | Statistics In System biology and Translational Medicine [SISTM] | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | ZYSMAN, Maeva | |
| dc.contributor.author | ASSELINEAU, Julien | |
| dc.contributor.author | SAUT, Olivier
IDREF: 076744531 | |
| dc.contributor.author | FRISON, Eric | |
| dc.contributor.author | ORANGER, Mathilde | |
| dc.contributor.author | MAURAC, Arnaud | |
| dc.contributor.author | CHARRIOT, Jeremy | |
| dc.contributor.author | ACHKIR, Rkia | |
| dc.contributor.author | REGUEME, Sophie | |
| dc.contributor.author | KLEIN, Emilie | |
| dc.contributor.author | BOMMART, Sébastien | |
| dc.contributor.author | BOURDIN, Arnaud | |
| dc.contributor.author | DOURNES, Gael | |
| dc.contributor.author | CASTEIGT, Julien | |
| dc.contributor.author | BLUM, Alain | |
| dc.contributor.author | FERRETTI, Gilbert | |
| dc.contributor.author | DEGANO, Bruno | |
| hal.structure.identifier | Statistics In System biology and Translational Medicine [SISTM] | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | THIEBAUT, Rodolphe | |
| dc.contributor.author | CHABOT, Francois | |
| dc.contributor.author | BERGER, Patrick
ORCID: 0000-0003-4702-0343 IDREF: 060717998 | |
| dc.contributor.author | LAURENT, Francois | |
| dc.contributor.author | BENLALA, Ilyes | |
| dc.date.accessioned | 2023-10-17T09:38:17Z | |
| dc.date.available | 2023-10-17T09:38:17Z | |
| dc.date.issued | 2023-07-05 | |
| dc.identifier.issn | 1432-1084 | en_US |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/184453 | |
| dc.description.abstractEn | 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. 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.iso | EN | en_US |
| dc.rights | Attribution 3.0 United States | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | * |
| dc.subject.en | Artificial intelligence | |
| dc.subject.en | COVID-19 | |
| dc.subject.en | Clinical decision rules | |
| dc.subject.en | Tomography | |
| dc.subject.en | X-ray computed | |
| dc.title.en | Development and external validation of a prediction model for the transition from mild to moderate or severe form of COVID-19 | |
| dc.title.alternative | Eur Radiol | en_US |
| dc.type | Article de revue | en_US |
| dc.identifier.doi | 10.1007/s00330-023-09759-x | en_US |
| dc.subject.hal | Sciences du Vivant [q-bio]/Santé publique et épidémiologie | en_US |
| dc.identifier.pubmed | 37405504 | en_US |
| bordeaux.journal | European Radiology | en_US |
| bordeaux.hal.laboratories | Bordeaux Population Health Research Center (BPH) - UMR 1219 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.institution | INSERM | en_US |
| bordeaux.institution | INRIA | en_US |
| bordeaux.team | SISTM | en_US |
| bordeaux.peerReviewed | oui | en_US |
| bordeaux.inpress | non | en_US |
| bordeaux.import.source | pubmed | |
| hal.popular | non | en_US |
| hal.audience | Internationale | en_US |
| hal.export | false | |
| workflow.import.source | pubmed | |
| dc.rights.cc | Pas de Licence CC | en_US |
| bordeaux.COinS | ctx_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 |
