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Optimal timing of viral load monitoring during pregnancy to predict viraemia at delivery in HIV‐infected women initiating ART in South Africa: a simulation study

INTRODUCTION: HIV viral load (VL) monitoring is a central tool to evaluate ART effectiveness and transmission risk. There is a global movement to expand VL monitoring following recent recommendations from the World Health Organization (WHO), but there has been little research into VL monitoring in p...

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Autores principales: Lesosky, Maia, Glass, Tracy, Mukonda, Elton, Hsiao, Nei‐Yuan, Abrams, Elaine J., Myer, Landon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5978661/
https://www.ncbi.nlm.nih.gov/pubmed/29171179
http://dx.doi.org/10.1002/jia2.25000
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author Lesosky, Maia
Glass, Tracy
Mukonda, Elton
Hsiao, Nei‐Yuan
Abrams, Elaine J.
Myer, Landon
author_facet Lesosky, Maia
Glass, Tracy
Mukonda, Elton
Hsiao, Nei‐Yuan
Abrams, Elaine J.
Myer, Landon
author_sort Lesosky, Maia
collection PubMed
description INTRODUCTION: HIV viral load (VL) monitoring is a central tool to evaluate ART effectiveness and transmission risk. There is a global movement to expand VL monitoring following recent recommendations from the World Health Organization (WHO), but there has been little research into VL monitoring in pregnant women. We investigated one important question in this area: when and how frequently VL should be monitored in women initiating ART during pregnancy to predict VL at the time of delivery in a simulated South African population. METHODS: We developed a mathematical model simulating VL from conception through delivery using VL data from the Maternal and Child Health – Antiretroviral Therapy (MCH‐ART) cohort. VL was modelled based on three major compartments: pre‐ART VL, viral decay immediately after ART initiation and viral maintenance (including viral suppression and viraemic episodes). Using this simulation, we examined the performance of various VL monitoring schema in predicting elevated VL at delivery. RESULTS AND DISCUSSION: If WHO guidelines for non‐pregnant adults were used, the majority of HIV‐infected pregnant women (69%) would not receive a VL test during pregnancy. Most models that based VL monitoring in pregnancy on the time elapsed since ART initiation (regardless of gestation) performed poorly (sensitivity <50%); models that based VL measures in pregnancy on the woman's gestation (regardless of time on ART) appeared to perform better overall (sensitivity >60%). Across all permutations, inclusion of pre‐ART VL values had a negligible impact on predictive performance (improving test sensitivity and specificity <6%). Performance of VL monitoring in predicting VL at delivery generally improved at later gestations, with the best performing option a single VL measure at 36 weeks’ gestation. CONCLUSIONS: Development and evaluation of a novel simulation model suggests that strategies to measure VL relative to gestational age may be more useful than strategies relative to duration on ART, in women initiating ART during pregnancy, supporting better integration of maternal and HIV health services. Testing turnaround times require careful consideration, and point‐of‐care VL testing may be the best approach for measuring VL at delivery. Broadening the scope of this simulation model in the light of current scale up of VL monitoring in high burden countries is important.
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spelling pubmed-59786612018-06-01 Optimal timing of viral load monitoring during pregnancy to predict viraemia at delivery in HIV‐infected women initiating ART in South Africa: a simulation study Lesosky, Maia Glass, Tracy Mukonda, Elton Hsiao, Nei‐Yuan Abrams, Elaine J. Myer, Landon J Int AIDS Soc Short Report INTRODUCTION: HIV viral load (VL) monitoring is a central tool to evaluate ART effectiveness and transmission risk. There is a global movement to expand VL monitoring following recent recommendations from the World Health Organization (WHO), but there has been little research into VL monitoring in pregnant women. We investigated one important question in this area: when and how frequently VL should be monitored in women initiating ART during pregnancy to predict VL at the time of delivery in a simulated South African population. METHODS: We developed a mathematical model simulating VL from conception through delivery using VL data from the Maternal and Child Health – Antiretroviral Therapy (MCH‐ART) cohort. VL was modelled based on three major compartments: pre‐ART VL, viral decay immediately after ART initiation and viral maintenance (including viral suppression and viraemic episodes). Using this simulation, we examined the performance of various VL monitoring schema in predicting elevated VL at delivery. RESULTS AND DISCUSSION: If WHO guidelines for non‐pregnant adults were used, the majority of HIV‐infected pregnant women (69%) would not receive a VL test during pregnancy. Most models that based VL monitoring in pregnancy on the time elapsed since ART initiation (regardless of gestation) performed poorly (sensitivity <50%); models that based VL measures in pregnancy on the woman's gestation (regardless of time on ART) appeared to perform better overall (sensitivity >60%). Across all permutations, inclusion of pre‐ART VL values had a negligible impact on predictive performance (improving test sensitivity and specificity <6%). Performance of VL monitoring in predicting VL at delivery generally improved at later gestations, with the best performing option a single VL measure at 36 weeks’ gestation. CONCLUSIONS: Development and evaluation of a novel simulation model suggests that strategies to measure VL relative to gestational age may be more useful than strategies relative to duration on ART, in women initiating ART during pregnancy, supporting better integration of maternal and HIV health services. Testing turnaround times require careful consideration, and point‐of‐care VL testing may be the best approach for measuring VL at delivery. Broadening the scope of this simulation model in the light of current scale up of VL monitoring in high burden countries is important. John Wiley and Sons Inc. 2017-11-24 /pmc/articles/PMC5978661/ /pubmed/29171179 http://dx.doi.org/10.1002/jia2.25000 Text en © 2017 The Authors. Journal of the International AIDS Society published by John Wiley & sons Ltd on behalf of the International AIDS Society. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Short Report
Lesosky, Maia
Glass, Tracy
Mukonda, Elton
Hsiao, Nei‐Yuan
Abrams, Elaine J.
Myer, Landon
Optimal timing of viral load monitoring during pregnancy to predict viraemia at delivery in HIV‐infected women initiating ART in South Africa: a simulation study
title Optimal timing of viral load monitoring during pregnancy to predict viraemia at delivery in HIV‐infected women initiating ART in South Africa: a simulation study
title_full Optimal timing of viral load monitoring during pregnancy to predict viraemia at delivery in HIV‐infected women initiating ART in South Africa: a simulation study
title_fullStr Optimal timing of viral load monitoring during pregnancy to predict viraemia at delivery in HIV‐infected women initiating ART in South Africa: a simulation study
title_full_unstemmed Optimal timing of viral load monitoring during pregnancy to predict viraemia at delivery in HIV‐infected women initiating ART in South Africa: a simulation study
title_short Optimal timing of viral load monitoring during pregnancy to predict viraemia at delivery in HIV‐infected women initiating ART in South Africa: a simulation study
title_sort optimal timing of viral load monitoring during pregnancy to predict viraemia at delivery in hiv‐infected women initiating art in south africa: a simulation study
topic Short Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5978661/
https://www.ncbi.nlm.nih.gov/pubmed/29171179
http://dx.doi.org/10.1002/jia2.25000
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