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Performance of Cepheid Xpert HIV-1 viral load plasma assay to accurately detect treatment failure

Sacks, Jilian A.a; Fong, Youyib; Gonzalez, Mercedes Perezc; Andreotti, Maurod; Baliga, Shrikalae; Garrett, Nigelf; Jordan, Jeanneg; Karita, Etienneh; Kulkarni, Smitai; Mor, Ornaj; Mosha, Faustak; Ndlovu, Zibusisol; Plantier, Jean-Christophem; Saravanan, Shanmugamn; Scott, Lesleyo; Peter, Trevora; Doherty, Megc; Vojnov, Larac

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doi: 10.1097/QAD.0000000000002303
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Abstract

Introduction

Viral load access is critical to both patient and public healthcare. Currently, 59% of patients on ART have access to viral load testing [1]; however, three countries in particular (Kenya, South Africa and Uganda) account for approximately 50% of viral load tests performed in 2017 suggesting that viral load coverage outside of these countries is significantly lower. Challenges with cold chain and specimen transport, particularly in ensuring blood samples, reach the laboratory within 6–24 h per manufacturer guidelines have hindered viral load access and scale-up.

Alternative technologies, such as near point-of-care and point-of-care assays, may support increased access to viral load testing. Several technologies are in development or on the market [2]. Point-of-care (POC) technologies have previously been found to significantly reduce test turnaround times and support faster treatment decisions. When CD4+ cell count was still used to determine treatment eligibility, a systematic review and meta-analysis found that POC CD4+ cell count testing improved linkage to care and timeliness of ART initiation [3]. Furthermore, two recent studies from Malawi and Mozambique have shown significantly reduced test turnaround times and improved treatment initiation rates when POC early infant diagnosis technologies were used compared with laboratory-based technologies [4,5]. Decentralizing testing for both diagnosis and monitoring closer to the patient may increase access to care, improve both personal and public health, and support more sustainable, inclusive health systems [6].

Monitoring patients on treatment is critical to understand treatment effectiveness and identify patients in need of increased adherence support or regimen switch. Although rapid or immediate test result return using point-of-care and near point-of-care technologies may not be necessary for all patients in need of viral load testing, several populations may most benefit. Pregnant and breastfeeding women to prevent vertical transmission, infants and children who often have higher levels of drug resistance [7,8], patients suspected of failing treatment or patients with advanced HIV disease may all be in need of more rapid testing and clinical action to address unsuppressed viral loads. As laboratory systems continue to be scaled up and improved, ensuring swift test turnaround times to facilitate high-quality clinical services continues to prove challenging. New technologies that can be placed closer to the patient may prove beneficial, especially to support patients in more critical need of rapid test results and clinical decision-making.

Several diagnostic accuracy studies have been conducted measuring the performance of the Cepheid Xpert HIV-1 viral load plasma assay [9–27] and a systematic review was generated to provide initial analytical data [28]; however, clinical utility data of the Cepheid Xpert HIV-1 viral load plasma assay are lacking. The Cepheid Xpert HIV-1 viral load assay was approved by the European Union in 2017 and included on the WHO list of prequalified in-vitro diagnostic products in August 2017 [29]. This assay can provide HIV-1 viral load results within 2 h, does not require the same specimen transportation logistics of laboratory-based technologies and has already been procured and implemented significantly for tuberculosis testing across high burden regions [2,29,30,31]. We, therefore, consolidated primary data from diagnostic accuracy studies [12,14–19,21,23–27] to determine how the Cepheid Xpert HIV-1 viral load assay performed when used within the WHO treatment failure algorithm [32,33].

Materials and methods

Search strategy and study selection

The search strategy was previously published [28]. An updated search was conducted to identify studies published since the previous systematic review. Briefly, PubMed, EMBASE, Google Scholar and Medline databases were used to identify peer-reviewed original research with appropriate data for this meta-analysis on 30 July 2018. Conference abstracts from Conference on Retroviruses and Opportunistic Infections (CROI), International Conference on AIDS and STIs in Africa (ICASA), International AIDS Society (IAS) and AIDS Conferences as well as extensive bibliography and grey literature were screened for possible inclusion. Studies were included if they included technical evaluation data comparing the Cepheid Xpert HIV-1 viral load plasma assay to a laboratory-based viral load plasma assay and were performed using HIV-positive clinical samples. Studies were excluded if they used spike blood samples or panels, performed a qualitative analysis or the comparator was a sample type other than plasma. We contacted the corresponding authors of all studies that met the inclusion criteria to explain the analysis plan and request original data. Each corresponding author was contacted at least twice.

Statistical analyses

Data from studies willing to share primary data were pooled to compare the performance of Cepheid Xpert HIV-1 viral load plasma assay with comparator laboratory technology. Study-specific data were generated for each study providing primary data, including the median values, distribution, R2 and bias [95% confidence interval (95% CI)]. As WHO guidelines suggest the use of a threshold to support treatment monitoring, several treatment failure thresholds for viral load were assessed, including detectable (defined as any result above the limit of detection indicates treatment failure), 200, 400, 500, 600, 800 and 1000 copies/ml. Each treatment failure threshold for Cepheid Xpert was compared with the same treatment failure threshold value for the comparator technology. Analyses were conducted by pooling all comparators together as well as disaggregating by comparator (three comparators were included in submitted data: Abbott RealTime HIV-1, bioMerieux NucliSENS EasyQ HIV-1 and Roche COBAS TaqMan HIV-1 v2.0). The limit of detection varied slightly by technology: Abbott RealTime HIV-1 = 40 copies/ml, bioMerieux NucliSENS EasyQ HIV-1 = 25 copies/ml, Cepheid Xpert HIV-1 viral load = 40 copies/ml and Roche COBAS TaqMan HIV-1 v2.0 = 20 copies/ml and notified the definition of the detectable threshold analysis. Using these treatment failure thresholds, the sensitivity, specificity, upward and downward misclassification rates, and positive and negative predictive values were calculated. Definitions were previously described (L. Vojnov, S. Carmona, C. Zeh, et al., in preparation). Bivariate random effects models were used to estimate summary sensitivity, specificity and other accuracy measures to simultaneously determine the pooled estimates, accounting for the covariance of sensitivity and specificity as well as study specific heterogeneity [34,35]. Analyses were completed using the lme4 R package in R version 3.4.3 (The R Foundation).

Protocol

The prepared protocol was reviewed by the WHO.

Results

A systematic review was previously conducted including 12 articles and 13 studies, in which the quality of studies and study characteristics were described [28]. Since the previous search, an additional six studies were published that met the inclusion criteria [10,21,22,25–27] (Table 1). After contacting each corresponding author, a total of 13 studies provided 14 primary datasets out of the total of 19 eligible Cepheid Xpert plasma viral load studies resulting in a total of 3790 paired plasma viral load results [12,14–19,21,23–27]. Approximately 55% of the paired data points were from studies conducted in Africa [12,14,15,21,26,27], of which 50% were from each of the Southern African Development Community region [12,14,15,26,27] and the East African Community region [21,25]. Just over 30% of paired data points were from studies conducted in Europe or USA [16,17], while 11% were from studies conducted in India [18,23,24]. The study-specific characteristics such as sample size, viral load medians and patient viral load distributions are summarized in Table 2.

Table 1
Table 1:
Study characteristics of additional studies found beyond those included in [28].
Table 2
Table 2:
Analytical (r 2, bias, median viral loads) and patient viral load distributions for each study included in the meta-analysis.

Figure 1 shows the distribution of the viral load values from all studies. Thirty percent of viral load test results were undetectable, while 45% were between detectable and 10 000 copies/ml and the remaining 25% were above 10 000 copies/ml. The median comparator viral load was 157 copies/ml and median Xpert viral load was 119 copies/ml, while the overall log10 bias was 0.04 log copies/ml (95% CI: 0.02–0.07) (Table 3 and Fig. 2). The R2 was 0.941 (P < 0.001). When disaggregated by the comparator used, the median and bias results were similarly consistent and within 0.2 log copies/ml.

Fig. 1
Fig. 1:
Patient plasma viral load distribution using the comparator assay from all studies included in the meta-analysis.
Table 3
Table 3:
Meta-analysis of clinical metrics (median viral loads, mean bias, sensitivity, specificity and misclassification) overall and for each comparator technology, across various treatment failure thresholds.
Fig. 2
Fig. 2:
Clinical performance of the Xpert viral load assay compared with the comparator.Forest plots of sensitivity (a), specificity (b) and log bias (c) of all studies included in the meta-analysis using a treatment failure threshold of 1000 copies/ml. (d) The Xpert viral load results that fall within and outside ±0.3 log copies/ml of the comparator viral load result. Blue circles and bars represent ±0.3 log copies/ml of the plasma result, while orange diamonds represent the paired Xpert viral load result.

The sensitivity and specificity to detect treatment failure were above 95% at all treatment failure thresholds, except for detectable. The sensitivity and specificity at 1000 copies/ml were 96.47% (95% CI: 95.1–97.5) and 96.59% (95% CI: 92.9–98.4), respectively (Table 3 and Fig. 2). At a treatment failure threshold of detectable, the sensitivity was 93.33% (95% CI: 88.2–96.3) and specificity was 80.56% (95% CI: 64.6–90.4). The positive and negative predictive values were also high at all treatment failure thresholds, except the negative predictive value at a detectable treatment failure threshold was 69.76% (95% CI: 41.9–88.1). Upward and downward misclassification rates were below 5% at all treatment failure thresholds, except for detectable. At a treatment failure threshold of detectable, the upward misclassification rate was 19.44% (95% CI: 9.6–35.4) and downward misclassification rate was 6.67% (95% CI: 3.7–11.8). When comparing the analytical performance between specific comparator technologies (Abbott RealTime HIV-1, bioMerieux NucliSENS EasyQ HIV-1 and Roche COBAS TaqMan HIV-1 v2.0), the differences were negligible (data not shown).

Quantitative polymerase chain reaction inherently has a level of variability in test results, generally ±0.3 log copies/ml [36]. We, therefore, sought to understand whether the performance observed with each technology was within the inherent assay variability limits. Nearly 65% of Cepheid Xpert HIV-1 viral load results were within ±0.3 log10 copies/ml of the comparator viral load (Fig. 2d).

Finally, the proportion of invalid rates was also recorded. The comparator technologies had an invalid rate of 0.5%, while the Xpert viral load assay had an invalid rate of 1.66%. However, it is possible that not all investigators provided invalid rates for both technologies.

Discussion

The performance of the Cepheid Xpert HIV-1 viral load plasma assay was comparable to laboratory-based assays. The clinical accuracy when measured considering programmatic use (the ability to correctly diagnose patients as failing treatment or suppressed) was notable in that very few patients (<5%) would have been misclassified. Furthermore, the proportion of invalid tests within these studies was low (<2%). This meta-analysis, therefore, provides clear insights into the clinical diagnostic accuracy of this technology to support viral load scale-up.

There was some heterogeneity across studies observed. The mean bias, for example, was beyond ±0.3 log copies/ml in three studies [15,19,23]. Furthermore, the difference in comparator and Xpert medians was significant, greater than 1 log copy/ml, in two studies [15,27]. It is unknown why these differences were observed; however, these studies had significantly higher proportions of patients who were undetectable included in the primary studies. This may have skewed the bias and medians, as any variability within the smaller subset of detectable results could have been accentuated.

We also reviewed the performance of the Cepheid Xpert HIV-1 viral load assay to correctly classify patients as above or below lower treatment failure thresholds. One published study has suggested that patients with low-level viremia on efavirenz-based treatment may have or be at risk for drug resistance [37]. The Cepheid Xpert HIV-1 viral load assay had good clinical performance (>95% sensitive and specific) at all lower treatment failure thresholds, except detectable. This would indicate that this technology could be used for countries considering a lower treatment failure threshold than the current standard of 1000 copies/ml. For those using or considering a treatment failure threshold of detectable, it should be noted that nearly 20% of patients who are actually suppressed will be misclassified as failing treatment. Although this could be resolved with follow-up testing, upward misclassification may result in unnecessarily switching patients to more expensive second-line treatment. Furthermore, the role of low-level viremia in the context of optimized treatment, such as dolutegravir, is unknown and may be different from efavirenz.

While the accuracy of the Xpert HIV-1 viral load assay is clear, the programmatic feasibility and operational acceptability remain to be studied. Although it can be used in decentralized settings near the point-of-care, centrifugation of whole blood into plasma is required. Thus far, a whole blood HIV-1 viral load assay has not yet been developed that can allow for direct sample application and greater decentralization. Few healthcare facilities have centrifuges and/or the necessary skills to process samples. In addition, this technology requires consistent electricity and temperature-controlled rooms, further limiting decentralization; however, new technologies are being developed, by this supplier and others, to tackle these challenges [2].

Although the patient impact of point-of-care assays has been well documented for CD4+ cell count and early infant diagnosis [3–5], the impact of point-of-care or near point-of-care technologies for viral load testing is unclear. For example, the clinical utility, reductions in morbidity and/or mortality, and improvements in and faster switching to second-line treatment will help to determine whether global recommendations should be made. In the meantime, consideration for point-of-care and near point-of-care viral load technologies may be focused on the most critical and vulnerable patient populations. Several countries are prioritizing point-of-care and near point-of-care technologies for use in maternity settings to support reduction of mother-to-child transmission as well as HIV care settings for patients suspected of failing treatment, infants and children who are typically at a greater risk for drug resistance, and patients with advanced HIV disease. These patient populations may be in need of more rapid clinical decision-making that can be supported with point-of-care and near point-of-care technology; however, further research is needed.

Furthermore, there is limited analysis on the affordability and cost-effectiveness of point-of-care and near point-of-care technologies for viral load testing. Several studies have found viral load monitoring in general to be cost-effective [38], particularly when coupled with differentiated care programmes. The few studies on the cost-effectiveness of point-of-care viral load were done before any such technology was on the market or implemented [39–41]. The models, including assumptive characteristics and pricing, indicated that point-of-care viral load testing was cost-effective due to reductions in unnecessary switches to second-line antiretroviral therapy. Shortening the delays associated with laboratory-based testing resulted in small benefits. Updated cost-effectiveness and affordability studies would be of great benefit, including current and future test pricing, as well as incorporating upcoming patient impact data.

Our study has several limitations. First, possible bias exists, as not all primary data were shared and included in this meta-analysis. Unfortunately, this will always be a significant limitation to such meta-analysis. However, nearly 70% of studies were included in this meta-analysis and limited heterogeneity was observed. The total sample size was large with nearly 4000 paired data points and CIs tight, indicating that additional data may not change the results significantly. Unfortunately, the comparators used were variable across studies. Seven studies used the Abbott RealTime HIV-1 assay, one used the bioMerieux NucliSENS EasyQ HIV-1 assay and six used the Roche COBAS TaqMan HIV-1 assay. Although differences between comparator assays should be subtle, this is not ideal within a meta-analysis. Fortunately, however, the results were relatively consistent across comparators and when analysed together. Finally, a similar study reviewing the clinical accuracy of laboratory-based assays has not been done to provide a clear comparison for new, alternative technologies such as point-of-care assays.

Although systematic reviews and meta-analyses using the results already included in primary manuscripts is the most conventional method of analysis, they can often lack in providing clear clinical data. Furthermore, it is frequent for studies to measure and include different parameters within manuscripts, making consolidation of data and comparisons difficult. This was especially observed in the original systematic review wherein Table 3 highlighted the diversity of measures of agreement between included studies; no one measurement was included within all [28]. Meta-analyses using primary data can allow for richer data analysis and consider not just analytical but more importantly clinical review. For example, though viral load is a continuous variable requiring analysis using Bland–Altman, linear regression and so on analyses, it is often used in many high burden programmatic settings as a binary value to determine whether patients may be failing treatment or are suppressed. Being able to analyse the data in the way the technology will be applied is critical to support country access and uptake. Ideally, systematic reviews and meta-analyses would review and include both analytical (bias, correlation coefficient, R2 and so on) and clinical data (sensitivity, specificity, misclassification).

The Cepheid Xpert HIV-1 viral load assay performed well compared with traditional laboratory-based comparators. Further diagnostic accuracy studies of the Cepheid Xpert HIV-1 viral load assay are unlikely to yield additional data or information; however, operational feasibility, acceptability and affordability remain unknown. The GeneXpert technology has already been widely used for tuberculosis testing [30,31] with over 10 000 Xpert devices procured in Africa and Asia by the end of 2017. A range of technologies that can test for multiple disease or assays at the point-of-care and within laboratories currently exist, both on the market and in development [42]. The ability to use a single technology to diagnose and monitor multiple diseases, called multiplexing, multidisease testing or diagnostics integration, is a novel and new innovation that should be considered by national programmes, as it may lead to significant programmatic, clinical and financial efficiencies [43]. Furthermore, alternative, more stable sample types, such as dried blood spot specimens and plasma separating products can offer additional approaches to enable increased access to viral load testing. Selection of appropriate technologies to support viral testing needs requires consideration of not only diagnostic accuracy but also patient impact, costs, supply chain, ease of use and implementation context. As with all diagnostic technologies, postmarket surveillance and quality assurance are critical to ensure high-quality test results.

Acknowledgements

J.S.: designed study, reviewed included papers, interpreted results, wrote the first draft and produced the finalized version of the manuscript.

Y.F.: conducted meta-analysis, interpreted results, developed table, substantially commented on manuscript drafts and approved the finalized version of the manuscript.

M.P.G., M.D., T.P., H.A.: substantially commented on manuscript drafts and approved the finalized version of the manuscript.

M.A., S.B., N.G., J.J., E.K., S.K., O.M., F.M., Z.N., J.C.P., S.S., L.S.: shared primary data, substantially commented on manuscript drafts and approved the finalized version of the manuscript.

L.V.: designed study, performed literature search, reviewed included papers, extracted data, collected primary data, interpreted results, substantially commented on manuscript drafts and approved the finalized version of the manuscript.

This work was supported by the Gates Foundation and NIH R01AI122991.

The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the US Centers for Disease Control and Prevention.

Conflicts of interest

There are no conflicts of interest.

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Keywords:

HIV; viral load; Xpert

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