Diagnostic Efficacy of 11 SARS-CoV-2 Serological Assays for COVID-19: A Meta-Analysis and Adjusted Indirect Comparison of Diagnostic Test Accuracy

IF 3.1 4区 医学 Q3 IMMUNOLOGY Immunity, Inflammation and Disease Pub Date : 2024-12-19 DOI:10.1002/iid3.70114
Ying Zhao, Minjie Zhang, Weiwei Liang, Lijiang Fang
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Abstract

Objective

In the past 5 years, a large number of serological assays for large-scale detection of antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigen emerged. Serological assays for SARS-CoV-2 were needed to support clinical diagnosis and epidemiological investigations. However, there were limited data on the diagnostic accuracy of these serological assays. We aimed to compare the diagnostic accuracy of 11 commercial serological assays for coronavirus disease-2019 (COVID-19) by taking the reverse transcriptase polymerase chain reaction (RT-PCR) assays as the reference standard, which served as the control arm to conduct an indirect comparison of diagnostic accuracy for 11 different SARS-CoV-2 serological assays.

Methods

This meta-analysis was conducted following the PRISMA 2020 reporting guideline. Electronic searches were performed using the Cochrane Library, PubMed, Embase, Web of Science, Chinese Biological Medicine Database (CBM), China National Knowledge Infrastructure (CNKI), WANFANG, and Chinese Weipu (VIP) databases. Fifty-seven articles, including 11 serologic-based IgG, IgM, and total antibodies assays for SARS-CoV-2, published before June 2024, were included in this meta-analysis. The main outcome of this meta-analysis used to evaluate the performance of 11 assays included pooled diagnostic odds ratio (DOR), area under the summary receiver operating characteristic (AUC), and summary receiver operating characteristic curve (SROC). The R software was used for adjusted indirect comparison to calculate the relative diagnostic odds ratio (RDOR) with corresponding 95% confidence intervals (CIs), and indirect comparison forest plots showed the results.

Results

A total of 57 articles met the eligibility criteria for inclusion in our meta-analysis. The pooled DOR and the AUC for access SARS-CoV-2 IgG were 564.28 (95% CI 229.58−1386.91) and 1.00, and as for EDI novel coronavirus COVID-19 IgG those were 85.27 (95% CI 53.99−134.68) and 0.95, for EDI novel coronavirus COVID-19 IgM were 49.42 (95% CI 16.47−148.30) and 0.86, for iFlash-SARS-CoV-2 IgG were 652.31 (95% CI 362.32−1174.41) and 0.97, for iFlash-SARS-CoV-2 IgM were 36.72 (95% CI 12.42−108.54) and 0.76, for MAGLUMI 2019-nCoV IgG were 145.44 (95% CI 59.37−356.30) and 0.90, for MAGLUMI 2019-nCoV IgM were 21.59 (95% CI 14.27−32.67) and 0.59, for ortho-clinical anti-SARS-CoV-2 IgG were 719.46 (95% CI 262.34−1973.13) and 1.00, for ortho-clinical anti-SARS-CoV-2 total were 1104.60 (95% CI 395.64−3083.99) and 1.00, for Siemens SARS-CoV-2 total (COV2T) were 1143.37 (95% CI 316.49−4130.62) and 0.99, for Wantai SARS-CoV-2 total Ab were 1014.98 (95% CI 618.48−1665.66) and 1.00. The pooled DOR for assays-based IgG (n = 43), assays-based total antibody (n = 35), and assays-based IgM (n = 20) was 242.88 (95% CI 157.66−374.16), 1215.90 (95% CI 547.14−2702.07), and 40.99 (95% CI 22.63−74.25). The diagnostic accuracy of assays-based total antibody performed better than those of assays-based IgG and assays-based IgM; assays-based IgG performed better than assays-based IgM.

Conclusion

This study suggested that the Siemens SARS-CoV-2 total (COV2T), ortho-clinical anti-SARS-CoV-2 total, and Wantai SARS-CoV-2 total had the best overall diagnostic accuracy. The diagnostic efficacy of the assays-based total antibody had statistically significantly higher accuracy than those of assays-based IgG and assays-based IgM for COVID-19.

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11种SARS-CoV-2血清学检测对COVID-19的诊断效果:荟萃分析和诊断检测准确性的调整间接比较
目的:近5年来,出现了大量大规模检测SARS-CoV-2抗原抗体的血清学检测方法。需要对SARS-CoV-2进行血清学检测,以支持临床诊断和流行病学调查。然而,这些血清学分析的诊断准确性数据有限。我们以逆转录酶聚合酶链反应(RT-PCR)检测为参比标准,以RT-PCR为对照,对11种不同的SARS-CoV-2血清学检测方法的诊断准确性进行间接比较,比较11种商业化血清学检测方法对2019冠状病毒病(COVID-19)的诊断准确性。方法:本荟萃分析遵循PRISMA 2020报告指南进行。电子检索使用Cochrane Library、PubMed、Embase、Web of Science、中国生物医药数据库(CBM)、中国知网(CNKI)、万方和中国卫普数据库(VIP)。这项荟萃分析纳入了在2024年6月之前发表的57篇文章,包括11篇基于血清学的SARS-CoV-2 IgG、IgM和总抗体测定。该荟萃分析的主要结果用于评估11项试验的性能,包括合并诊断优势比(DOR)、总体受试者工作特征下面积(AUC)和总体受试者工作特征曲线(SROC)。采用R软件进行校正间接比较,计算相对诊断优势比(RDOR)和相应的95%置信区间(ci),间接比较森林图显示结果。结果:共有57篇文章符合纳入meta分析的资格标准。汇集金龟子和AUC访问SARS-CoV-2免疫球蛋白是564.28 (95% CI 229.58 - -1386.91)和1.00,至于EDI小说冠状病毒COVID-19免疫球蛋白这是85.27 (95% CI 53.99 - -134.68)和0.95,对EDI小说冠状病毒COVID-19 IgM分别为49.42 (95% CI 16.47 - -148.30)和0.86,为iFlash-SARS-CoV-2免疫球蛋白是652.31 (95% CI 362.32 - -1174.41)和0.97,为iFlash-SARS-CoV-2 IgM分别为36.72 (95% CI 12.42 - -108.54)和0.76,MAGLUMI 2019 - ncov免疫球蛋白是145.44 (95% CI 59.37 - -356.30)和0.90,MAGLUMI 2019-nCoV IgM分别为21.59 (95% CI 14.27 ~ 32.67)和0.59,抗SARS-CoV-2 IgG分别为719.46 (95% CI 262.34 ~ 1973.13)和1.00,抗SARS-CoV-2 IgG总抗体分别为1104.60 (95% CI 395.64 ~ 3083.99)和1.00,西门子SARS-CoV-2总抗体(COV2T)分别为1143.37 (95% CI 316.49 ~ 4130.62)和0.99,万泰SARS-CoV-2总抗体分别为1014.98 (95% CI 618.48 ~ 1665.66)和1.00。基于检测的IgG (n = 43)、基于检测的总抗体(n = 35)和基于检测的IgM (n = 20)的合并DOR分别为242.88 (95% CI 157.66-374.16)、1215.90 (95% CI 547.14-2702.07)和40.99 (95% CI 22.63-74.25)。总抗体检测的诊断准确率优于IgG和IgM检测;IgG检测法优于IgM检测法。结论:本研究提示Siemens SARS-CoV-2 total (COV2T)、orthoclinical anti- cov -2 total和Wantai SARS-CoV-2 total具有最佳的综合诊断准确性。基于elisa的总抗体诊断COVID-19的准确率高于基于elisa的IgG和基于elisa的IgM。
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来源期刊
Immunity, Inflammation and Disease
Immunity, Inflammation and Disease Medicine-Immunology and Allergy
CiteScore
3.60
自引率
0.00%
发文量
146
审稿时长
8 weeks
期刊介绍: Immunity, Inflammation and Disease is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research across the broad field of immunology. Immunity, Inflammation and Disease gives rapid consideration to papers in all areas of clinical and basic research. The journal is indexed in Medline and the Science Citation Index Expanded (part of Web of Science), among others. It welcomes original work that enhances the understanding of immunology in areas including: • cellular and molecular immunology • clinical immunology • allergy • immunochemistry • immunogenetics • immune signalling • immune development • imaging • mathematical modelling • autoimmunity • transplantation immunology • cancer immunology
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