Exploration of severe Covid-19 associated risk factor in China: Meta-analysis of current evidence.

AIM: This meta-analysis aimed to explore potential risk factors for severe Covid-19. METHODS: We systemically and comprehensively retrieved the eligible study evaluating clinical differences between severe vs non-severe Covid-19. Main effect sizes were demographic characteristics, comorbidities, signs and symptoms, laboratory findings as well as radiological features of chest CT. RESULTS: A total of 2566 Covid-19 people (771 in the severe group and 1795 in the non-severe group) from 14 studies were eligible for this meta-analysis. It was demonstrated that older age and males were more likely to have severe Covid-19. Patients with underlying comorbidities, such as hypertension, diabetes, heart disease and COPD were significantly more susceptible to severe Covid-19. Patients with dyspnoea were more likely to be severely ill. Depressed total lymphocytes were observed in this article. Meanwhile, although reticulation (30.8%), intrathoracic lymph node enlargement (20.5%) and pleural effusions (30.8%) were relatively infrequent, meta-analysis revealed that patients with these presentations in chest CT were associated with increased risk of severe Covid-19. CONCLUSIONS: There are significant differences in clinical characteristics between the severe and non-severe Covid-19 patients. Many factors are related to the severity of the disease, which can help clinicians to differentiate severe patients from non-severe patients.

Comparison of detection rate of 16 sampling methods for respiratory viruses: a Bayesian network meta-analysis of clinical data and systematic review.

BACKGROUND: Respiratory viruses (RVs) is a common cause of illness in people of all ages, at present, different types of sampling methods are available for respiratory viral diagnosis. However, the diversity of available sampling methods and the limited direct comparisons in randomised controlled trials (RCTs) make decision-making difficult. We did a network meta-analysis, which accounted for both direct and indirect comparisons, to determine the detection rate of different sampling methods for RVs. METHODS: Relevant articles were retrieved comprehensively by searching the online databases of PubMed, Embase and Cochrane published before 25 March 2020. With the help of R V.3.6.3 software and 'GeMTC V.0.8.2' package, network meta-analysis was performed within a Bayesian framework. Node-splitting method and I2 test combined leverage graphs and Gelman-Rubin-Brooks plots were conducted to evaluate the model's accuracy. The rank probabilities in direct and cumulative rank plots were also incorporated to rank the corresponding sampling methods for overall and specific virus. RESULTS: 16 sampling methods with 54 438 samples from 57 literatures were ultimately involved in this study. The model indicated good consistency and convergence but high heterogeneity, hence, random-effect analysis was applied. The top three sampling methods for RVs were nasopharyngeal wash (NPW), mid-turbinate swab (MTS) and nasopharyngeal swab (NPS). Despite certain differences, the results of virus-specific subanalysis were basically consistent with RVs: MTS, NPW and NPS for influenza; MTS, NPS and NPW for influenza-a and b; saliva, NPW and NPS for rhinovirus and parainfluenza; NPW, MTS and nasopharyngeal aspirate for respiratory syncytial virus; saliva, NPW and MTS for adenovirus and sputum; MTS and NPS for coronavirus. CONCLUSION: This network meta-analysis provides supporting evidences that NPW, MTS and NPS have higher diagnostic value regarding RVs infection, moreover, particular preferred methods should be considered in terms of specific virus pandemic. Of course, subsequent RCTs with larger samples are required to validate our findings.