Factors Associated with Incorrect Identification of Recent HIV Infection Using the BED Capture Immunoassay

Abstract

The BED capture enzyme immunoassay (BED-CEIA) was developed for estimating HIV incidence from cross-sectional data. This assay misclassifies some individuals with nonrecent HIV infection as recently infected, leading to overestimation of HIV incidence. We analyzed factors associated with misclassification by the BED-CEIA. We analyzed samples from 383 men who were diagnosed with HIV infection less than 1 year after a negative HIV test (Multicenter AIDS Cohort Study). Samples were collected 2-8 years after HIV seroconversion, which was defined as the midpoint between the last negative and first positive HIV test. Samples were analyzed using the BED-CEIA with a cutoff of OD-n ≤ 0.8 for recent infection. Logistic regression was used to identify factors associated with misclassification. Ninety-one (15.1%) of 603 samples were misclassified. In multivariate models, misclassification was independently associated with highly active antiretroviral treatment (HAART) for >2 years, HIV RNA <400 copies/ml, and CD4 cell count <50 or <200 cells/mm(3); adjusted odds ratios (OR) and 95% confidence intervals (CI) were 4.72 (1.35-16.5), 3.96 (1.53-10.3), 6.85 (2.71-17.4), and 11.5 (3.64-36.0), respectively. Among 220 men with paired samples, misclassification 2-4 years after seroconversion was significantly associated with misclassification 6-8 years after seroconversion [adjusted OR: 25.8 (95% CI: 8.17-81.5), p<0.001] after adjusting for race, CD4 cell count, HIV viral load, and HAART use. Low HIV viral load, low CD4 cell count, and >2 years of HAART were significantly associated with misclassification using the BED-CEIA. Some men were persistently misclassified as recently infected up to 8 years after HIV seroconversion.

Short Communication: Defining Optimality of a Test for Recent Infection for HIV Incidence Surveillance

“…Currently available, and perhaps all conceivable, tests for recent infection present a subtle problem in that some individuals who have been infected for long periods of time may nevertheless yield spurious ''recent'' results. [15][16][17] With some simplifying assumptions (which are often not numerically catastrophic) it has been shown how this ''false-recent'' phenomenon can be intuitively understood as requiring a ''subtraction'' of the estimated number of ''falserecent'' results from the observed number of ''recent'' results. [18][19][20][21] More recently, a very general analysis has been obtained by introducing a convenience recency time cut-off, T (presumed to be 1 year for the purposes of model scenarios throughout this article), which represents the time, postinfection, after which a ''recent'' test result is a ''false-recent'' result.…”

HIV Diversity as a Biomarker for HIV Incidence Estimation: Including a High-Resolution Melting Diversity Assay in a Multiassay Algorithm

“…These assays are based on the premise that the antibody response to HIV infection matures over time. However, these assays can overestimate HIV incidence because some individuals with long-standing HIV infection are classified as assay positive and are counted as incident infections (2). Serologic incidence assays have also been used as components of multiassay algorithms (MAAs) developed for cross-sectional HIV incidence estimation (3).…”

“…Serologic incidence assays have also been used as components of multiassay algorithms (MAAs) developed for cross-sectional HIV incidence estimation (3). Some MAAs also include nonserologic biomarkers, such as CD4 cell count, viral load, and antiretroviral testing or self-report, to help identify individuals with long-standing HIV infections (2,4,5).…”

Assessment of Ambiguous Base Calls in HIV-1 pol Population Sequences as a Biomarker for Identification of Recent Infections in HIV-1 Incidence Studies

“…This allows recent and nonrecent infections to be distinguished by serology-based HIV-1 incidence assays in which the increase in the proportion of HIV-specific antibodies (2), the avidity of HIV-specific antibodies (3,4), or a combination of HIV-specific antibody level and avidity (5) is measured. Unfortunately, samples from individuals infected with HIV-1 non-B subtypes (6,7), from elite controllers (8), from individuals treated with antiretroviral drugs (8,9), and from individuals with advanced stages of disease (7,9) can be misclassified on the basis of serological criteria because of delayed or reduced production of HIV-specific antibodies. Nonserological HIV-1 incidence assays (10,11) and algorithms combining serological and nonserological biomarkers have therefore been developed (12,13).…”