【Cochrane简语概要】抗体测试检测COVID-19病毒感染的诊断准确性如何？

研究背景

COVID-19是一种由SARS-CoV-2病毒引起的传染病，它很容易在人与人之间传播，传播方式类似于普通感冒或流感。COVID-19患者多数有轻度至中度呼吸道疾病，有些人可能没有症状（无症状感染）。有的人则出现严重症状，需要专科治疗和重症监护。

COVID-19患者的免疫系统会通过产生可攻击血液中病毒的蛋白质（抗体）来应对感染。检测人们血液中抗体的测试可能会显示他们目前是否感染了COVID-19或以前是否曾被感染。

为什么准确的测试很重要？

准确的测试可以识别可能需要治疗的人，或者需要自我隔离以防止感染传播的人。如果未能及时发现COVID-19患者(假阴性结果)，可能会延误治疗，并有进一步感染他人的风险。当COVID-19不存在时，错误地识别COVID-19（假阳性结果）可能会导致不必要的进一步检测、治疗，以及隔离当事人和密切接触者。正确识别曾感染过COVID-19的人，有助于评估疾病传播、评估公共卫生干预措施（如隔离）的成功与否，以及潜在地识别具有免疫力的人群（如果将来能以抗体表明具有免疫力的话）。

为了识别假阴性和假阳性结果，对已知COVID-19感染者和未感染者的抗体检测结果进行比较。根据“参考标准”，将受试者分为已知COVID-19感染者或未感染者两组。许多研究使用从鼻咽和口咽采集的样本来识别COVID-19。样品经过称为逆转录酶聚合酶链反应（reverse transcriptase polymerase chain reaction，RT-PCR）的测试。该测试过程有时可能会漏诊感染（假阴性结果），但进一步的测试可以在RT-PCR测试呈阴性的人中识别COVID-19感染。这些措施包括评估临床症状（例如咳嗽或高热）或影像检查（例如胸部X光片）。有时会从COVID-19出现之前采集的血液样本中，或从由其他疾病引起呼吸道症状的患者中识别出未感染COVID-19的人。

本系统综述研究了什么？

研究专注于三种类型的抗体：IgA，IgG和IgM。大多数测试都同时检测IgG和IgM，但有些测试则测量单个抗体或这三种抗体的组合。

感染后抗体在不同时间升高和降低。IgG是最后上升的抗体，但持续时间最长。抗体水平通常在感染后几周内达到最高。

一些抗体测试需要专门的实验室设备。另一些则使用类似于妊娠试验的一次性装置。这些检测可在实验室或病人所在的任何地方（医疗点）、医院或家中使用。

我们想确定抗体测试是否：

- 足够准确地诊断出有或没有COVID-19症状的人的感染情况，以及

- 可用于确定某人是否已经感染过COVID-19。

我们做了什么？

我们检索评估抗体测试准确性的研究，这些研究以抗体测试检测当前或过去是否感染COVID-19，并将结果与参考标准相比较。这些研究可以是将任何抗体测试与任何参考标准进行比较。人们可以在医院或社区接受检测。研究可以检测COVID-19已知感染者、未感染者或疑似感染者。

研究特征

我们共纳入了54项相关研究。研究在亚洲（38个），欧洲（15个）以及美国和中国（1个）进行。

46项研究只纳入了在医院中疑似或确诊COVID-19的感染者。29项研究比较了COVID-19患者与健康人或其他疾病患者的抗体检测结果。

并非所有研究都提供了有关受试者年龄和性别的详细信息。通常，我们无法判断研究是评估当前还是过去的感染，因为很少有研究报告受试者是否正在康复。我们没有发现任何仅测试无症状人群的研究。

主要结果

我们的研究结果主要来自38项研究，这些研究提供的结果是基于人们首次发生症状的时间。

首次出现症状一周后的抗体测试仅检测到30%的COVID-19患者。准确度在第2周有所提高，检测到70％，在第3周最高（检测到90％以上）。几乎没有第3周后的证据。在COVID-19未感染者的测试中，有2%的人检测结果为假阳性。

症状开始三周后，IgG/IgM测试的结果表明，如果有1000人进行了抗体测试，而其中有50（5％）确实感染了COVID-19（正如我们在全国筛查调查中所期望的那样）：

- 58人的COVID-19测试结果呈阳性。其中，有12人（21％）没有COVID-19（假阳性结果）。

- 942人的COVID-19测试结果呈阴性。其中，有4人（0.4％）实际感染了COVID-19（假阴性结果）。

如果我们对1000名出现过症状的医护人员（在高危环境下）进行测试，其中500人（50%）确实感染了COVID-19：

- 461人的COVID-19测试结果呈阳性。其中，有7人（2％）没有感染COVID-19（假阳性结果）。

- 537人的COVID-19测试结果为阴性。其中，有43人（8％）实际感染了COVID-19（假阴性结果）。

对于不同类型的抗体测试，我们没有发现的令人信服的准确性方面的差异。

本系统综述中各项研究结果的可信度如何？

由于以下几个原因，我们对证据的信心有限。总的来说，研究规模很小，没有使用最可靠的方法，也没有充分报告其结果。通常这些研究未纳入可能在PCR上产生假阴性结果的COVID-19患者，并从出现COVID-19之前进行的测试记录中获取了未感染者的资料。这可能会影响测试的准确性，但无法确定有多大的影响。

本系统综述的结果适用于谁？

大多数受试者都因COVID-19住院，因此与没有住院的症状轻微的人相比，他们的病情可能更严重。这意味着我们不知道对于轻症或无症状的人来说，抗体测试有多准确。

超过一半的研究评估了他们自己开发的测试，其中大多数都无法购买。许多研究作为“预印本”在网上迅速发表。预印本没有经过如已发表研究的严格检查，因此我们不确定它们的可靠性。

由于大多数研究都是在亚洲进行的，因此我们不知道测试结果在世界其他地方是否会相似。

本系统综述的意义是什么？

这项系统综述表明，抗体测试在检测是否感染COVID-19方面可能发挥有用的作用，但使用这些测试的时间很重要。抗体测试可能有助于确认症状超过两周且未进行RT-PCR测试或RT-PCR测试结果阴性的人是否感染COVID-19。抗体测试更适用于在症状开始两周或更长时间后检测是否感染COVID-19，但我们不知道在症状开始五周后测试的效果如何。我们不知道这些测试对轻症或无症状者的效果如何，因为本系统综述中的研究主要是在住院患者中进行的。未来，我们将了解已感染过COVID-19的人群是否对未来的感染具有免疫力。

对于COVID-19感染恢复期的人群，轻症以及无症状病人使用抗体测试的情况，还需要进一步研究。

本系统综述的时效性如何?

本系统综述纳入截至2020年4月27日发表的证据。由于该领域有许多新研究正在发表，因此我们经常更新系统综述。

（图片来源于网络）

作者结论: 

在症状出现后的第一周，抗体检测的敏感度太低，无法对COVID-19的诊断发挥主要作用，但在RT-PCR检测为阴性或未进行检测的个体中，抗体检测仍可作为其他检测的补充。如果在症状出现后15天或更长时间内使用抗体检测，则可能对检测以前的SARS-CoV-2感染有一定的作用。但是，目前尚不清楚抗体增加的持续时间，并且我们发现症状出现后超过35天的资料很少。因此，我们不能确定这些检测是否适用于公共卫生管理目的的血清效价调查。由于担心存在高风险偏倚和适用性，临床医疗中使用的检测准确度可能低于纳入研究的报告。敏感度主要在住院患者中进行了评估，因此尚不清楚该测试是否能够检测到在轻症和无症状COVID-19感染者中可能出现的较低抗体水平。

COVID-19测试准确性研究的设计，执行和报告需要大幅度改进。研究必须报告自出现症状以来按时间分列的敏感度数据。根据世界卫生组织（World Health Organization，WHO）和中华人民共和国国家卫生健康委员会（China National Health Commission of the People's Republic of China，CDC）的病例定义，应纳入RT-PCR呈阴性的COVID-19阳性病例以及经RT-PCR确诊的病例。我们只能从一小部分可用测试中获取数据，并且需要采取措施以确保所有测试评估结果都可以在公共领域获得，以防止选择性报告。这是一个快速发展的领域，我们计划对该实时系统综述进行持续更新。

译者：赵洁；审校：刘旭，香港中文大学那打素护理学院；编辑排版：张晓雯、郑偌祥，北京中医药大学循证医学中心

相关文章链接

【Cochrane Plain Language Summary】What is the diagnostic accuracy of antibody tests for the detection of infection with the COVID-19 virus?

Background

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus that spreads easily between people in a similar way to the common cold or ‘flu. Most people with COVID-19 have a mild to moderate respiratory illness, and some may have no symptoms (asymptomatic infection). Others experience severe symptoms and need specialist treatment and intensive care.

The immune system of people who have COVID-19 responds to infection by developing proteins that can attack the virus (antibodies) in their blood. Tests to detect antibodies in peoples' blood might show whether they currently have COVID-19 or have had it previously.

Why are accurate tests important?

Accurate testing allows identification of people who might need treatment, or who need to isolate themselves to prevent the spread of infection. Failure to detect people with COVID-19 when it is present (a false negative result) may delay treatment and risk further spread of infection to others. Incorrect identification of COVID-19 when it is not present (a false positive result) may lead to unnecessary further testing, treatment, and isolation of the person and close contacts. Correct identification of people who have previously had COVID-19 is important in measuring disease spread, assessing the success of public health interventions (like isolation), and potentially in identifying individuals with immunity (should antibodies in the future be shown to indicate immunity).

To identify false negative and false positive results, antibody test results are compared in people known to have COVID-19 and known not to have COVID-19. Study participants are classified as to whether they are known or not known to have COVID-19 based on criteria known as the ‘reference standard’. Many studies use samples taken from the nose and throat to identify people with COVID-19. The samples undergo a test called reverse transcriptase polymerase chain reaction (RT-PCR). This testing process can sometimes miss infection (false negative result), but additional tests can identify COVID-19 infection in people with a negative RT-PCR result. These include measuring clinical symptoms, like coughing or high temperature, or ‘imaging’ tests like chest X-rays. People known not to have COVID-19 are sometimes identified from stored blood samples taken before COVID-19 existed, or from patients with respiratory symptoms found to be caused by other diseases.

What did the review study?

The studies looked at three types of antibody, IgA, IgG and IgM. Most tests measure both IgG and IgM, but some measure a single antibody or combinations of the three antibodies.

Levels of antibodies rise and fall at different times after infection. IgG is the last to rise but lasts longest. Levels of antibodies are usually highest a few weeks after infection.

Some antibody tests need specialist laboratory equipment. Others use disposable devices, similar to pregnancy tests. These tests can be used in laboratories or wherever the patient is (point-of-care), in hospital or at home.

We wanted to find out whether antibody tests:

- are accurate enough to diagnose infection in people with or without symptoms of COVID-19, and

- can be used to find out if someone has already had COVID-19.

What did we do?

We looked for studies that measured the accuracy of antibody tests compared with reference standard criteria to detect current or past COVID-19 infection. Studies could assess any antibody test compared with any reference standard. People could be tested in hospital or the community. Studies could test people known to have – or not to have – or be suspected of having COVID-19.

Study characteristics

We found 54 relevant studies. Studies took place in Asia (38), Europe (15), and in both USA and China (1).

Forty-six studies included people who were in hospital with suspected or confirmed COVID-19 infection only. Twenty-nine studies compared test results in people with COVID-19 with test results in healthy people or people with other diseases.

Not all studies provided details about participants’ age and gender. Often, we could not tell whether studies were evaluating current or past infection, as few reported whether participants were recovering. We did not find any studies that tested only asymptomatic people.

Main results

Our findings come mainly from 38 studies that provided results based on the time since people first noticed symptoms.

Antibody tests one week after first symptoms only detected 30% of people who had COVID-19. Accuracy increased in week 2 with 70% detected, and was highest in week 3 (more than 90% detected). Little evidence was available after week 3. Tests gave false positive results in 2% of those without COVID-19.

Results from IgG/IgM tests three weeks after symptoms started suggested that if 1000 people had antibody tests, and 50 (5%) of them really had COVID-19 (as we might expect in a national screening survey):

- 58 people would test positive for COVID-19. Of these, 12 people (21%) would not have COVID-19 (false positive result).

- 942 people would test negative for COVID-19. Of these, 4 people (0.4%) would actually have COVID-19 (false negative result).

If we tested 1000 healthcare workers (in a high-risk setting) who had had symptoms, and 500 (50%) of them really had COVID-19:

- 464 people would test positive for COVID-19. Of these, 7 people (2%) would not have COVID-19 (false positive result).

- 537 people would test negative for COVID-19. Of these, 43 (8%) would actually have COVID-19 (false negative result).

We did not find convincing differences in accuracy for different types of antibody test.

How reliable were the results of the studies of this review?

Our confidence in the evidence is limited for several reasons. In general, studies were small, did not use the most reliable methods and did not report their results fully. Often, they did not include patients with COVID-19 who may have had a false negative result on PCR, and took their data for people without COVID-19 from records of tests done before COVID-19 arose. This may have affected test accuracy, but it is impossible to identify by how much.

Who do the results of this review apply to?

Most participants were in hospital with COVID-19, so were likely to have more severe disease than people with mild symptoms who were not hospitalised. This means that we don't know how accurate antibody tests are for people with milder disease or no symptoms.

More than half of the studies assessed tests they had developed themselves, most of which are not available to buy. Many studies were published quickly online as ‘preprints’. Preprints do not undergo the normal rigorous checks of published studies, so we are not certain how reliable they are.

As most studies took place in Asia, we don't know whether test results would be similar elsewhere in the world.

What are the implications of this review?

The review shows that antibody tests could have a useful role in detecting if someone has had COVID-19, but the timing of when the tests are used is important. Antibody tests may help to confirm COVID-19 infection in people who have had symptoms for more than two weeks and do not have a RT-PCR test, or have negative RT-PCR test results. The tests are better at detecting COVID-19 in people two or more weeks after their symptoms started, but we do not know how well they work more than five weeks after symptoms started. We do not know how well the tests work for people who have milder disease or no symptoms, because the studies in the review were mainly done in people who were in hospital. In time, we will learn whether having previously had COVID-19 provides individuals with immunity to future infection.

Further research is needed into the use of antibody tests in people recovering from COVID-19 infection, and in people who have experienced mild symptoms or who never experienced symptoms.

How up-to-date is this review?

This review includes evidence published up to 27 April 2020. Because a lot of new research is being published in this field, we will update this review frequently.

Authors' conclusions: 

The sensitivity of antibody tests is too low in the first week since symptom onset to have a primary role for the diagnosis of COVID-19, but they may still have a role complementing other testing in individuals presenting later, when RT-PCR tests are negative, or are not done. Antibody tests are likely to have a useful role for detecting previous SARS-CoV-2 infection if used 15 or more days after the onset of symptoms. However, the duration of antibody rises is currently unknown, and we found very little data beyond 35 days post-symptom onset. We are therefore uncertain about the utility of these tests for seroprevalence surveys for public health management purposes. Concerns about high risk of bias and applicability make it likely that the accuracy of tests when used in clinical care will be lower than reported in the included studies. Sensitivity has mainly been evaluated in hospitalised patients, so it is unclear whether the tests are able to detect lower antibody levels likely seen with milder and asymptomatic COVID-19 disease.

The design, execution and reporting of studies of the accuracy of COVID-19 tests requires considerable improvement. Studies must report data on sensitivity disaggregated by time since onset of symptoms. COVID-19-positive cases who are RT-PCR-negative should be included as well as those confirmed RT-PCR, in accordance with the World Health Organization (WHO) and China National Health Commission of the People's Republic of China (CDC) case definitions. We were only able to obtain data from a small proportion of available tests, and action is needed to ensure that all results of test evaluations are available in the public domain to prevent selective reporting. This is a fast-moving field and we plan ongoing updates of this living systematic review.

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