Spectrum of Vascular Involvement in Coronavirus Disease 2019 Pneumonia-Findings on CT Perfusion.

OBJECTIVES: There is accumulating evidence of a distinct coagulopathy in severe acute respiratory syndrome coronavirus 2 infection which is associated with poor prognosis in coronavirus disease 2019. Coagulation abnormalities in blood samples resemble systemic coagulopathies in other severe infections but demonstrate specific features such as a very high d-dimer. These clinical observations are consistent with histopathologic findings of locally disturbed pulmonary microvascular thrombosis and angiopathy in end-stage coronavirus disease 2019. However, exact underlying processes and the sequence of events are not fully understood. DATA SOURCES: CT perfusion may provide insight in the dynamic aspect of the vascularity in pulmonary lesions in coronavirus disease 2019 infection as, in contrast to dual energy CT, a multiphase perfusion pattern is displayed. STUDY SELECTION: In six patients with coronavirus disease 2019 pneumonia, findings on additional CT perfusion series were correlated with known histopathologic vascular patterns upon pulmonary autopsy of patients who had died of coronavirus disease 2019. DATA EXTRACTION: In this case series, we were able to show perfusion changes on CT scans in typical pulmonary lesions illustrating diverse patterns. DATA SYNTHESIS: We demonstrated hyperperfusion in areas with ground glass and a severely decreased perfusion pattern in more consolidated areas often seen later in the course of disease. A combination was also observed, illustrating temporal heterogeneity. CONCLUSIONS: These findings provide new insights into the pathophysiology of coronavirus disease 2019 pneumonia and further understanding of the mechanisms that lead to respiratory failure in these patients.

Chest CT in the Emergency Department for Diagnosis of COVID-19 Pneumonia: Dutch Experience.

Background Clinicians need to rapidly and reliably diagnose coronavirus disease 2019 (COVID-19) for proper risk stratification, isolation strategies, and treatment decisions. Purpose To assess the real-life performance of radiologist emergency department chest CT interpretation for diagnosing COVID-19 during the acute phase of the pandemic, using the COVID-19 Reporting and Data System (CO-RADS). Materials and Methods This retrospective multicenter study included consecutive patients who presented to emergency departments in six medical centers between March and April 2020 with moderate to severe upper respiratory symptoms suspicious for COVID-19. As part of clinical practice, chest CT scans were obtained for primary work-up and scored using the five-point CO-RADS scheme for suspicion of COVID-19. CT was compared with severe acute respiratory syndrome coronavirus 2 reverse-transcription polymerase chain reaction (RT-PCR) assay and a clinical reference standard established by a multidisciplinary group of clinicians based on RT-PCR, COVID-19 contact history, oxygen therapy, timing of RT-PCR testing, and likely alternative diagnosis. Performance of CT was estimated using area under the receiver operating characteristic curve (AUC) analysis and diagnostic odds ratios against both reference standards. Subgroup analysis was performed on the basis of symptom duration grouped presentations of less than 48 hours, 48 hours through 7 days, and more than 7 days. Results A total of 1070 patients (median age, 66 years; interquartile range, 54-75 years; 626 men) were included, of whom 536 (50%) had a positive RT-PCR result and 137 (13%) of whom were considered to have a possible or probable COVID-19 diagnosis based on the clinical reference standard. Chest CT yielded an AUC of 0.87 (95% CI: 0.84, 0.89) compared with RT-PCR and 0.87 (95% CI: 0.85, 0.89) compared with the clinical reference standard. A CO-RADS score of 4 or greater yielded an odds ratio of 25.9 (95% CI: 18.7, 35.9) for a COVID-19 diagnosis with RT-PCR and an odds ratio of 30.6 (95% CI: 21.1, 44.4) with the clinical reference standard. For symptom duration of less than 48 hours, the AUC fell to 0.71 (95% CI: 0.62, 0.80; P < .001). Conclusion Chest CT analysis using the coronavirus disease 2019 (COVID-19) Reporting and Data System enables rapid and reliable diagnosis of COVID-19, particularly when symptom duration is greater than 48 hours. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Elicker in this issue.

CO-RADS: A Categorical CT Assessment Scheme for Patients Suspected of Having COVID-19-Definition and Evaluation.

Background A categorical CT assessment scheme for suspicion of pulmonary involvement of coronavirus disease 2019 (COVID-19 provides a basis for gathering scientific evidence and improved communication with referring physicians. Purpose To introduce the COVID-19 Reporting and Data System (CO-RADS) for use in the standardized assessment of pulmonary involvement of COVID-19 on unenhanced chest CT images and to report its initial interobserver agreement and performance. Materials and Methods The Dutch Radiological Society developed CO-RADS based on other efforts for standardization, such as the Lung Imaging Reporting and Data System or Breast Imaging Reporting and Data System. CO-RADS assesses the suspicion for pulmonary involvement of COVID-19 on a scale from 1 (very low) to 5 (very high). The system is meant to be used in patients with moderate to severe symptoms of COVID-19. The system was evaluated by using 105 chest CT scans of patients admitted to the hospital with clinical suspicion of COVID-19 and in whom reverse transcription-polymerase chain reaction (RT-PCR) was performed (mean, 62 years ± 16 [standard deviation]; 61 men, 53 with positive RT-PCR results). Eight observers used CO-RADS to assess the scans. Fleiss κ value was calculated, and scores of individual observers were compared with the median of the remaining seven observers. The resulting area under the receiver operating characteristics curve (AUC) was compared with results from RT-PCR and clinical diagnosis of COVID-19. Results There was absolute agreement among observers in 573 (68.2%) of 840 observations. Fleiss κ value was 0.47 (95% confidence interval [CI]: 0.45, 0.47), with the highest κ value for CO-RADS categories 1 (0.58, 95% CI: 0.54, 0.62) and 5 (0.68, 95% CI: 0.65, 0.72). The average AUC was 0.91 (95% CI: 0.85, 0.97) for predicting RT-PCR outcome and 0.95 (95% CI: 0.91, 0.99) for clinical diagnosis. The false-negative rate for CO-RADS 1 was nine of 161 cases (5.6%; 95% CI: 1.0%, 10%), and the false-positive rate for CO-RADS category 5 was one of 286 (0.3%; 95% CI: 0%, 1.0%). Conclusion The coronavirus disease 2019 (COVID-19) Reporting and Data System (CO-RADS) is a categorical assessment scheme for pulmonary involvement of COVID-19 at unenhanced chest CT that performs very well in predicting COVID-19 in patients with moderate to severe symptoms and has substantial interobserver agreement, especially for categories 1 and 5. © RSNA, 2020 Online supplemental material is available for this article.