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Evaluation of a Multiplex Assay for Estimation of HIV-1 Incidence
  • Published Date:
    May 22 2013
  • Source:
    PLoS One. 2013; 8(5).
Filetype[PDF - 347.11 KB]


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  • Description:
    Objectives

    Accurate methods of estimating HIV-1 incidence are critical for monitoring the status of the epidemic and the impact of prevention strategies. Although several laboratory-based tests have been developed strictly for this purpose, several limitations exist and improved methods or technologies are needed. We sought to further optimize a previously described bead-based, HIV-1-specific multiplex assay with the capability of measuring multiple immune responses for determining recent infection.

    Methods

    We refined the customized HIV-1 Bio-Plex assay by determining cutoffs and mean durations of recency (MDR), based on the reactivity to longitudinal seroconversion specimens (n = 1347) from 311 ART-naïve, HIV-1-infected subjects. False-recent rates (FRRs) were calculated for various long-term cohorts, including AIDS patients, individuals on ART, and subtype C specimens. Incidence was estimated for each individual assay analyte from a simulated population with a known incidence of 1%. For improved incidence estimates, multi-analyte algorithms based on combinations of 3 to 6 analytes were evaluated and compared to the performance of each individual analyte.

    Results

    The MDR for the six analytes varied from 164.2 to 279.4 days, while the multi-analyte algorithm MDRs were less variable with a minimum and maximum value of 228.4 and 277.9 days, respectively. The FRRs for the 7 multi-analyte algorithms evaluated in this study varied from 0.3% to 3.1%, in a population of ART-naïve, long-term individuals. All algorithms yielded improved incidence estimates as compared to the individual analytes, predicting an incidence of 0.95% to 1.02%.

    Conclusions

    The HIV-specific multiplex assay described here measures several distinct immune responses in a single assay, allowing for the consideration of multi-analyte algorithms for improved HIV incidence estimates.