No abstract

To describe radiographic key patterns on Chest X-ray (CXR) in patients with SARS-CoV-2 infection, assessing the prevalence of radiographic signs of interstitial pneumonia. To evaluate pattern variation between a baseline and a follow-up CXR. 1117 patients tested positive for SARS-CoV-2 infection were retrospectively enrolled from four centers in Lombardy region. All patients underwent a CXR at presentation. Follow-up CXR was performed when clinically indicated. Two radiologists in each center reviewed images and classified them as suggestive or not for interstitial pneumonia, recording the presence of ground-glass opacity (GGO), reticular pattern or consolidation and their distribution. Pearson’s χ2 test for categorical variables and McNemar test (χ2 for paired data) were performed. Patients mean age 63.3 years, 767 were males (65.5%). The main result is the large proportion of positive CXR in COVID-19 patients. Baseline CXR was positive in 940 patients (80.3%), with significant differences in age and sex distribution between patients with positive and negative CXR. 382 patients underwent a follow-up CXR. The most frequent pattern on baseline CXR was the GGO (66.1%), on follow-up was consolidation (53.4%). The most common distributions were peripheral and middle-lower lung zone. We described key-patterns and their distribution on CXR in a large cohort of COVID-19 patients: GGO was the most frequent finding on baseline CXR, while we found an increase in the proportion of lung consolidation on follow-up CXR. CXR proved to be a reliable tool in our cohort obtaining positive results in 80.3% of the baseline cases.

Preparedness for the ongoing coronavirus disease 2019 (COVID-19) and its spread in Italy called for setting up of adequately equipped and dedicated health facilities to manage sick patients while protecting healthcare workers, uninfected patients, and the community. In our country, in a short time span, the demand for critical care beds exceeded supply. A new sequestered hospital completely dedicated to intensive care (IC) for isolated COVID-19 patients needed to be designed, constructed, and deployed. Along with this new initiative, the new concept of "Pandemic Radiology Unit" was implemented as a practical solution to the emerging crisis, born out of a critical and urgent acute need. The present article describes logistics, planning, and practical design issues for such a pandemic radiology and critical care unit (e.g., space, infection control, safety of healthcare workers, etc.) adopted in the IC Hospital Unit for the care and management of COVID-19 patients.

Purpose: The computation of lung recruitability in acute respiratory distress syndrome (ARDS) is advocated to set positive end-expiratory pressure (PEEP) for preventing lung collapse. The quantitative lung CT scan, obtained by manual image processing, is the reference method but it is time consuming. The aim of this study was to evaluate the accuracy of a visual anatomical analysis compared with a quantitative lung CT scan analysis in assessing lung recruitability. Methods: Fifty sets of two complete lung CT scans of ALI/ARDS patients computing lung recruitment were analyzed. Lung recruitability computed at an airway pressure of 5 and 45 cmH 2 O was defined as the percentage decrease in the collapsed/ consolidated lung parenchyma assessed by two expert radiologists using a visual anatomical analysis and as the decrease in not aerated lung regions using a quantitative analysis computed by dedicated software.Results: Lung recruitability was 11.3 % (interquartile range 7.39-16.41) and 15.5 % (interquartile range 8.18-21.43) with the visual anatomical and quantitative analysis, respectively. In the Bland-Altman analysis, the bias and agreement bands between the visual anatomical and quantitative analysis were -2.9 % (-11.8 to ?5.9 %). The ROC curve showed that the optimal cutoff values for the visual anatomical analysis in predicting high versus low lung recruitability was 8.9 % (area under the ROC curve 0.9248, 95 % CI 0.8550-0.9946). Considering this cutoff, the sensitivity, specificity, and diagnostic accuracy were 0.96, 0.76, and 0.86, respectively. Conclusions: Visual anatomical analysis can classify patients into those with high and low lung recruitability allowing more intensivists to get access to lung recruitability assessment.