Medical Research Council Programme on AIDS in Uganda, Uganda Virus Research Institute, PO Box 49, Entebbe, Uganda.
Cédric Mahé, Unit of Descriptive Epidemiology, International Agency for Research on Cancer, 150, cours Albert Thomas, 69372 Lyon Cedex 08, France. E-mail: mahe{at}iarc.fr
Abstract
Background Western blot (WB) criteria in epidemiological studies in Africa exhibit an unacceptably high proportion of indeterminate results. New diagnostic criteria are urgently needed.
Methods From 1989 to 1998, WB confirmatory tests were performed after weakly positive or discordant results of two enzyme immunoassays in a large Ugandan population. Enzyme immunoassays (EIA) on new sera taken prospectively from the same individuals one year later were used to assess the human immunodeficiency virus (HIV) status of these people. A logistic model was used to determine which set of WB bands was the most predictive of HIV status. Diagnostic criteria were then established, based on the likely HIV status determined using the predictive values and the intensity of the bands.
Results Using 1109 WB tests, the best diagnostic criteria were based on only two bands (gp160 and p31). These criteria were validated on an independent sample of 587 WB tests, giving a high sensitivity and specificity (90.3% and 97.0%, respectively) and few indeterminate results (2.7%). These criteria classified correctly 96.3% of the sera.
Conclusion Our diagnostic criteria gave far better results in our population than the existing published criteria. This suggests that new criteria could be developed to improve WB interpretation in African settings.
Keywords Africa, epidemiology, HIV diagnostic test, Western blot
Accepted 10 June 2002
Human immunodeficiency virus (HIV) is one of the most crucial problems in Africa. Prevalence of HIV is very high in many African countries. For this reason, it is important to have a tool that allows us to give reliable and definitive HIV serology test results. Western blotting is one of the reference confirmatory tests for the diagnosis of HIV infection or after inconclusive EIA results. However, the use of WB is limited by difficulty in their interpretation. Two different diagnostic criteria have already been proposed by the World Health Organization (WHO)1 and the Centers for Disease Control and Prevention (CDC).2 This difficulty, and cost, has led to a reduction in recent years in the use of WB by groups such as WHO which recommend using a combination of EIA which are easier to interpret. Nevertheless, WB tests are still commonly used to confirm results from these EIA. In Africa, the presence of non-specific bands on WB lead to many tests being labelled indeterminate, undermining the use of WB as a diagnostic tool.35 There is no clear biological explanation as to why the host response on WB bands differs in Africans compared to others. Several hypotheses have been advanced including cross-reactivity with malaria, other retroviruses, Herpes simplex virus infection, HIV-2, group O viruses, and anti-HLA antibodies.69
In this paper, we have investigated the possibility of developing simple and reliable diagnostic criteria for the interpretation of confirmatory WB in epidemiological studies in Africa. Specifically, our objective was to determine which set of WB bands are the most predictive of HIV status in a large Ugandan population and to establish appropriate diagnostic criteria, which minimize the proportion of indeterminate results and maximize both the sensitivity and specificity.
Material and Methods
Western blot reading
In brief, a WB test consists of a set of nine HIV-specific bands (gp160, gp120, gp41, p66, p55, p51, p31, p24 and p17). When they are exposed to individual serum, the pattern of reactivity of these bands allows us to determine the HIV status. The method used for the scoring of the bands was that suggested by the commercial kit (Cambridge Biotech HIV-1, Calypte biomedical, Rockville, MD, USA) i.e. we used the WB kit weak positive control strip to score bands for each serum sample: any band stronger than the p24 band of the weak positive control was scored as strong and those similar or weaker were scored as faint. We thus classify band reactivity in three categories (none, faint and strong) in order to quantify the contribution of this reactivity level to the HIV status prediction.
Study sample
The serological laboratory of the Medical Research Council (MRC) Programme on AIDS in Uganda (Uganda Virus Research Institute, Entebbe) conducts HIV testing for a large community-based longitudinal cohort study.10 Since 1989, all consenting adults living in a cluster of 15 villages are bled annually. Compliance to the bleeding is high and the people bled are representative of this population (data not shown). Sera were tested using a set of two independent EIA: Cambridge Recombigen HIV-1/2 (Cambridge Biotech Corp, Cambridge, MA, USA) and Wellcozyme-Murex HIV (Wellcome Diagnostics, Hertford, UK). For sera with inconclusive result by EIA, i.e. discordant or weak positive (optical density/cut-off ratio between 1 and 2 for Cambridge and between 0.5 and 1 for Wellcozyme) EIA results, a WB confirmatory test (Cambridge Biotech HIV-1, Calypte biomedical, Rockville, MD, USA) was performed. Western blot tests were scored independently shortly after testing (because the bands fade over time) by each member of a panel of three experts. The final consensus score was then recorded for each band.
One year later, another serum sample from the same people was taken prospectively and tested by the same two EIA (Figure 1). Enzyme immunoassays are the most common assay used for HIV antibody detection and they are usually very reliable. Nevertheless, to avoid any bias, only people with concordant negative results or concordant strong positive results (optical density/cut-off ratio >2 for Cambridge and <0.5 for Wellcozyme) obtained through the second EIA combination were used for categorizing the HIV status. Most of the people who were seroconverting during the first combination of EIA are thus considered as positive. In this way, we do not discard the effect of bands, which may be predictive of seroconversion. Misclassified people (uninfected at the time the WB sample was taken, but infected and seropositive at the time the last EIA sample was taken) are expected to be low in this population since the incidence rate is less than 1%.10 Data presented in this study are all the WB tests performed between December 1989 and November 1998 on this population. All the participants in the study were invited to receive their HIV results after counselling.
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A logistic model was constructed to predict the HIV status (positive = 1 and negative = 0) as assessed by the second set of EIA. The covariates included in this model were the nine WB bands according to their reactivity level. To take into account the hierarchical property of the reactivity to a band (none faint
strong), encoding of the band covariates was thus realized according to this scheme:
Using this encoding, within a band, each regression coefficient associated with one of the covariates represents the effect of an additional reactivity level on HIV status (e.g. strong relative to faint if we consider covariate 2). This thus implies that the regression coefficients should be positive or null (hierarchical trend); such constraints have thus been added in the model. Since several sera can pertain to a same individual, a correlation between these samples was taken into account using a robust variance-covariance matrix. A univariate logistic regression analysis using each band in turn was first performed in order to determine their predictive value and to assess which level of reactivity is significantly meaningful regarding HIV status. The multivariate analysis selected the best model combining bands together, on the basis of the highest likelihood. The goodness-of-fit of the model was also assessed using Pearson goodness-of-fit test. In fact, this model assigns a different weight to each band/reactivity level. Using the probability of being HIV-positive predicted by the multivariate model according to the bands reactivity level, cut-off points were selected to obtain appropriate diagnostic criteria minimizing the number of indeterminate tests and maximizing sensitivity and specificity.
The resulting diagnostic criteria were applied to the validation sample in order to assess its performance on a different and independent sample. This was compared with the standard WHO and CDC criteria.
Results
Construction sample
Of the 1696 WB tests, 1109 (65.3%) were randomly selected to construct the diagnostic criteria. In this sample, 92 people had more than one test (79 had 2 tests, 8 had 3, 4 had 4 and 3 had 5), and 530 were women (47.9%). The median age of the people associated with the tested sera was 27 (inter-quartile range [IQR]: 13, 45). The median gap between the WB test and the EIA was 1.0 year (IQR: 0.96, 1.04). In all, 124 (11.2%) of the 1109 sets of EIA tests done subsequently were positive. The distribution of the band reactivity results of the WB is presented in Table 1. Relatively few faint reactivities were observed for envelope bands (gp160, gp120 and gp41) and p31 (about 2%). On the other hand, a frequent reactivity (faint or strong) to p24 and p17 was observed (48% and 29%, respectively).
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The indicator function I{gp160 faint} means equal to 1 if reactivity to gp160 is faint or strong, 0 otherwise.
As an example, using this formula, a strong reactivity to gp160 and a faint reactivity to p31 will give a probability of being HIV+ of p where:
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Using this model, we constructed Table 2 that represents the predicted probability of being HIV+ according to the observed reactivities to gp160 and p31.
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The diagnostic criteria we have constructed can be summarized as:
Validation
The validation dataset was constituted of the remaining 587 sera (34.7% of the whole dataset) not used to construct the diagnostic criteria. In this demographically similar sample, 62 (10.6%) of the 587 subsequent EIA tests were positive. Applying our diagnostic criteria to these sera, we obtained a high sensitivity (90.3%), a high specificity (97.0%) and very few indeterminate results (2.7%). These diagnostic criteria classify correctly 96.3% of the sera.
Comparison with existing published criteria
Existing reference criteria (CDC and WHO)1,2 were applied to the validation sample in order to compute their accuracy and to compare their performances with our revised criteria. The results are summarized in Table 3. The WHO and CDC reference criteria both had slightly better sensitivity but very poor specificity compared with our revised criteria. They exhibit a very high proportion of indeterminate test results and thus a low proportion of correctly classified samples.
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Discussion
In epidemiology, missing values may induce a bias because they are rarely random. The usual criteria used by clinicians for individual patients are designed to be conservative to be sure of the HIV status. In this case many indeterminate results may be acceptable. In epidemiology, completeness is more important. Indeed, a small number of discrepancies do not induce a bias by comparison with the size of the overall population. We have developed for epidemiological studies simple WB confirmatory diagnostic criteria based on only two bands (gp160 and p31) that give better results in an African population than the existing criteria involving all nine bands of the WB. The first reason for the increased efficiency of these criteria is that we have taken into account the different levels of reactivity within a band, which we have shown are meaningful for predicting HIV status. Levels of reactivity have also recently been incorporated in the Calypte biomedical manual (Rockville, MD, USA). In addition, using our multivariate model, we have given a different weight to each band according to its predictive value. Indeed, there is no reason, as has already been indicated by others,11 why each band should have the same predictive value regarding HIV infection.
If WB is being used to resolve indeterminate EIA results, having many WB giving indeterminate results is a waste of money and energy. However, the conditions needed to be classified as negative in existing criteria are too restrictive for African populations, leading to many indeterminate results. Usually, samples with reactivity to bands such as p55, p66 or p51 are classified as indeterminate when they are in fact negative in more than 98% of cases in our population. This leads to a high percentage of indeterminate tests and thus a low percentage of correctly classified samples. Known predictive bands like gp120 and gp41 were not included in the multivariate model because they were too highly correlated with gp160, so that their addition did not add useful information. Indeed, of the 209 reactivities observed on gp120 and/or gp41 in our population, 93% had a reactivity to gp160. In our population, the p31 band is useful in predicting HIV infection. A high proportion of false positive results among sera without reactivity to p31 have already been reported.12
The same testing algorithm was used throughout the study.13 The quality of this algorithm was routinely checked (twice a year) using the CDC Model Performance Evaluation Program (MPEP). Western blot was only performed after discordant EIA results according to this algorithm. Because we were focussing on people with concordant EIA results at the second test, WB was not performed after this test. However in our study, people with clear positive EIA results for the first time were tested by WB and there was a high concordance between EIA and WB results. Nevertheless, we did not do WB on any other clear positive or negative sample by EIA. We thus do not have any data to show there was a high concordance in these circumstances.
The samples were taken from a general population cohort, used to monitor prevalence and incidence in rural Uganda.14 The size of the population bled was on average 4650 each year and the prevalence rate varied between 6.2 and 7.5% in the last 10 years.14 Because it is a cohort followed up for more than 10 years, most of the positive or negative samples were confirmed at later times (a longitudinal consistency check of the HIV results was annually performed). The natural history of the disease in this population is comparable to that in industrialized countries before the introduction of highly active antiretroviral therapy (HAART), but with a higher morbidity due to the high prevalence of underlying infections in the overall population.15,16 If we consider the subsample of this cohort for whom we have details on natural history (N = 236), few were in late stage disease: 40.7% were in stage 1, 29.9% in stage 2, 27.1% in stage 3 and 12.3% in stage 4. In our population, out of 55 people with reactivity to p31 and documented HIV infection, 44% were in late disease stage (WHO clinical stage 3 or 4), suggesting that the loss of antibody reaction to p31 or other antigens reported in late stage disease observed in developed countries is not frequent in Africa (Calypte biomedical manual). The problem is rather that we tend to observe reactivity to some antigens even in HIV-negative people. Uganda is a country where subtype A and D are roughly equally distributed among the population.17 It is possible that HIV-1 subtype could impact the sensitivity and specificity in specific WB bands. Nevertheless, we think this is unlikely because the antigens used in WB are large molecules with different conserved regions that are recognized by all subtypes.
Because the construction sample was different from the validation sample, we can assume that our model is robust. Using criteria based on all nine bands, one would tend to obtain diagnostic criteria that are highly specific for the study population but not reproducible in other populations. For this reason, our simple criteria involving only two bands can probably be more easily applied to other African populations than existing criteria, but this needs to be confirmed. This study suggests that alternative WB criteria for Africa can be very effective. Further research should determine new criteria that could be successfully applied, for epidemiological purposes, in African populations.
KEY MESSAGES
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The authors would thank Aristides Triantafillidis for some preliminary analyses done on this study sample.
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