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      Options to Expand HIV Viral Load Testing in South Africa: Evaluation of the GeneXpert® HIV-1 Viral Load Assay

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          Abstract

          Background

          Expansion of HIV viral load (VL) testing services are required to meet increased targets for monitoring patients on antiretroviral treatment. South Africa currently tests >4million VLs per annum in 16 highly centralised, automated high-throughput laboratories. The Xpert HIV-1 VL assay (Cepheid) was evaluated against in-country predicates, the Roche Cobas Taqmanv2 and Abbott HIV-1RT, to investigate options for expanding VL testing using GeneXpert’s random access, polyvalent capabilities and already established footprint in South Africa with the Xpert MTB/RIF assay (207 sites). Additionally, the performance of Xpert HIV-1VL on alternative, off-label specimen types, Dried Blood Spots (DBS) and whole blood, was investigated.

          Method

          Precision, accuracy (agreement) and clinical misclassification (1000cp/ml) of Xpert HIV-1VL plasma was compared to Taqmanv2 (n = 155) and Abbott HIV-1 RT (n = 145). Misclassification of Xpert HIV-1VL was further tested on DBS (n = 145) and whole blood (n = 147).

          Results

          Xpert HIV-1VL demonstrated 100% concordance with predicate platforms on a standardised frozen, plasma panel (n = 42) and low overall percentage similarity CV of 1.5% and 0.9% compared to Taqmanv2 and Abbott HIV-1 RT, respectively. On paired plasma clinical specimens, Xpert HIV-1VL had low bias (SD 0.32–0.37logcp/ml) and 3% misclassification at the 1000cp/ml threshold compared to Taqmanv2 (fresh) and Abbott HIV-1 RT (frozen), respectively. Xpert HIV-1VL on whole blood and DBS increased misclassification (upward) by up to 14% with increased invalid rate. All specimen testing was easy to perform and compatible with concurrent Xpert MTB/RIF Tuberculosis testing on the same instrument.

          Conclusion

          The Xpert HIV-1VL on plasma can be used interchangeably with existing predicate platforms in South Africa. Whole blood and DBS testing requires further investigation, but polyvalency of the GeneXpert offers a solution to extending VL testing services.

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          Most cited references 11

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          Statistical methods for assessing agreement between two methods of clinical measurement.

          In clinical measurement comparison of a new measurement technique with an established one is often needed to see whether they agree sufficiently for the new to replace the old. Such investigations are often analysed inappropriately, notably by using correlation coefficients. The use of correlation is misleading. An alternative approach, based on graphical techniques and simple calculations, is described, together with the relation between this analysis and the assessment of repeatability.
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            Comparison of methods: Passing and Bablok regression.

            The comparison of methods experiment is important part in process of analytical methods and instruments validation. Passing and Bablok regression analysis is a statistical procedure that allows valuable estimation of analytical methods agreement and possible systematic bias between them. It is robust, non-parametric, non sensitive to distribution of errors and data outliers. Assumptions for proper application of Passing and Bablok regression are continuously distributed data and linear relationship between data measured by two analytical methods. Results are presented with scatter diagram and regression line, and regression equation where intercept represents constant and slope proportional measurement error. Confidence intervals of 95% of intercept and slope explain if their value differ from value zero (intercept) and value one (slope) only by chance, allowing conclusion of method agreement and correction action if necessary. Residual plot revealed outliers and identify possible non-linearity. Furthermore, cumulative sum linearity test is performed to investigate possible significant deviation from linearity between two sets of data. Non linear samples are not suitable for concluding on method agreement.
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              Multiple method comparison: statistical model using percentage similarity.

              Method comparison typically determines how well two methods agree. This is usually performed using the difference plot model, which measures absolute differences between two methods. This is often not applicable to data with wide ranges of absolute values. An alternative model is introduced that simplifies comparisons specifically for multiple methods compared to a gold standard. The average between a new method and the gold standard is represented as a percentage of the gold standard. This is interpreted as a percentage similarity value and accommodates wide ranges of data. The representation of the percentage similarity values in a histogram format highlights the accuracy and precision of several compared methods to a gold standard. The calculation of a coefficient of variation further defines agreement between methods. Percentage similarity histograms of several new methods can be compared to a gold standard simultaneously, and the comparison easily visualized through use of a single 100% similarity reference line drawn common to all plots. This simple method of comparison would be particularly useful for multiple method comparison and is especially applicable for centers collating for external quality assessment or assurance programs to demonstrate differences in results between laboratories. Copyright 2003 Wiley-Liss, Inc.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                16 December 2016
                2016
                : 11
                : 12
                Affiliations
                [1 ]Department of Haematology and Molecular Medicine, School of Pathology, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa
                [2 ]National Priority Program of the National Health Laboratory Services, Johannesburg, South Africa
                Centers for Disease Control and Prevention, UNITED STATES
                Author notes

                Competing Interests: The authors declare that no competing interests exist. Cepheid provided the technical support and Xpert HIV-1 VL testing kits for the study but was not involved in study design, data collection, analysis, or manuscript preparation. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

                • Conceptualization: WS.

                • Formal analysis: NG LS.

                • Funding acquisition: WS.

                • Investigation: NG.

                • Methodology: NG LS.

                • Project administration: LB WS.

                • Resources: LB WS.

                • Supervision: LS WS.

                • Validation: NG.

                • Visualization: NG LS.

                • Writing – original draft: NG LS.

                • Writing – review & editing: NG LS LB WS.

                Article
                PONE-D-16-31361
                10.1371/journal.pone.0168244
                5161463
                27992495
                © 2016 Gous et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 5, Tables: 3, Pages: 14
                Product
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100004828, Grand Challenges Canada;
                Award ID: 0007-02-01-01-01
                Award Recipient :
                This publication was made possible by the generous financial support of the Grand Challenges Canada POC implementation grant (grant 0007-02-01-01-01). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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