The diagnostic accuracy of serological tests for Lyme borreliosis in Europe: a systematic review and meta-analysis

Lyme borreliosis is one of the most prevalent vector-borne diseases in Europe. Its incidence varies between countries, with approximately 65,500 patients annually in Europe (estimated in 2009) [1]. It is caused by spirochetes of the Borrelia burgdorferi sensu lato species complex, which are transmitted by several species of Ixodid ticks [2]. In Europe, at least five genospecies of the Borrelia burgdorferi sensu lato complex can cause disease, leading to a variety of clinical manifestations including erythema migrans (EM), neuroborreliosis, arthritis and acrodermatitis chronica atrophicans (ACA). Each of these clinical presentations can be seen as a distinct target condition, i.e. the disorder that a test tries to determine, as they affect different body parts and different organ systems, and because the patients suffering from these conditions may enter and travel through the health care system in different ways, hence following different clinical pathways.

The diagnosis of Lyme borreliosis is based on the presence of specific symptoms, combined with laboratory evidence for infection. Laboratory confirmation is essential in case of non-specific disease manifestations. Serology is the cornerstone of Lyme laboratory diagnosis, both in primary care and in more specialized settings. Serological tests that are most often used are enzyme-linked immunosorbent assays (ELISAs) or immunoblots. ELISAs are the first test to be used; immunoblots are typically applied only when ELISA was positive. If signs and symptoms are inconclusive, the decision may be driven by the serology test results. In such a situation, patients may be treated with antibiotics after a positive serology result – a positive ELISA possibly followed by a positive immunoblot. After negative serology – a negative ELISA or a positive ELISA followed by a negative immunoblot – patients will not be treated for Lyme borreliosis, but they will be followed up or referred for further diagnosis. This implies that false positively tested patients (who have no Lyme borreliosis, but have positive serology) will be treated for Lyme borreliosis while they have another condition. It also implies that falsely negative tested patients (who have the disease, but test negative) will not be treated for Lyme borreliosis. A test with a high specificity – which is the percentage true negative results among patients without the target condition – will result in a low percentage of false positives. A test with a high sensitivity – being the percentage true positives among patients with the target condition – will result in a low percentage of false negatives.

The interpretation of serology results is complicated. The link between antibody status and actual infection may not be obvious: non-infected people may have immunity and test positive, while infected people may have a delay in their antibody response and may test negative. Furthermore, there is an overwhelming number of different available assays that have all been evaluated in different patient populations and settings and that may perform differently for the various disease manifestations [3]. We therefore systematically reviewed all available literature to assess the accuracy (expressed as sensitivity and specificity) of serological tests for the diagnosis of the different manifestations of Lyme borreliosis in Europe. Our secondary aim was to investigate potential sources of heterogeneity, for example test-type, whether the test was a commercial test or an in-house test, publication year and antigens used.

We searched EMBASE and Medline (Appendix 1) and contacted experts for studies evaluating serological tests against a reference standard. The reference standard is the test or testing algorithm used to define whether someone has Lyme borreliosis or not. We included studies using any reference standard, but most studies used clinical criteria, sometimes in combination with serology. Studies performed in Europe and published in English, French, German, Norwegian, Spanish and Dutch were included.

The ideal study type to answer our question would be a cross-sectional study, including a series of representative, equally suspected patients who undergo both the index test and the reference standard [4]. Such studies would provide valid estimates of sensitivity and specificity and would also directly provide estimates of prevalence and predictive values. However, as we anticipated that these cross-sectional studies would be very sparse, we decided to include case-control studies or so-called two-gate designs as well [5]. These studies estimate the sensitivity of a test in a group of cases, i.e. patients for whom one is relatively sure that they have Lyme borreliosis. They estimate the specificity in a group of controls, i.e. patients of whom one is relatively sure that they do not have Lyme borreliosis. These are healthy volunteers, or patients with other diseases than Lyme.

We included studies on ELISAs, immunoblots, two-tiered testing algorithms of an ELISA followed by an immunoblot, and specific antibody index measurement (calculated using the antibody titers in both serum and cerebrospinal fluid). We excluded indirect fluorescent antibody assays, as these are rarely used in practice. Studies based on make-up samples were excluded. We also excluded studies for which 2 × 2 tables could not be inferred from the study results.

For each article, two authors independently collected study data and assessed quality. We assessed the quality using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) checklist. This checklist consists of four domains: patient selection, index test, reference standard and flow and timing [6]. Each of these domains has a sub-domain for risk of bias and the first three have a sub-domain for concerns regarding the applicability of study results. The sub-domains about risk of bias include a number of signalling questions to guide the overall judgement about whether a study is highly likely to be biased or not (Appendix 2).

We analysed test accuracy for each of the manifestations of Lyme borreliosis separately and separately for case-control designs and cross-sectional designs. If a study did not distinguish between the different manifestations, we used the data of this study in the analysis for the target condition “unspecified Lyme”. Serology assays measure the level of immunoglubulins (Ig) in the patient’s serum. IgM is the antibody most present in the early stages of disease, while IgG increases later in the disease. Some tests only measure IgM, some only IgG and some tests measure any type of Ig. In some studies, the accuracy was reported for IgM only, IgG only and for detection of IgG and IgM. In those cases, we included the data for simultaneous detection of both IgG and IgM (IgT).

We meta-analyzed the data using the Hierarchical Summary ROC (HSROC) model, a hierarchical meta-regression method incorporating both sensitivity and specificity while taking into account the correlation between the two [7]. The model assumes an underlying summary ROC curve through the study results and estimates the parameters for this curve: accuracy, threshold at which the tests are assumed to operate and shape of the curve. Accuracy is a combination of sensitivity and specificity; the shape of the curve provides information about how accuracy varies when the threshold varies. From these parameters we derived the reported sensitivity and specificity estimates. We used SAS 9.3 for the analyses and Review Manager 5.3 for the ROC plots.

There is no recommended measure to estimate the amount of heterogeneity in diagnostic accuracy reviews, but researchers are encouraged to investigate potential sources of heterogeneity [7]. The most prominent source of heterogeneity is variation in threshold, which is taken into account by using the HSROC model. Other potential sources of heterogeneity are: test type (ELISA or immunoblot); a test being commercial or not; immunoglobulin type; antigen used; publication year; late versus early disease; and study quality. These were added as covariates to the model to explain variation in accuracy, threshold or shape of the curve.

Some studies reported results for patients with “possible Lyme” (i.e. no clear cases, neither clear controls). We included these as cases. As this may lead to underestimation of sensitivity, we investigated the effect of this approach. Borderline test results were included in the test-positive group.

Overall, the diagnostic accuracy of ELISAs and immunoblots for Lyme borreliosis in Europe varies widely, with an average sensitivity of ~80 % and a specificity of ~95 %. For Lyme arthritis and ACA the sensitivity was around 95 %. For EM the sensitivity was ~50 %. In cross-sectional studies of neuroborreliosis and unspecified Lyme borreliosis, the sensitivity was comparable to the case-control designs, but the specificity decreased to 78 and 77 % respectively. Two-tiered tests did not outperform single tests. Specific antibody index tests did not outperform the other tests for neuroborreliosis, although the specificity remained high even in the cross-sectional designs. All results should be interpreted with caution, as the results showed much variation and the included studies were at high risk of bias.

Although predictive values could not be meta-analyzed, the sensitivity and specificity estimates from this review may be used to provide an idea of the consequences of testing when the test is being used in practice. Imagine that a clinician sees about 1000 people a year who are suspected of one of the manifestations of Lyme borreliosis, in a setting where the expected prevalence of that manifestation is 10 %. A prevalence of 10 % would mean that 100 out of 1000 tested patients will really have a form of Lyme borreliosis. If these people are tested by an ELISA with a sensitivity 80 %, then 0.80*100 = 80 patients with Lyme borreliosis will test positive and 20 patients will test negative. If we assume a specificity of 80 % as well (following the estimates from the cross-sectional designs), then out of the 900 patients without Lyme borreliosis, 0.80*900 = 720 will test negative and 180 will test positive. These numbers mean that in this hypothetical cohort of 1000 tested patients, 80 + 180 = 260 patients will have a positive test result. Only 80 of these will be true positives and indeed have Lyme borreliosis (positive predictive value 80/260 = 0.31 = 31 %). The other 180 positively tested patients are the false positives and they will be treated for Lyme while they have another cause of disease, thus delaying their final diagnosis and subsequent treatment. In a two-tiered approach, all positives will be tested with immunoblot after ELISA. These numbers also mean that we will have 720 + 20 = 740 negative test results, of which 3 % (negative predictive value 720/740 = 0.97 = 97 %) will have Lyme borreliosis despite a negative test result. These are the false-negatives, their diagnosis will be missed or delayed. Although calculations like these may provide insight in the consequences of testing, they should be taken with caution. The results were overall very heterogeneous and may depend on patient characteristics. Also, the prevalence of 10 % may not be realistic. In our review, we found prevalences ranging from 1 to 79 % for unspecified Lyme borreliosis and ranging from 12 to 62 % for neuroborreliosis. Appendix 3 shows some more of these inferences, for different prevalence situations and different sensitivity and specificity of the tests.

Limitations of this review are the representativeness of the results, the poor reporting of study characteristics and the lack of a true gold standard. Most studies included were case-control studies. These may be easier to perform in a laboratory setting than cross-sectional designs, but their results are less representative for clinical practice. Also the immunoblot was not analysed in a way that is representative for practice: most immunoblots were analysed on the same samples as the ELISAs, while in practice immunoblots will only be used on ELISA-positive samples. EM patients formed the second largest group of patients in our review. The low sensitivity in this group of patients supports the guidelines stating that serological testing in EM patients is not recommended [86]. On the other hand, patients with atypical manifestations were not included in the reviewed studies, while this group of patients does form a diagnostic problem [87, 88]. A more detailed analyses of the included patients’ characteristics and test characteristics would have been nice, but these characteristics were poorly reported. This is also reflected in the Quality-assessment table, with many ‘unclear’ scores, even for more recent studies. Authors may not have been aware of existing reporting guidelines and we therefore suggest that authors of future studies use the STAndards for Reporting Diagnostic accuracy studies (STARD) to guide their manuscript [89].

There is no gold standard for Lyme borreliosis, so we used the reference standard as presented by the authors of the included studies. This may have added to the amount of variation. Furthermore, many of the investigated studies included the results from antibody testing in their definition of Lyme borreliosis, which may have overestimated sensitivity and specificity. However, this was not proven in our heterogeneity analyses.

The performance of diagnostic tests very much depends on the population in which the test is being used. Future studies should therefore be prospective cross-sectional studies including a consecutive sample of presenting patients, preferably stratified by the situation in which the patient presents (e.g. a tertiary Lyme referral center versus general practice). The lack of a gold standard may be solved by using a reference standard with multiple levels of certainty [90, 91]. Although this will diminish contrasts and will thus be more difficult to interpret, it does reflect practice in a better way. Other solutions may be more statistically derived approaches like latent class analysis, use of expert-opinion and/or response to treatment [92].

However, more and better designed diagnostic accuracy studies will not improve the accuracy of these tests themselves. They will provide more valid estimates of the tests’ accuracy, including predictive values, but the actual added value of testing for Lyme disease requires information about subsequent actions and consequences of testing. If the final diagnosis or referral pattern is solely based upon the clinical picture, then testing patients for Lyme may have no added value. In that case, a perfect test may still be useless if it does not change clinical management decisions [93]. On the other hand, imperfect laboratory tests may still be valuable for clinical decision making if subsequent actions improve the patient’s outcomes. The challenge for clinicians is to deal with the uncertainties of imperfect laboratory tests.

We found no evidence that ELISAs have a higher or lower accuracy than immunoblots; neither did we find evidence that two-tiered approaches have a better performance than single tests. However, the data in this review do not provide sufficient evidence to make inferences about the value of the tests for clinical practice. Valid estimates of sensitivity and specificity for the tests as used in practice require well-designed cross-sectional studies, done in the relevant clinical patient populations. Furthermore, information is needed about the prevalence of Lyme borreliosis among those tested for it and the clinical consequences of a negative or positive test result. The latter depend on the place of the test in the clinical pathway and the clinical decisions that are driven by the test results or not. Future research should primarily focus on more targeted clinical validations of these tests and research into appropriate use of these tests.

The raw data (data-extraction results, reference lists, statistical codes) will be provided upon request by ECDC (info@ecdc.europa.eu).