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. 2022 Nov 11;22(1):838.
doi: 10.1186/s12879-022-07850-0.

Evaluation of multi-assay algorithms for cross-sectional HIV incidence estimation in settings with universal antiretroviral treatment

Wendy Grant-McAuley  1 , Oliver Laeyendecker  2   3 , Daniel Monaco  1   4 , Athena Chen  5 , Sarah E Hudelson  1 , Ethan Klock  3 , Ron Brookmeyer  6 , Douglas Morrison  7 , Estelle Piwowar-Manning  1 , Charles S Morrison  8 , Richard Hayes  9 , Helen Ayles  10   11 , Peter Bock  12 , Barry Kosloff  10   11 , Kwame Shanaube  10 , Nomtha Mandla  12 , Anneen van Deventer  12 , Ingo Ruczinski  5 , Kai Kammers  13 , H Benjamin Larman  1   4 , Susan H Eshleman  14
Affiliations

Evaluation of multi-assay algorithms for cross-sectional HIV incidence estimation in settings with universal antiretroviral treatment

Wendy Grant-McAuley et al. BMC Infect Dis. .

Abstract

Background: Multi-assay algorithms (MAAs) are used to estimate population-level HIV incidence and identify individuals with recent infection. Many MAAs use low viral load (VL) as a biomarker for long-term infection. This could impact incidence estimates in settings with high rates of early HIV treatment initiation. We evaluated the performance of two MAAs that do not include VL.

Methods: Samples were collected from 219 seroconverters (infected < 1 year) and 4376 non-seroconverters (infected > 1 year) in the HPTN 071 (PopART) trial; 28.8% of seroconverter samples and 73.2% of non-seroconverter samples had VLs ≤ 400 copies/mL. Samples were tested with the Limiting Antigen Avidity assay (LAg) and JHU BioRad-Avidity assays. Antibody reactivity to two HIV peptides was measured using the MSD U-PLEX assay. Two MAAs were evaluated that do not include VL: a MAA that includes the LAg-Avidity assay and BioRad-Avidity assay (LAg + BR) and a MAA that includes the LAg-Avidity assay and two peptide biomarkers (LAg + PepPair). Performance of these MAAs was compared to a widely used MAA that includes LAg and VL (LAg + VL).

Results: The incidence estimate for LAg + VL (1.29%, 95% CI: 0.97-1.62) was close to the observed longitudinal incidence (1.34% 95% CI: 1.17-1.53). The incidence estimates for the other two MAAs were higher (LAg + BR: 2.56%, 95% CI 2.01-3.11; LAg + PepPair: 2.84%, 95% CI: 1.36-4.32). LAg + BR and LAg + PepPair also misclassified more individuals infected > 2 years as recently infected than LAg + VL (1.2% [42/3483 and 1.5% [51/3483], respectively, vs. 0.2% [6/3483]). LAg + BR classified more seroconverters as recently infected than LAg + VL or LAg + PepPair (80 vs. 58 and 50, respectively) and identified ~ 25% of virally suppressed seroconverters as recently infected.

Conclusions: The LAg + VL MAA produced a cross-sectional incidence estimate that was closer to the longitudinal estimate than two MAAs that did not include VL. The LAg + BR MAA classified the greatest number of individual seroconverters as recently infected but had a higher false recent rate.

Keywords: Early antiretroviral treatment; HIV incidence estimation; Recent infections; Universal antiretroviral treatment; Viral suppression.

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Conflict of interest statement

H.B.L. is an inventor on an issued patent (US20160320406A) filed by Brigham and Women’s Hospital that covers the use of the VirScan technology, is a founder of ImmuneID, Portal Bioscience and Alchemab, and is an advisor to TScan Therapeutics. All other authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Study design. Samples for this study were obtained from two study cohorts (Table 1). The figure shows the assessments performed with samples from each cohort. Three multi-assay algorithms (MAAs) were assessed (Table 2). aIncluded in prior publications [25, 26]. bSee Additional file 1
Fig. 2
Fig. 2
Observed longitudinal Incidence and cross-sectional Incidence estimates, HPTN 071 (PopART). The figure shows the observed longitudinal HIV incidence in the Validation Cohort, compared with incidence estimated using the LAg + VL MAA, the LAg + BR MAA, and the LAg + PepPairA MAA. Error bars represent 95% confidence intervals. MAA multi-assay algorithm, PY person years
See this image and copyright information in PMC

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