Methods to discriminate primary from secondary dengue during acute symptomatic infection

Dengue is the most widely distributed mosquito-borne human viral disease and represents a major public health burden globally. An estimated 390 million infections occur each year, of which around 100 million are symptomatic [1]. Although the majority of symptomatic individuals recover after a short illness, a small proportion of patients develop severe complications that can be life threatening. There are four dengue viral serotypes (DENV1–4), all of which may cause severe disease. Although the pathogenesis of severe dengue remains incompletely understood, it is clear that following an initial (primary) infection with one viral serotype, a subsequent infection with a different serotype (secondary infection) is more likely to result in severe disease [2, 3]. A number of virological and immunological parameters that are thought to contribute to dengue pathogenesis differ between individuals with primary and secondary infections [4–7], and differentiating between primary and secondary dengue is important for pathogenesis and epidemiological research. However, it also has potential utility in clinical practice, especially early in the disease evolution when knowledge of the immune status of a confirmed dengue case could help clinicians decide on the need for hospitalisation or frequency of follow-up, and might improve the performance of risk prediction algorithms for severe disease.

Several serological methods have been developed to categorise dengue infections as either primary or secondary. Although Haemagglutination Inhibition Assays (HI assays) have been traditionally considered the gold standard, the technique is complicated and time-consuming to perform, and requires experienced technical staff and samples collected in late convalescence. By comparison, serological testing using ELISA (enzyme-linked immunosorbent assay) techniques to measure IgM and/or IgG levels is technically much simpler, and a number of algorithms now use ELISA titres measured on a single specimen to define immune status. However, the range of techniques and definitions used is highly variable including: Capture IgM/IgG ratios greater than 1.78 [8], 1.2 [9] and 1.4 [10] to define primary infections; Capture IgG/IgM ratios greater than 1.10 [11] and 1.14 [12] to define secondary infections; NS1 specific IgG titres [13], absolute IgG titres [14] and/or measures of IgG avidity [15–17]; and Indirect IgG ELISA results [18]. In most of these reported studies only small numbers of participants were involved and the data used to define the outcome (primary versus secondary disease), and to develop the corresponding diagnostic algorithm, relied on serological tests performed on the same samples; while the specific tests were usually different, some degree of linkage is inevitable given the coordinated nature of immune responses to infection within individuals. In addition the available algorithms have only occasionally accounted for the known kinetics of antibody responses during acute infection [19, 20]. A combined approach has sometimes been adopted, in one study a case was defined as primary if the IgM/IgG ratio was above 1.78, secondary if the ratio was under 1.2 and indeterminate if it fell between these values [21].

Recently, the various laboratory methods used to diagnose dengue were evaluated to define the best approach within specified time-periods during infection [22, 23]. The aim of this study was to characterise the influence of illness day on a variety of methods currently used to determine immune status in confirmed dengue cases. If applicable, we also wished to develop simple and practical models to differentiate primary from secondary dengue on specimens obtained at any time during the acute illness. To avoid the circularity mentioned above, we elected to use plaque reduction neutralisation tests (PRNTs) performed 6 months after the acute illness episode to define immune status.

Differentiating between primary and secondary dengue infections is important especially in pathogenesis research and for epidemiological surveillance, but also potentially in clinical practice. In this study, we evaluated the time-course of different sero-diagnostic responses during acute dengue in 1214 daily specimens from 249 patients, and developed various models to differentiate between primary and secondary infections, using the PRNT responses 6 months after infection as gold standard. For all assays, the best fitting models estimated a different cut-off value for different days of illness, confirming how rapidly the immune response changes during acute infection.

The all-inclusive models using the Panbio Indirect IgG, in-house capture IgG, and in-house capture IgM/IgG ratio performed well, both in general over the illness course, and when derived on any day from Day2 to Day7 (accuracy of 0.8–0.85 in differentiating between primary and secondary dengue). Although the dual-phase and Day3–6 models were a little better, the cost and practical difficulties associated with additional sampling and testing, limit the relevance of this approach. We also assessed the performance of the two most widely used of the established algorithms [10, 13] using our dataset. Innis and Shu’s algorithms showed better performance in the late phase, which may reflect the time point of sample collection in the studies used to define these algorithms. The combined strategy (which has never been formally assessed), using both Innis’ and Shu’s cut-offs of 1.78 and 1.2, gave comparable performance to the all-inclusive model based on the in-house capture IgM/IgG ratio. This is consistent with the findings for the cut-offs derived from the in-house capture IgM/IgG all-inclusive model, which ranged from 1.8 on Day2 (similar to Innis) to 1.0 on Day7. Using the combined algorithm, however, immune status cannot be defined for patients where the IgM/IgG ratio falls between 1.2 and 1.78, an issue that is circumvented with the models developed here. The 1.4 cut-off used by Kuno is the same as the value we estimate on Day4, and the 1.2 used by Shu falls between our estimates from Day5 and Day6. Therefore application of Innis’ algorithm overestimates secondary infections from Day2 onwards, while both Kuno and Shu algorithms overestimate primary infections early in illness, and overestimate secondary infections later in illness. Although here we assessed our in-house capture assays, the same principles are likely to apply to commercial capture ELISA assays, particularly when considering the IgM/IgG ratio.

It is important to note however, that these models were developed using confirmed dengue cases, and further work will be needed to assess their utility when dengue is suspected but not yet confirmed and where Zika and chikungunya may be circulating. Although rapid NS1 testing is available in some clinical settings, there is usually a delay for RT-PCR confirmation. IgM and IgG responses as measured by capture ELISA did not rise above the threshold for a positive response until after Day4 in most cases (Fig. 2), and the reliability of measurements that fall below the positive threshold in any assay is questionable [10]. By contrast indirect ELISA methodology measures much lower concentrations of dengue specific IgG, which should already be present in the early days of an infection in individuals previously exposed to DENV, but not in naïve individuals. In line with this, the all-inclusive model based on Panbio Indirect IgG without the interaction with illness day showed very good performance in the early phase, but less good performance later in illness course; however the interaction term helped to improve performance at this later time point. At present, the Panbio Indirect IgG ELISA is mainly used in a qualitative way (with a positive or negative outcome) and this assessment extends the utility of the test in classifying primary and secondary infections in confirmed dengue cases.

Solely based on measures of goodness of fit, a model based on the Panbio Indirect IgG would be appropriate for early specimens (illness day≤4), while a model based on capture IgG or IgM/IgG ratios would be more suitable for late acute specimens (illness day> 4). In practice, the choice will likely depend on the setting. In intervention trials or cohort studies, where subjects are being closely monitored, individuals are more likely to be tested early in infection, so the Panbio Indirect IgG model may be preferable. However, in tertiary hospital settings, patients tend to present later in illness course, so the capture IgG or IgM/IgG ratio model is likely to be preferable. Whichever test is used, our results show that the incorporation of day of illness into the algorithm means that determining primary or secondary status can be done using a single sample on each day of acute infection.

Several potential limitations to this study need to be considered. First, we developed a large number of models on the same dataset. However, this is the most extensive assessment of its kind to date involving 249 individuals, and we planned all statistical analyses in advance. In addition we included validation using bootstrapping and temporal cross-validation to show that the reported models did not over-fit the data or lead to over-optimistic performance claims. Second, we chose to use PRNTs 6 months after infection as the gold standard for developing the models, not HI during infection as has been used conventionally in previous studies. One reason for this choice was to try to avoid circularity – i.e. use of serological data collected at the same time-point both for developing the diagnostic algorithms and for assessing outcome. In addition, although cross-reactivity is recognized as a potential issue for all serological tests for dengue, neutralization assays are considered to have the greatest specificity to differentiate between flaviviruses. Japanese Encephalitis virus (JEV) transmission occurs in Vietnam, with increasing uptake of vaccination in recent years. However since the PRNTs were dengue specific, cross-reactivity should be reduced, and there were still many individuals who were indirect IgG negative at the time of the acute illness episode, suggesting that widespread JE vaccination has not yet had a major impact. DENV neutralizing antibody level is also not thought to be influenced by prior flavivirus vaccination (yellow fever or Japanese encephalitis virus) if performed in late convalescence [33, 34].

With the recent widespread transmission of Zika virus globally, we must also consider the possibility of Zika cross-reactivity in both the acute samples and in the PRNTs. Though we know little about Zika circulation in Vietnam at the time this study was undertaken, we cannot exclude the possibility that those classified as a secondary dengue infection had instead experienced a first Zika infection. Overall however, we identified more secondary cases compared to primary, consistent with the theory that the outcome of a true secondary dengue exposure will be more severe compared to a first. However, more research is needed to understand how exposure to other flaviviruses changes the results of dengue serology and infection outcome.

Another limitation of using the PRNT assay is that the differences in titres to each serotype (generally higher titres against DENV1 compared to other serotypes) meant that more DENV2, DENV3 and DENV4 infections were classified as indeterminate immune status and therefore excluded from the analysis. Although this might bias the performance of the all-inclusive models, when we applied these models to specimens obtained during the acute illness from this indeterminate group of 54 patients, the majority (70–74%) were classified as secondary infections, in agreement with visual inspection of marker dynamics shown in Fig. 2. In addition, when using the models to define immune status for all 303 patients included in the study, 59–63% were classified as secondary dengue, a very similar proportion to the 144/249 (59%) classified as secondary dengue based only on the 6 month PRNT data. This suggests that the indeterminate group were similar to the whole study group.

In summary, in this study we developed diagnostic algorithms aimed at differentiating between primary and secondary dengue infections during the acute phase of illness. By describing in fine detail acute serological responses in this large group of confirmed dengue patients we have contributed to the overall knowledge of the typical patterns seen, and confirmed the rapid evolution of responses during the first week of illness. We tested models based on a variety of assays and found that models using our in-house capture IgG, the capture IgM/IgG ratio or the Panbio Indirect IgG all performed well across sequential days of illness. We illustrate that primary vs secondary discrimination on single samples from different days of illness can be improved by taking the change in titres over time into account. The findings suggest that the all-inclusive model based on the commercial Panbio Indirect IgG kit may be suitable for defining immune status in the early phase of illness, while the capture IgG or IgM/IgG models are preferable after Day4 of illness. However, more importantly the work emphasizes the variability and uncertainty surrounding use of the current algorithms. The participants in our studies were mainly children and young adults reflecting the epidemiology of symptomatic dengue in southern Vietnam; the underlying principles should be similar in other populations with different dengue endemicity, but local evaluation and adaptation of the models/cutoffs would likely be necessary before application of these models in other settings.