wepidemics

meta4diag2

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title of the paper: Diagnostic efficacy of anti-SARS-CoV-2 IgG/IgM test for COVID-19: A meta-analysis

keywords: diagnostic tests, meta-analysis, bayesian inference analysis, integrated nested laplace approximation

Parameters: diagnostic test accuracy

software:

meta4diag (R)

Content

From the paper’s Abstract:

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. As a result, 22 from a total of 1613 articles, including 2282 patients with SARS-CoV-2 and 1485 healthy persons or patients without SARS-CoV-2, were selected for a meta-analysis. Pooled sensitivity, specificity, and area under curve of the summary receiver operator curve (SROC) were: (a) 0.85 (95% confidence interval [CI]: 0.79-0.90), 0.99 (95% CI: 0.98-1.00), and 0.99 (95% CI: 0.97-0.99) for anti-SARS-CoV-2 IgG and (b) 0.74 (95% CI: 0.65-0.81), 0.99 (95% CI: 0.97-1.00), and 0.95 (95% CI: 0.93-0.97) for IgM. 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.

original entry: https://github.com/UT-IDDynamics/wepidemics/blob/main/data/meta4diag2.md