Soon after reports of a novel coronavirus capable of causing severe pneumonia surfaced in late 2019, expeditious global spread of Severe Acute Respiratory Distress Syndrome Coronavirus 2 (SARS-CoV-2) forced the World Health Organization to declare an international state of emergency. Although best known for causing symptoms of upper respiratory tract infection in mild cases and fulminant pneumonia in severe disease, Coronavirus Disease 2019 (COVID-19) has also been associated with gastrointestinal, neurologic, cardiac, and hematologic presentations. Despite concerns over poor specificity and undue radiation exposure, chest imaging nonetheless remains central to the initial diagnosis and monitoring of COVID-19 progression, as well as to the evaluation of complications. Classic features on chest CT include ground-glass and reticular opacities with or without superimposed consolidations, frequently presenting in a bilateral, peripheral, and posterior distribution. More recently, studies conducted with MRI have shown excellent concordance with chest CT in visualizing typical features of COVID-19 pneumonia. For patients in whom exposure to ionizing radiation should be avoided, particularly pregnant women and children, pulmonary MRI may represent a suitable alternative to chest CT. Although PET imaging is not typically considered among first-line investigative modalities for the diagnosis of lower respiratory tract infections, numerous reports have noted incidental localization of radiotracer in parenchymal regions of COVID-19-associated pulmonary lesions. These findings are consistent with data from Middle East Respiratory Syndrome-CoV cohorts suggesting an ability for 18 F-FDG PET to detect subclinical infection and lymphadenitis in subjects without overt clinical signs of infection. Though highly sensitive, use of PET/CT for primary detection of COVID-19 is constrained by poor specificity, as well as considerations of cost, radiation burden, and prolonged exposure times for imaging staff. Even still, decontamination of scanner bays is a time-consuming process, and proper ventilation of scanner suites may additionally require up to an hour of downtime to allow for sufficient air exchange. Yet, in patients who require nuclear medicine investigations for other clinical indications, PET imaging may yield the earliest detection of nascent infection in otherwise asymptomatic individuals. Especially for patients with concomitant malignancies and other states of immunocompromise, prompt recognition of infection and early initiation of supportive care is crucial to maximizing outcomes and improving survivability.

Background Efforts to reduce nosocomial spread of COVID-19 have resulted in unprecedented disruptions in clinical workflows and numerous unexpected stressors for imaging departments across the country. Our purpose was to more precisely evaluate these impacts on radiologists through a nationwide survey. Methods A 43-item anonymous questionnaire was adapted from the AO Spine Foundation's survey and distributed to 1521 unique email addresses using REDCap™ (Research Electronic Data Capture). Additional invitations were sent out to American Society of Emergency Radiology (ASER) and Association of University Radiologists (AUR) members. Responses were collected over a period of 8 days. Descriptive analyses and multivariate modeling were performed using SAS v9.4 software. Results A total of 689 responses from radiologists across 44 different states met the criteria for inclusion in the analysis. As many as 61% of respondents rated their level of anxiety with regard to COVID-19 to be a 7 out of 10 or greater, and higher scores were positively correlated the standardized number of COVID-19 cases in a respondent's state (RR = 1.11, 95% CI: 1.02–1.21, p = 0.01). Citing the stressor of “personal health” was a strong predictor of higher anxiety scores (RR 1.23; 95% CI: 1.13–1.34, p < 0.01). By contrast, participants who reported needing no coping methods were more likely to self-report lower anxiety scores (RR 0.4; 95% CI: 0.3–0.53, p < 0.01). Conclusion COVID-19 has had a significant impact on radiologists across the nation. As these unique stressors continue to evolve, further attention must be paid to the ways in which we may continue to support radiologists working in drastically altered practice environments and in remote settings.

Since first report of a novel coronavirus in December of 2019, the Coronavirus Disease 2019 (COVID-19) pandemic has crippled healthcare systems around the world. While many initial screening protocols centered around laboratory detection of the virus, early testing assays were thought to be poorly sensitive in comparison to chest computed tomography, especially in asymptomatic disease. Coupled with shortages of reverse transcription polymerase chain reaction (RT-PCR) testing kits in many parts of the world, these regions instead turned to the use of advanced imaging as a first-line screening modality. However, in contrast to previous Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome coronavirus epidemics, chest X-ray has not demonstrated optimal sensitivity to be of much utility in first-line screening protocols. Though current national and international guidelines recommend for the use of RT-PCR as the primary screening tool for suspected cases of COVID-19, institutional and regional protocols must consider local availability of resources when issuing universal recommendations. Successful containment and social mitigation strategies worldwide have been thus far predicated on unified governmental responses, though the underlying ideologies of these practices may not be widely applicable in many Western nations. As the strain on the radiology workforce continues to mount, early results indicate a promising role for the use of machine-learning algorithms as risk stratification schema in the months to come.

No abstract

No abstract

Since the spread of the coronavirus disease 2019 (COVID-19) was designated as a pandemic by the World Health Organization, health care systems have been forced to adapt rapidly to defer less urgent care during the crisis. The United States (U.S.) has adopted a four-phase approach to decreasing and then resuming non-essential work. Through strong restrictive measures, Phase I slowed the spread of disease, allowing states to safely diagnose, isolate, and treat patients with COVID-19. In support of social distancing measures, non-urgent studies were postponed, and this created a backlog. Now, as states transition to Phase II, restrictions on non-essential activities will ease, and radiology departments must re-establish care while continuing to mitigate the risk of COVID-19 transmission all while accommodating this backlog. In this article, we propose a roadmap that incorporates the current practice guidelines and subject matter consensus statements for the phased reopening of non-urgent and elective radiology services. This roadmap will focus on operationalizing these recommendations for patient care and workforce management. Tiered systems are proposed for the prioritization of elective procedures, with physician-to-physician communication encouraged. Infection control methods, provision of personal protective equipment (PPE), and physical distancing measures are highlighted. Finally, changes in hours of operation, hiring strategies, and remote reading services are discussed for their potential to ease the transition to normal operations.

No abstract

Background: The novel Severe Acute Respiratory Syndrome Coronavirus-2 has led to a global pandemic in which case fatality rate (CFR) has varied from country to country. This study aims to identify factors that may explain the variation in CFR across countries. Methods: We identified 24 potential risk factors affecting CFR. For all countries with over 5000 reported COVID-19 cases, we used country-specific datasets from the WHO, the OECD, and the United Nations to quantify each of these factors. We examined univariable relationships of each variable with CFR, as well as correlations among predictors and potential interaction terms. Our final multivariable negative binomial model included univariable predictors of significance and all significant interaction terms. Results: Across the 39 countries under consideration, our model shows COVID-19 case fatality rate was best predicted by time to implementation of social distancing measures, hospital beds per 1000 individuals, percent population over 70 years, CT scanners per 1 million individuals, and (in countries with high population density) smoking prevalence. Conclusion: Our model predicted an increased CFR for countries that waited over 14 days to implement social distancing interventions after the 100th reported case. Smoking prevalence and percentage population over the age of 70 years were also associated with higher CFR. Hospital beds per 1000 and CT scanners per million were identified as possible protective factors associated with decreased CFR.