wepidemics

meta4diag

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title of the paper: Meta-analysis of diagnostic performance of serology tests for COVID-19: impact of assay design and post-symptom-onset intervals

doi: 10.1080/22221751.2020.1826362

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

Parameters: diagnostic test accuracy

software:

Content

From the paper’s Abstract:

Serology detection is recognized for its sensitivity in convalescent patients with COVID-19, in comparison with nucleic acid amplification tests (NAATs). This article aimed to evaluate the diagnostic accuracy of serologic methods for COVID-19 based on assay design and post-symptom-onset intervals. Two authors independently searched PubMed, Cochrane library, Ovid, EBSCO for case–control, longitudinal and cohort studies that determined the diagnostic accuracy of serology tests in comparison with NAATs in COVID-19 cases and used QUADAS-2 for quality assessment. Pooled accuracy was analysed using INLA method. A total of 27 studies were included in this meta-analysis, with 4 cohort, 16 case–control and 7 longitudinal studies and 4565 participants. Serology tests had the lowest sensitivity at 0–7 days after symptom onset and the highest at >14 days. TAB had a better sensitivity than IgG or IgM only. Using combined nucleocapsid (N) and spike(S) protein had a better sensitivity compared to N or S protein only. Lateral flow immunoassay (LFIA) had a lower sensitivity than enzyme-linked immunoassay (ELISA) and chemiluminescent immunoassay (CLIA). Serology tests will play an important role in the clinical diagnosis for later stage COVID-19 patients. ELISA tests, detecting TAB or targeting combined N and S proteins had a higher diagnostic sensitivity compared to other methods.

original entry: https://github.com/UT-IDDynamics/wepidemics/blob/main/data/meta4diag.md