Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK.
Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK.
Cochrane Database Syst Rev. 2022 Nov 17;11(11):CD013652. doi: 10.1002/14651858.CD013652.pub2.
BACKGROUND: The diagnostic challenges associated with the COVID-19 pandemic resulted in rapid development of diagnostic test methods for detecting SARS-CoV-2 infection. Serology tests to detect the presence of antibodies to SARS-CoV-2 enable detection of past infection and may detect cases of SARS-CoV-2 infection that were missed by earlier diagnostic tests. Understanding the diagnostic accuracy of serology tests for SARS-CoV-2 infection may enable development of effective diagnostic and management pathways, inform public health management decisions and understanding of SARS-CoV-2 epidemiology. OBJECTIVES: To assess the accuracy of antibody tests, firstly, to determine if a person presenting in the community, or in primary or secondary care has current SARS-CoV-2 infection according to time after onset of infection and, secondly, to determine if a person has previously been infected with SARS-CoV-2. Sources of heterogeneity investigated included: timing of test, test method, SARS-CoV-2 antigen used, test brand, and reference standard for non-SARS-CoV-2 cases. SEARCH METHODS: The COVID-19 Open Access Project living evidence database from the University of Bern (which includes daily updates from PubMed and Embase and preprints from medRxiv and bioRxiv) was searched on 30 September 2020. We included additional publications from the Evidence for Policy and Practice Information and Co-ordinating Centre (EPPI-Centre) 'COVID-19: Living map of the evidence' and the Norwegian Institute of Public Health 'NIPH systematic and living map on COVID-19 evidence'. We did not apply language restrictions. SELECTION CRITERIA: We included test accuracy studies of any design that evaluated commercially produced serology tests, targeting IgG, IgM, IgA alone, or in combination. Studies must have provided data for sensitivity, that could be allocated to a predefined time period after onset of symptoms, or after a positive RT-PCR test. Small studies with fewer than 25 SARS-CoV-2 infection cases were excluded. We included any reference standard to define the presence or absence of SARS-CoV-2 (including reverse transcription polymerase chain reaction tests (RT-PCR), clinical diagnostic criteria, and pre-pandemic samples). DATA COLLECTION AND ANALYSIS: We use standard screening procedures with three reviewers. Quality assessment (using the QUADAS-2 tool) and numeric study results were extracted independently by two people. Other study characteristics were extracted by one reviewer and checked by a second. We present sensitivity and specificity with 95% confidence intervals (CIs) for each test and, for meta-analysis, we fitted univariate random-effects logistic regression models for sensitivity by eligible time period and for specificity by reference standard group. Heterogeneity was investigated by including indicator variables in the random-effects logistic regression models. We tabulated results by test manufacturer and summarised results for tests that were evaluated in 200 or more samples and that met a modification of UK Medicines and Healthcare products Regulatory Agency (MHRA) target performance criteria. MAIN RESULTS: We included 178 separate studies (described in 177 study reports, with 45 as pre-prints) providing 527 test evaluations. The studies included 64,688 samples including 25,724 from people with confirmed SARS-CoV-2; most compared the accuracy of two or more assays (102/178, 57%). Participants with confirmed SARS-CoV-2 infection were most commonly hospital inpatients (78/178, 44%), and pre-pandemic samples were used by 45% (81/178) to estimate specificity. Over two-thirds of studies recruited participants based on known SARS-CoV-2 infection status (123/178, 69%). All studies were conducted prior to the introduction of SARS-CoV-2 vaccines and present data for naturally acquired antibody responses. Seventy-nine percent (141/178) of studies reported sensitivity by week after symptom onset and 66% (117/178) for convalescent phase infection. Studies evaluated enzyme-linked immunosorbent assays (ELISA) (165/527; 31%), chemiluminescent assays (CLIA) (167/527; 32%) or lateral flow assays (LFA) (188/527; 36%). Risk of bias was high because of participant selection (172, 97%); application and interpretation of the index test (35, 20%); weaknesses in the reference standard (38, 21%); and issues related to participant flow and timing (148, 82%). We judged that there were high concerns about the applicability of the evidence related to participants in 170 (96%) studies, and about the applicability of the reference standard in 162 (91%) studies. Average sensitivities for current SARS-CoV-2 infection increased by week after onset for all target antibodies. Average sensitivity for the combination of either IgG or IgM was 41.1% in week one (95% CI 38.1 to 44.2; 103 evaluations; 3881 samples, 1593 cases), 74.9% in week two (95% CI 72.4 to 77.3; 96 evaluations, 3948 samples, 2904 cases) and 88.0% by week three after onset of symptoms (95% CI 86.3 to 89.5; 103 evaluations, 2929 samples, 2571 cases). Average sensitivity during the convalescent phase of infection (up to a maximum of 100 days since onset of symptoms, where reported) was 89.8% for IgG (95% CI 88.5 to 90.9; 253 evaluations, 16,846 samples, 14,183 cases), 92.9% for IgG or IgM combined (95% CI 91.0 to 94.4; 108 evaluations, 3571 samples, 3206 cases) and 94.3% for total antibodies (95% CI 92.8 to 95.5; 58 evaluations, 7063 samples, 6652 cases). Average sensitivities for IgM alone followed a similar pattern but were of a lower test accuracy in every time slot. Average specificities were consistently high and precise, particularly for pre-pandemic samples which provide the least biased estimates of specificity (ranging from 98.6% for IgM to 99.8% for total antibodies). Subgroup analyses suggested small differences in sensitivity and specificity by test technology however heterogeneity in study results, timing of sample collection, and smaller sample numbers in some groups made comparisons difficult. For IgG, CLIAs were the most sensitive (convalescent-phase infection) and specific (pre-pandemic samples) compared to both ELISAs and LFAs (P < 0.001 for differences across test methods). The antigen(s) used (whether from the Spike-protein or nucleocapsid) appeared to have some effect on average sensitivity in the first weeks after onset but there was no clear evidence of an effect during convalescent-phase infection. Investigations of test performance by brand showed considerable variation in sensitivity between tests, and in results between studies evaluating the same test. For tests that were evaluated in 200 or more samples, the lower bound of the 95% CI for sensitivity was 90% or more for only a small number of tests (IgG, n = 5; IgG or IgM, n = 1; total antibodies, n = 4). More test brands met the MHRA minimum criteria for specificity of 98% or above (IgG, n = 16; IgG or IgM, n = 5; total antibodies, n = 7). Seven assays met the specified criteria for both sensitivity and specificity. In a low-prevalence (2%) setting, where antibody testing is used to diagnose COVID-19 in people with symptoms but who have had a negative PCR test, we would anticipate that 1 (1 to 2) case would be missed and 8 (5 to 15) would be falsely positive in 1000 people undergoing IgG or IgM testing in week three after onset of SARS-CoV-2 infection. In a seroprevalence survey, where prevalence of prior infection is 50%, we would anticipate that 51 (46 to 58) cases would be missed and 6 (5 to 7) would be falsely positive in 1000 people having IgG tests during the convalescent phase (21 to 100 days post-symptom onset or post-positive PCR) of SARS-CoV-2 infection. AUTHORS' CONCLUSIONS: Some antibody tests could be a useful diagnostic tool for those in whom molecular- or antigen-based tests have failed to detect the SARS-CoV-2 virus, including in those with ongoing symptoms of acute infection (from week three onwards) or those presenting with post-acute sequelae of COVID-19. However, antibody tests have an increasing likelihood of detecting an immune response to infection as time since onset of infection progresses and have demonstrated adequate performance for detection of prior infection for sero-epidemiological purposes. The applicability of results for detection of vaccination-induced antibodies is uncertain.
背景:由于 COVID-19 大流行相关的诊断挑战,针对 SARS-CoV-2 感染的诊断检测方法迅速发展。用于检测 SARS-CoV-2 抗体存在的血清学检测可检测既往感染,并且可能检测到更早的诊断检测漏诊的 SARS-CoV-2 感染病例。了解 SARS-CoV-2 感染血清学检测的诊断准确性可能有助于制定有效的诊断和管理途径,为公共卫生管理决策和 SARS-CoV-2 流行病学的理解提供信息。
目的:评估抗体检测的准确性,首先,根据感染后时间确定在社区或初级或二级保健中出现症状的人是否患有当前 SARS-CoV-2 感染,其次,确定一个人是否曾经感染过 SARS-CoV-2。纳入的异质性来源包括:检测时间、检测方法、使用的 SARS-CoV-2 抗原、检测品牌以及非 SARS-CoV-2 病例的参考标准。
检索方法:对伯尔尼大学 COVID-19 开放获取项目的生命证据数据库(其中包括每日更新的 PubMed 和 Embase 以及 medRxiv 和 bioRxiv 的预印本)进行了检索,检索日期为 2020 年 9 月 30 日。我们还从证据与实践信息协调中心 (EPPI-Centre) 的“COVID-19:证据实时地图”和挪威公共卫生研究所的“SARS-CoV-2 证据的系统和实时地图”中纳入了其他出版物。我们没有应用语言限制。
选择标准:我们纳入了评估商业化生产的血清学检测的任何设计的研究,这些检测针对 IgG、IgM、IgA 单独或联合使用。研究必须提供可分配到症状发作后特定时间段内或阳性 RT-PCR 检测后的敏感性数据。小样本量(少于 25 例 SARS-CoV-2 感染病例)的研究被排除在外。我们纳入了任何用于定义 SARS-CoV-2 存在或不存在的参考标准(包括逆转录聚合酶链反应 (RT-PCR) 检测、临床诊断标准和大流行前样本)。
数据收集和分析:我们使用标准的筛查程序,由三位评审员进行。使用两位研究人员独立提取质量评估(使用 QUADAS-2 工具)和数值研究结果。其他研究特征由一位评审员提取,由第二位评审员检查。我们为每个检测报告了敏感性和特异性及其 95%置信区间(CI),对于荟萃分析,我们根据合格时间期和参考标准组拟合了单变量随机效应逻辑回归模型以获得敏感性和特异性。通过在随机效应逻辑回归模型中纳入指标变量来调查异质性。我们按检测制造商对结果进行了制表,并汇总了评估了 200 个或更多样本且符合英国药品和保健产品监管局 (MHRA) 目标性能标准的检测。
主要结果:我们纳入了 178 项单独的研究(由 177 项研究报告描述,其中 45 项为预印本),提供了 527 项检测评估。这些研究包括 64688 个样本,其中 25724 个来自确诊的 SARS-CoV-2 感染者;大多数研究比较了两种或多种检测方法的准确性(102/178,57%)。确诊 SARS-CoV-2 感染的参与者最常见的是医院住院患者(78/178,44%),45%(81/178)的研究使用大流行前样本来估计特异性。超过三分之二的研究(123/178,69%)根据已知的 SARS-CoV-2 感染状况招募参与者。所有研究均在 SARS-CoV-2 疫苗引入之前进行,报告了自然获得的抗体反应数据。79%(141/178)的研究报告了症状发作后一周的敏感性,66%(117/178)报告了恢复期感染的敏感性。研究评估了酶联免疫吸附试验 (ELISA)(165/527;31%)、化学发光测定法 (CLIA)(167/527;32%)或侧向流动测定法 (LFA)(188/527;36%)。由于参与者选择(172,97%);索引测试的应用和解释(35,20%);参考标准的弱点(38,21%);以及参与者流程和时间的问题(148,82%),风险偏倚很高。我们认为,在 170 项(96%)研究中,对证据的适用性存在高度关注,在 162 项(91%)研究中对参考标准的适用性存在高度关注。所有目标抗体的当前 SARS-CoV-2 感染的平均敏感性随着发病后时间的推移而增加。对于 IgG 或 IgM 的组合,在发病第一周的平均敏感性为 41.1%(95%CI 38.1 至 44.2;103 项评估;3881 个样本,1593 例),在发病第二周为 74.9%(95%CI 72.4 至 77.3;96 项评估,3948 个样本,2904 例),在发病后第三周为 88.0%(95%CI 86.3 至 89.5;103 项评估,2929 个样本,2571 例)。在感染的恢复期(最长可达症状发作后 100 天,如有报告),IgG 的平均敏感性为 89.8%(95%CI 88.5 至 90.9;253 项评估,16846 个样本,14183 例),IgG 或 IgM 联合的平均敏感性为 92.9%(95%CI 91.0 至 94.4;108 项评估,3571 个样本,3206 例),总抗体的平均敏感性为 94.3%(95%CI 92.8 至 95.5;58 项评估,7063 个样本,6652 例)。IgM 单独的平均敏感性也呈现出类似的模式,但在每个时间点的检测准确性都较低。平均特异性一直很高且准确,尤其是大流行前样本,这些样本提供了特异性的最低偏差估计(范围从 IgM 的 98.6%到总抗体的 99.8%)。亚组分析表明,检测技术的敏感性和特异性存在微小差异,但研究结果的时间、样本收集时间以及某些组别的样本数量较少,使得比较困难。对于 IgG,CLIA 比 ELISA 和 LFA 更敏感(恢复期感染)和特异性(大流行前样本)(在测试方法之间的差异具有统计学意义,P < 0.001)。使用的抗原(来自 Spike 蛋白或核衣壳)似乎对发病后前几周的平均敏感性有一定影响,但在恢复期感染时没有明显证据表明对敏感性有影响。对品牌进行的检测性能调查显示,不同检测之间的敏感性差异较大,同一研究中评估的同一检测的结果也存在差异。对于评估了 200 个或更多样本的检测,敏感性的 95%CI 下限为 90%或更高的检测数量很少(仅 IgG,n = 5;IgG 或 IgM,n = 1;总抗体,n = 4)。更多的检测品牌符合特异性 98%或更高的 MHRA 最低标准(IgG,n = 16;IgG 或 IgM,n = 5;总抗体,n = 7)。有 7 种检测方法符合敏感性和特异性都符合指定标准。在抗体检测用于诊断症状出现但 PCR 检测为阴性的 COVID-19 患者当前 SARS-CoV-2 感染的低患病率(2%)环境中,我们预计在 1000 名 IgG 或 IgM 检测在 SARS-CoV-2 感染后第三周的人中,将有 1 例(1 至 2 例)病例被漏诊,8 例(5 至 15 例)将出现假阳性。在血清流行率调查中,假设既往感染的流行率为 50%,我们预计在 1000 名 IgG 检测在 SARS-CoV-2 感染恢复期(21 至 100 天发病后或 PCR 阳性后)的人中,将有 51 例(46 至 58 例)病例被漏诊,6 例(5 至 7 例)将出现假阳性。
结论:一些抗体检测可能是
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