Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Our aim was to develop practical models built with simple clinical and radiological features to help diagnosing Coronavirus disease [COVID] in a real-life emergency cohort.
To do so, consecutive adult patients suspected of having COVID from 15 emergency departments from to were included as long as chest CT-scans and real-time polymerase chain reaction RT-PCR results were available [ 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.
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 patients, validated on an independent cohort of patients and compared with the OCTR performances 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. 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 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.
