Evidence map›Paper›PMID 36589993›Full record

SynthesisFrontiers in public health2022

Accuracy of serological tests for COVID-19: A systematic review and meta-analysis.

Xiaoyan Zheng, Rui Hua Duan, Fen Gong, Xiaojing Wei, Yu Dong, Rouhao Chen, Ming Yue Liang, Chunzhi Tang, Liming Lu

Open access · goldAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
3.7field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.

  1. Pooled it
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  7. Contribution ofCurrent issues in molecular biology · 2025
    Review
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  14. Performance assessment of a new serological diagnostic test for COVID-19 with candidate peptides from spike and nucleocapsid viral proteins.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2024
    Article
  15. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Xiaoyan ZhengSchool of Rehabilitation Sciences, Southern Medical University, Guangzhou, China.
Rui Hua DuanFirst Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China.
Fen GongFirst Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China.
Xiaojing WeiMedical College of Acupuncture-Moxibustion and Rehabilitation, Guangzhou University of Chinese Medicine, Guangzhou, China.
Yu DongMedical College of Acupuncture-Moxibustion and Rehabilitation, Guangzhou University of Chinese Medicine, Guangzhou, China.
Rouhao ChenMedical College of Acupuncture-Moxibustion and Rehabilitation, Guangzhou University of Chinese Medicine, Guangzhou, China.
Ming Yue LiangMedical College of Acupuncture-Moxibustion and Rehabilitation, Guangzhou University of Chinese Medicine, Guangzhou, China.
Chunzhi TangMedical College of Acupuncture-Moxibustion and Rehabilitation, Guangzhou University of Chinese Medicine, Guangzhou, China.
Liming LuMedical College of Acupuncture-Moxibustion and Rehabilitation, Guangzhou University of Chinese Medicine, Guangzhou, China.
Guangzhou University of Chinese Medicine · CNSouthern Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To determine the diagnostic accuracy of serological tests for coronavirus disease-2019 (COVID-19). Methods: PubMed, Embase and the Cochrane Library were searched from January 1 2020 to September 2 2022. We included studies that measured the sensitivity, specificity or both qualities of a COVID-19 serological test and a reference standard of a viral culture or reverse transcriptase polymerase chain reaction (RT-PCR). The risk of bias was assessed by using quality assessment of diagnostic accuracy studies 2 (QUADAS-2). The primary outcomes included overall sensitivity and specificity, as stratified by the methods of serological testing [enzyme-linked immunosorbent assays (ELISAs), lateral flow immunoassays (LFIAs) or chemiluminescent immunoassays (CLIAs)] and immunoglobulin classes (IgG, IgM, or both). Secondary outcomes were stratum-specific sensitivity and specificity within the subgroups, as defined by study or participant characteristics, which included the time from the onset of symptoms, testing Results: Eight thousand seven hundred and eighty-five references were identified and 169 studies included. Overall, we judged the risk of bias to be high in 47.9 % (81/169) of the studies, and a low risk of applicability concerns was found in 100% (169/169) of the studies. For each method of testing, the pooled sensitivity of the ELISAs ranged from 81 to 82%, with sensitivities ranging from 69 to 70% for the LFIAs and 77% to 79% for the CLIAs. Among the evaluated tests, IgG (80-81%)-based tests exhibited better sensitivities than IgM-based tests (66-68%). IgG/IgM-based CLIA had the highest sensitivity [87% (86-88%)]. All of the tests displayed high specificity (97-98%). Heterogeneity was observed in all of the analyses. The detection of nucleocapsid protein (77-80%) as the antigen target was found to offer higher sensitivity results than surface protein detection (66-68%). Sensitivity was higher in the in-house assays (78-79%) than in the commercial kits (47-48%). Conclusion: Among the evaluated tests, ELISA and CLIA tests performed better in terms of sensitivity than did the LFIA. IgG-based tests had higher sensitivity than IgM-based tests, and combined IgG/IgM test-based CLIA tests had the best overall diagnostic test accuracy. The type of sample, serological kit and timing of use of the specific tests were associated with the diagnostic accuracy. Due to the limitations of the serological tests, other techniques should be quickly approved to provide guidance for the correct diagnosis of COVID-19.

Indexed as

COVID-19HumansImmunoglobulin GImmunoglobulin MSARS-CoV-2Serologic TestsImmunoglobulin GImmunoglobulin MCOVID-19meta-analysisRT–PCRserological testssystematic review

Identifiers

PMID36589993
PMCPMC9800917
OpenAlexW4313275874

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.