CROI 2015 Program and Abstracts

Abstract Listing

Oral Abstracts

Table 1: False-recent rates for recent infection testing algorithms Conclusions: Adapted to provide a standard desirable MDRI of 200 days, none of the assays, used alone, provide an acceptably low FRR. With the use of any realistic viral load threshold, the FRR values drop dramatically, to between 0.4% and 3.3%, which is operationally feasible for population-level surveillance in high incidence contexts. Increasing the viral load threshold above 75 copies/ml offered little improvement in FRRs while decreasing MDRIs. Methods for optimally combining all information about predictors of ‘false-recent’ results into real-world context-specific FRR estimates require further development. Also, judicious combinations of these assays could potentially yield further improvements in performance. 625 False Recent Rates for Two Recent Infection Testing Algorithms, South Nyanza, Kenya Clement Zeh 1 ; David Maman 4 ; Harrison Omondi 2 ; Alex Morwabe 2 ; Collins Odhiambo 2 ; Beatrice Kirubi 4 ; Irene Mukui 3 ; MartinusW. Borgdorff 1 ; Jean-François Etard 4 ; Andrea A. Kim 1 1 US Centers for Disease Control and Prevention, Kisumu, Kenya; 2 Kenya Medical Research Institute, Kisumu, Kenya; 3 National AIDS and STI Control and Prevention, Nairobi, Kenya; 4 Médecins Sans Frontières, Paris, France Background: Evaluation of candidate tests for recent HIV infection (TRI), designed to distinguish recent from chronic HIV infection, is an essential step prior to estimating cross- sectional HIV incidence. The TRI’s false-recent rate (FRR), the probability that a chronic infection will misclassify as recent, is a required parameter for calculating HIV incidence and should not exceed 2% for accuracy. Because the FRR varies by TRI and sub-population, the FRR should be assessed in all settings in which HIV incidence will be estimated. We compare the FRR for the Limiting Antigen Avidity Enzyme Immunoassay (LAg) and Bio-Rad Avidity Enzyme Immunoassay (Bio-Rad), respectively, in a high HIV prevalence setting in South Nyanza, Kenya. Methods: We conducted a population-based household survey of persons aged 15-59 years in Ndhiwa District in South Nyanza, Kenya. HIV treatment naive participants with documented chronic HIV infection (defined as testing HIV+ in the survey and reporting the first HIV+ test result ≥ 12 months preceding the survey) were tested for recent infection using the LAg and Bio-Rad on serologic blood samples. Recent infection was defined based on two recent infection testing algorithms (RITA): 1) a multi-assay algorithm (MAA) which defined a recent case as: a) tested recent on the TRI; b) not virally suppressed defined as HIV-1 RNA concentration ≥ 400 copies/mL; and 2) a single-assay algorithm (SAA) which defined a recent case as tested recent on the TRI. The FRR was calculated by dividing the number of recent cases observed on the RITA by the number of chronic infections tested. Results: Of 1,465 HIV-positive samples, 835 (57.0%) were chronic infections. Based on the MAA, the FRR was 0.5% (95% CI 0.01 – 1.0) for LAg and 2.4% (95% CI 1.4 – 3.4) for Bio-Rad. Based on the SAA, the FRR was 4.6% (95% CI 3.2 – 6.0) for LAg and 7.2% (95% CI 5.5 – 9.0) for Biorad. The FRR did not differ by sex and RITA, but varied by age group for the two RITAs. In the MAA, the FRR was highest among youth aged 15-24 years (1.2%; 95% CI 0 – 3.5 for LAg; 3.5%; 95% CI 0 – 7.4 for Bio-Rad). In the SAA, the FRR was highest among persons aged 45-59 years at 5.7%; 95% CI 2.8 – 8.6 for LAg and 8.9%; 95% CI 5.4 – 12.5 for Bio-Rad. Conclusions: The recommended threshold for a FRR was met by LAg, but only in the MAA which excluded individuals with suppressed viral load. Performance of the TRIs using the SAA resulted in high FRRs that are inappropriate for estimating incidence. 622 The Effect of HIV-1 Subtype A, C and D on Cross-Sectional Incidence Assay Performance Andrew F. Longosz 2 ; Mary Grabowski 2 ; Charles S. Morrison 3 ; Ronald H. Gray 2 ; Connie Celum 4 ; Quarraisha Abdool Karim 5 ; Hilmarie Brand 6 ;Thomas C. Quinn 1 ; Susan H. Eshleman 2 ; Oliver B. Laeyendecker 1 1 National Institute of Allergy and Infectious Diseases, Baltimore, MD, US; 2 Johns Hopkins University, Baltimore, MD, US; 3 FHI 360, Durham, NC, US; 4 University of Washington, Seattle, WA, US; 5 CAPRISA, University of KwaZulu-Natal, Congella, South Africa; 6 SACEMA, Stellenbosch University, Stellenbosch, South Africa Background: We examined the impact of HIV subtype A, C and D on the performance of serologic cross-sectional HIV incidence assays. Methods: Three assays were evaluated: the limiting antigen avidity enzyme immunoassay (LAg-Avidity assay), the BED capture enzyme immunoassay (BED-CEIA), and an avidity assay based on the Genetic Systems 1/2 + O ELISA (BioRad-Avidity assay). We evaluated 4,821 plasma and serum samples from individuals known to be infected with HIV-1 subtypes A, C and D from 6 different cohort studies in Zimbabwe, Uganda, South Africa, Kenya, Zambia and Botswana. This study included 2,045 subtype A samples (212 samples from the 2008-2009 Rakai Community Cohort Study (RCCS) and 1,833 samples from the Ugandan Genital Shedding (GS) Study. 1,697 subtype C samples (329 samples from the Ugandan and Zimbabwean GS Studies, 85 samples from HPTN 039, 727 samples from the Partners in Prevention Study and 556 samples from the CAPRISA 004 Trial Group) were analyzed. 1,079 subtype D samples (781 samples from the Ugandan Genital Shedding (GS) Study and 298 samples from the 2008-2009 RCCS) were tested. Date of HIV seroconversion was defined as either the midpoint between the last negative and first positive HIV antibody test, or fifteen days after acute infection was documented (defined as HIV RNA positive / HIV antibody negative). Viral load and HIV-1 subtype data were determined previously in parent studies. Mean duration of recent infection (MDRI) was calculated for subtypes A, C and D using a time window of two years post-seroconversion. To define recent infection, assay cutoffs of 1.5 normalized optical density (OD-n), 0.8 OD-n and 40% avidity index (AI), were used for the LAg-Avidity assay, BED-CEIA, and Bio-Rad-Avidity assay respectively. The false recent rate (FRR), the fraction of samples misclassified as recent, was calculated for all samples and those with detectable viral loads (>400 cps/ml). Results: There were significant differences for MDRI and FRR estimates by subtype for all three assays (see Table). The largest differences in MDRI were seen for the LAg-Avidity and BED-CEIA assays between subtypes A and D. FRR results were significantly higher for subtype D for all three assays.

Oral Abstracts

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CROI 2015