Practical clinical and radiological models to diagnose COVID-19 based on a multicentric teleradiological emergency chest CT cohort.

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.

Response to the COVID-19 Pandemic: Practical Guide to Rapidly Deploying Home Workstations to Guarantee Radiology Services During Quarantine, Social Distancing, and Stay Home Orders.

OBJECTIVE. The purpose of this article is to share an experience in the rapid deployment of home workstations that illustrates a creative solution that transcended typical administrative barriers. CONCLUSION. In response to the global coronavirus disease (COVID-19) pandemic, radiology departments need to rapidly deploy home PACS workstations to facilitate physical distancing and to guarantee radiologic expertise despite possible home quarantining or stay home, work safe orders.

Remote reporting in the COVID-19 era: from pilot study to practice.

AIM: To assess the benefits and challenges of remote reporting using an intra-departmental teleradiology system. MATERIALS AND METHODS: A pilot of an in-hospital Trust radiologist reporting on in-hospital Trust patients via a remote login was undertaken. Reporting output, training impact, and quality improvement were measured. RESULTS: Reporting output increased by 140%. Trainee satisfaction was high in a qualitative survey, particularly for out-of-hours support and teaching. Clinicians found the service to be similar to the same service provided by a locally based radiologist. CONCLUSION: In the COVID-19 era, remote working has developed rapidly. This study shows that radiology departments can provide remote reporting that is equal in standard to reporting from within the hospital, and in addition, that there are advantages to output and training.