Practical clinical and radiological models to diagnose COVID-19 based on a multicentric teleradiological emergency chest CT cohort
Abstract Our aim was to develop practical models built with simple clinical and radiological features to help diagnosing Coronavirus disease 2019 [COVID-19] in a real-life emergency cohort. To do so, 513 consecutive adult patients suspected of having COVID-19 from 15 emergency departments from 2020-...
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2021
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oai:doaj.org-article:db4dd3af5915405f9d3d6815066a09552021-12-02T17:15:33ZPractical clinical and radiological models to diagnose COVID-19 based on a multicentric teleradiological emergency chest CT cohort10.1038/s41598-021-88053-62045-2322https://doaj.org/article/db4dd3af5915405f9d3d6815066a09552021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88053-6https://doaj.org/toc/2045-2322Abstract Our aim was to develop practical models built with simple clinical and radiological features to help diagnosing Coronavirus disease 2019 [COVID-19] in a real-life emergency cohort. To do so, 513 consecutive adult patients suspected of having COVID-19 from 15 emergency departments from 2020-03-13 to 2020-04-14 were included as long as chest CT-scans and real-time polymerase chain reaction (RT-PCR) results were available (244 [47.6%] with a positive RT-PCR). Immediately after their acquisition, the chest CTs were prospectively interpreted by on-call teleradiologists (OCTRs) and systematically reviewed within one week by another senior teleradiologist. Each OCTR reading was concluded using a 5-point scale: normal, non-infectious, infectious non-COVID-19, indeterminate and highly suspicious of COVID-19. The senior reading reported the lesions’ semiology, distribution, extent and differential diagnoses. After pre-filtering clinical and radiological features through univariate Chi-2, Fisher or Student t-tests (as appropriate), multivariate stepwise logistic regression (Step-LR) and classification tree (CART) models to predict a positive RT-PCR were trained on 412 patients, validated on an independent cohort of 101 patients and compared with the OCTR performances (295 and 71 with available clinical data, respectively) through area under the receiver operating characteristics curves (AUC). Regarding models elaborated on radiological variables alone, best performances were reached with the CART model (i.e., AUC = 0.92 [versus 0.88 for OCTR], sensitivity = 0.77, specificity = 0.94) while step-LR provided the highest AUC with clinical-radiological variables (AUC = 0.93 [versus 0.86 for OCTR], sensitivity = 0.82, specificity = 0.91). Hence, these two simple models, depending on the availability of clinical data, provided high performances to diagnose positive RT-PCR and could be used by any radiologist to support, modulate and communicate their conclusion in case of COVID-19 suspicion. Practically, using clinical and radiological variables (GGO, fever, presence of fibrotic bands, presence of diffuse lesions, predominant peripheral distribution) can accurately predict RT-PCR status.Paul SchusterAmandine CrombéHubert NivetAlice BergerLaurent PourriolNicolas FavardAlban ChazotFlorian Alonzo-LacroixEmile YoussofAlexandre Ben CheikhJulien BaliqueBasile PortaFrançois PetitpierreGrégoire BouquetCharles MastierFlavie BratanJean-François BergerotVivien ThomsonNathan BanasteGuillaume GorincourNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021) |
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Medicine R Science Q Paul Schuster Amandine Crombé Hubert Nivet Alice Berger Laurent Pourriol Nicolas Favard Alban Chazot Florian Alonzo-Lacroix Emile Youssof Alexandre Ben Cheikh Julien Balique Basile Porta François Petitpierre Grégoire Bouquet Charles Mastier Flavie Bratan Jean-François Bergerot Vivien Thomson Nathan Banaste Guillaume Gorincour Practical clinical and radiological models to diagnose COVID-19 based on a multicentric teleradiological emergency chest CT cohort |
| description |
Abstract Our aim was to develop practical models built with simple clinical and radiological features to help diagnosing Coronavirus disease 2019 [COVID-19] in a real-life emergency cohort. To do so, 513 consecutive adult patients suspected of having COVID-19 from 15 emergency departments from 2020-03-13 to 2020-04-14 were included as long as chest CT-scans and real-time polymerase chain reaction (RT-PCR) results were available (244 [47.6%] with a positive RT-PCR). Immediately after their acquisition, the chest CTs were prospectively interpreted by on-call teleradiologists (OCTRs) and systematically reviewed within one week by another senior teleradiologist. Each OCTR reading was concluded using a 5-point scale: normal, non-infectious, infectious non-COVID-19, indeterminate and highly suspicious of COVID-19. The senior reading reported the lesions’ semiology, distribution, extent and differential diagnoses. After pre-filtering clinical and radiological features through univariate Chi-2, Fisher or Student t-tests (as appropriate), multivariate stepwise logistic regression (Step-LR) and classification tree (CART) models to predict a positive RT-PCR were trained on 412 patients, validated on an independent cohort of 101 patients and compared with the OCTR performances (295 and 71 with available clinical data, respectively) through area under the receiver operating characteristics curves (AUC). Regarding models elaborated on radiological variables alone, best performances were reached with the CART model (i.e., AUC = 0.92 [versus 0.88 for OCTR], sensitivity = 0.77, specificity = 0.94) while step-LR provided the highest AUC with clinical-radiological variables (AUC = 0.93 [versus 0.86 for OCTR], sensitivity = 0.82, specificity = 0.91). Hence, these two simple models, depending on the availability of clinical data, provided high performances to diagnose positive RT-PCR and could be used by any radiologist to support, modulate and communicate their conclusion in case of COVID-19 suspicion. Practically, using clinical and radiological variables (GGO, fever, presence of fibrotic bands, presence of diffuse lesions, predominant peripheral distribution) can accurately predict RT-PCR status. |
| format |
article |
| author |
Paul Schuster Amandine Crombé Hubert Nivet Alice Berger Laurent Pourriol Nicolas Favard Alban Chazot Florian Alonzo-Lacroix Emile Youssof Alexandre Ben Cheikh Julien Balique Basile Porta François Petitpierre Grégoire Bouquet Charles Mastier Flavie Bratan Jean-François Bergerot Vivien Thomson Nathan Banaste Guillaume Gorincour |
| author_facet |
Paul Schuster Amandine Crombé Hubert Nivet Alice Berger Laurent Pourriol Nicolas Favard Alban Chazot Florian Alonzo-Lacroix Emile Youssof Alexandre Ben Cheikh Julien Balique Basile Porta François Petitpierre Grégoire Bouquet Charles Mastier Flavie Bratan Jean-François Bergerot Vivien Thomson Nathan Banaste Guillaume Gorincour |
| author_sort |
Paul Schuster |
| title |
Practical clinical and radiological models to diagnose COVID-19 based on a multicentric teleradiological emergency chest CT cohort |
| title_short |
Practical clinical and radiological models to diagnose COVID-19 based on a multicentric teleradiological emergency chest CT cohort |
| title_full |
Practical clinical and radiological models to diagnose COVID-19 based on a multicentric teleradiological emergency chest CT cohort |
| title_fullStr |
Practical clinical and radiological models to diagnose COVID-19 based on a multicentric teleradiological emergency chest CT cohort |
| title_full_unstemmed |
Practical clinical and radiological models to diagnose COVID-19 based on a multicentric teleradiological emergency chest CT cohort |
| title_sort |
practical clinical and radiological models to diagnose covid-19 based on a multicentric teleradiological emergency chest ct cohort |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/db4dd3af5915405f9d3d6815066a0955 |
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