Earliest histopathological changes in COVID-19 pneumonia with comprehensive gene expression analyses: A case series study.

AIMS: In COVID-19 pneumonia, early detection and appropriate treatment are essential to prevent severe exacerbation. Therefore, it is important to understand the initiating events of COVID-19 pneumonia. However, at present, the literature about early stage disease has been very limited. Here, we investigated the earliest histopathological changes and gene expression profiles associated with COVID-19 pneumonia. METHODS AND RESULTS: We carefully examined 25 autopsied cases with different clinical courses. Dilation of capillaries and edematous thickening of the alveolar septa were found even in areas that macroscopically looked almost normal. Pneumocytes, histocytes/macrophages, and vascular endothelial cells were immunohistochemically positive for tissue factor, which is an important early responder to tissue injuries. Comprehensive gene expression analyses revealed that those lesions presented differential profiles compared to those of control lungs and were associated with a significant upregulation of the lysosomal pathway. CONCLUSIONS: Alveolar capillary dilation and edematous thickening may be the earliest histopathological change detected in COVID-19 pneumonia. Intensive investigations of such lesions may lead to an understanding of the initiating event of not only COVID-19 pneumonia but also of general diffuse alveolar damage.

Prediction of prognosis in patients with severe COVID-19 pneumonia using CT score by emergency physicians: a single-center retrospective study.

We aimed to develop a method to determine the CT score that can be easily obtained from CT images and examine its prognostic value for severe COVID pneumonia. Patients with COVID pneumonia who required ventilatory management by intubation were included. CT score was based on anatomical information in axial CT images and were divided into three sections of height from the apex to the bottom. The extent of pneumonia in each section was rated from 0 to 5 and summed. The primary outcome was the prediction of patients who died or were managed on extracorporeal membrane oxygenation (ECMO) based on the CT score at admission. Of the 71 patients included, 12 (16.9%) died or required ECMO management, and the CT score predicted death or ECMO management with ROC of 0.718 (0.561-0.875). The death or ECMO versus survival group (median [quartiles]) had a CT score of 17.75 (14.75-20) versus 13 (11-16.5), p = 0.017. In conclusion, a higher score on our generated CT score could predict the likelihood of death or ECMO management. A CT score at the time of admission allows for early preparation and transfer to a hospital that can manage patients who may need ECMO.

Comparison of CO-RADS Scores Based on Visual and Artificial Intelligence Assessments in a Non-Endemic Area.

In this study, we first developed an artificial intelligence (AI)-based algorithm for classifying chest computed tomography (CT) images using the coronavirus disease 2019 Reporting and Data System (CO-RADS). Subsequently, we evaluated its accuracy by comparing the calculated scores with those assigned by radiologists with varying levels of experience. This study included patients with suspected SARS-CoV-2 infection who underwent chest CT imaging between February and October 2020 in Japan, a non-endemic area. For each chest CT, the CO-RADS scores, determined by consensus among three experienced chest radiologists, were used as the gold standard. Images from 412 patients were used to train the model, whereas images from 83 patients were tested to obtain AI-based CO-RADS scores for each image. Six independent raters (one medical student, two residents, and three board-certified radiologists) evaluated the test images. Intraclass correlation coefficients (ICC) and weighted kappa values were calculated to determine the inter-rater agreement with the gold standard. The mean ICC and weighted kappa were 0.754 and 0.752 for the medical student and residents (taken together), 0.851 and 0.850 for the diagnostic radiologists, and 0.913 and 0.912 for AI, respectively. The CO-RADS scores calculated using our AI-based algorithm were comparable to those assigned by radiologists, indicating the accuracy and high reproducibility of our model. Our study findings would enable accurate reading, particularly in areas where radiologists are unavailable, and contribute to improvements in patient management and workflow.

Real-world evaluation of a computed tomography-first triage strategy for suspected Coronavirus disease 2019 in outpatients in Japan: An observational cohort study.

ABSTRACT: The Coronavirus disease 2019 pandemic continues to spread worldwide. Because of the absence of reliable rapid diagnostic systems, patients with symptoms of Coronavirus disease 2019 are treated as suspected of the disease. Use of computed tomography findings in Coronavirus disease 2019 are expected to be a reasonable method for triaging patients, and computed tomography-first triage strategies have been proposed. However, clinical evaluation of a computed tomography-first triage protocol is lacking.The aim of this study is to investigate the real-world efficacy and limitations of a computed tomography-first triage strategy in patients with suspected Coronavirus disease 2019.This was a single-center cohort study evaluating outpatients with fever who received medical examination at Yokohama City University Hospital, prospectively registered between 9 February and 5 May 2020. We treated according to the computed tomography-first triage protocol. The primary outcome was efficacy of the computed tomography-first triage protocol for patients with fever in an outpatient clinic. Efficacy of the computed tomography-first triage protocol for outpatients with fever was evaluated using sensitivity, specificity, positive predictive value, and negative predictive value. We conducted additional analyses of the isolation time of feverish outpatients and final diagnoses.In total, 108 consecutive outpatients with fever were examined at our hospital. Using the computed tomography-first triage protocol, 48 (44.9%) patients were classified as suspected Coronavirus disease 2019. Nine patients (18.8%) in this group were positive for severe acute respiratory syndrome coronavirus 2 using polymerase chain reaction; no patients in the group considered less likely to have Coronavirus disease 2019 tested positive for the virus. The protocol significantly shortened the duration of isolation for the not-suspected versus the suspected group (70.5 vs 1037.0 minutes, P < .001).Our computed tomography-first triage protocol was acceptable for screening patients with suspected Coronavirus disease 2019. This protocol will be helpful for appropriate triage, especially in areas where polymerase chain reaction is inadequate.

COVID-19 pneumonia: infection control protocol inside computed tomography suites.

A novel coronavirus (severe acute respiratory syndrome coronavirus 2) causes a cluster of pneumonia cases in Wuhan, China. It spread rapidly and globally. CT imaging is helpful for the evaluation of the novel coronavirus disease 2019 (COVID-19) pneumonia. Infection control inside the CT suites is also important to prevent hospital-related transmission of COVID-19. We present our experience with infection control protocol for COVID-19 inside the CT suites.