Journal of medical virology
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Review Meta Analysis
Meta-analysis of chest CT features of patients with COVID-19 pneumonia.
The objective of this paper is to perform a meta-analysis regarding the chest computed tomography (CT) manifestations of coronavirus disease-2019 (COVID-19) pneumonia patients. PubMed, Embase, and Cochrane Library databases were searched from 1 December 2019 to 1 May 2020 using the keywords of "COVID-19 virus," "the 2019 novel coronavirus," "novel coronavirus," and "COVID-19." Studies that evaluated the CT manifestations of common and severe COVID-19 pneumonia were included. ⋯ Other CT features including ground-glass opacities (P = .404), air bronchogram (P = .070), nodule (P = .093), bronchial wall thickening (P = .15), subpleural band (P = .983), vascular enlargement (P = .207), and peripheral distribution (P = .668) did not have a significant association with the severity of the disease. No publication bias among the selected studies was suggested (Harbord's tests, P > .05 for all.) We obtained reliable estimates of the chest CT manifestations of COVID-19 pneumonia patients, which might provide an important clue for the diagnosis and classification of COVID-19 pneumonia.
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The serological testing of anti-SARS-CoV-2 immunoglobulin G (IgG) and/or IgM is widely used in the diagnosis of COVID-19. However, its diagnostic efficacy remains unclear. In this study, we searched for diagnostic studies from the Web of Science, PubMed, Embase, CNKI, and Wanfang databases to calculate the pooled diagnostic accuracy measures using bivariate random-effects model meta-analysis. ⋯ A subgroup analysis among detection methods indicated the sensitivity of IgG and IgM using enzyme-linked immunosorbent assay were slightly lower than those using gold immunochromatography assay (GICA) and chemiluminescence immunoassay (P > .05). These results showed that the detection of anti-SARS-CoV-2 IgG and IgM had high diagnostic efficiency to assist the diagnosis of SARS-CoV-2 infection. And, GICA might be used as the preferred method for its accuracy and simplicity.
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Multicenter Study
Simple nomogram based on initial laboratory data for predicting the probability of ICU transfer of COVID-19 patients: Multicenter retrospective study.
This retrospective, multicenter study investigated the risk factors associated with intensive care unit (ICU) admission and transfer in 461 adult patients with confirmed coronavirus disease 2019 (COVID-19) hospitalized from 22 January to 14 March 2020 in Hunan, China. Outcomes of ICU and non-ICU patients were compared, and a simple nomogram for predicting the probability of ICU transfer after hospital admission was developed based on initial laboratory data using a Cox proportional hazards regression model. Differences in laboratory indices were observed between patients admitted to the ICU and those who were not admitted. ⋯ The lymphocyte count and albumin level were negatively associated with mortality (HR = 0.08 and 0.86, respectively). The developed model provides a means for identifying, at hospital admission, the subset of patients with COVID-19 who are at high risk of progression and would require transfer to the ICU within 3 and 7 days after hospitalization. This method of early patient triage allows a more effective allocation of limited medical resources.
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Coronavirus disease 2019 (COVID-19), which began in Wuhan, China, in December 2019, has caused a large global pandemic and poses a serious threat to public health. More than 4 million cases of COVID-19, which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), have been confirmed as of 11 May 2020. SARS-CoV-2 is a highly pathogenic and transmissible coronavirus that primarily spreads through respiratory droplets and close contact. ⋯ In the absence of antivirals and vaccines for COVID-19, there is an urgent need to understand the cytokine storm in COVID-19. Here, we have reviewed the current understanding of the features of SARS-CoV-2 and the pathological features, pathophysiological mechanisms, and treatments of the cytokine storm induced by COVID-19. In addition, we suggest that the identification and treatment of the cytokine storm are important components for rescuing patients with severe COVID-19.