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

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 treat...

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Autores principales: Grant-McAuley, Wendy, Laeyendecker, Oliver, Monaco, Daniel, Chen, Athena, Hudelson, Sarah E., Klock, Ethan, Brookmeyer, Ron, Morrison, Douglas, Piwowar-Manning, Estelle, Morrison, Charles S., Hayes, Richard, Ayles, Helen, Bock, Peter, Kosloff, Barry, Shanaube, Kwame, Mandla, Nomtha, van Deventer, Anneen, Ruczinski, Ingo, Kammers, Kai, Larman, H. Benjamin, Eshleman, Susan H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9652879/
https://www.ncbi.nlm.nih.gov/pubmed/36368950
http://dx.doi.org/10.1186/s12879-022-07850-0
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author Grant-McAuley, Wendy
Laeyendecker, Oliver
Monaco, Daniel
Chen, Athena
Hudelson, Sarah E.
Klock, Ethan
Brookmeyer, Ron
Morrison, Douglas
Piwowar-Manning, Estelle
Morrison, Charles S.
Hayes, Richard
Ayles, Helen
Bock, Peter
Kosloff, Barry
Shanaube, Kwame
Mandla, Nomtha
van Deventer, Anneen
Ruczinski, Ingo
Kammers, Kai
Larman, H. Benjamin
Eshleman, Susan H.
author_facet Grant-McAuley, Wendy
Laeyendecker, Oliver
Monaco, Daniel
Chen, Athena
Hudelson, Sarah E.
Klock, Ethan
Brookmeyer, Ron
Morrison, Douglas
Piwowar-Manning, Estelle
Morrison, Charles S.
Hayes, Richard
Ayles, Helen
Bock, Peter
Kosloff, Barry
Shanaube, Kwame
Mandla, Nomtha
van Deventer, Anneen
Ruczinski, Ingo
Kammers, Kai
Larman, H. Benjamin
Eshleman, Susan H.
author_sort Grant-McAuley, Wendy
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-022-07850-0.
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spelling pubmed-96528792022-11-15 Evaluation of multi-assay algorithms for cross-sectional HIV incidence estimation in settings with universal antiretroviral treatment Grant-McAuley, Wendy Laeyendecker, Oliver Monaco, Daniel Chen, Athena Hudelson, Sarah E. Klock, Ethan Brookmeyer, Ron Morrison, Douglas Piwowar-Manning, Estelle Morrison, Charles S. Hayes, Richard Ayles, Helen Bock, Peter Kosloff, Barry Shanaube, Kwame Mandla, Nomtha van Deventer, Anneen Ruczinski, Ingo Kammers, Kai Larman, H. Benjamin Eshleman, Susan H. BMC Infect Dis Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-022-07850-0. BioMed Central 2022-11-11 /pmc/articles/PMC9652879/ /pubmed/36368950 http://dx.doi.org/10.1186/s12879-022-07850-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Grant-McAuley, Wendy
Laeyendecker, Oliver
Monaco, Daniel
Chen, Athena
Hudelson, Sarah E.
Klock, Ethan
Brookmeyer, Ron
Morrison, Douglas
Piwowar-Manning, Estelle
Morrison, Charles S.
Hayes, Richard
Ayles, Helen
Bock, Peter
Kosloff, Barry
Shanaube, Kwame
Mandla, Nomtha
van Deventer, Anneen
Ruczinski, Ingo
Kammers, Kai
Larman, H. Benjamin
Eshleman, Susan H.
Evaluation of multi-assay algorithms for cross-sectional HIV incidence estimation in settings with universal antiretroviral treatment
title Evaluation of multi-assay algorithms for cross-sectional HIV incidence estimation in settings with universal antiretroviral treatment
title_full Evaluation of multi-assay algorithms for cross-sectional HIV incidence estimation in settings with universal antiretroviral treatment
title_fullStr Evaluation of multi-assay algorithms for cross-sectional HIV incidence estimation in settings with universal antiretroviral treatment
title_full_unstemmed Evaluation of multi-assay algorithms for cross-sectional HIV incidence estimation in settings with universal antiretroviral treatment
title_short Evaluation of multi-assay algorithms for cross-sectional HIV incidence estimation in settings with universal antiretroviral treatment
title_sort evaluation of multi-assay algorithms for cross-sectional hiv incidence estimation in settings with universal antiretroviral treatment
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9652879/
https://www.ncbi.nlm.nih.gov/pubmed/36368950
http://dx.doi.org/10.1186/s12879-022-07850-0
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