Evidence map›Paper›PMID 29171179›Full record

ArticleJournal of the International AIDS Society2017

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.

Maia Lesosky, Tracy Glass, Elton Mukonda, Nei-Yuan Hsiao, Elaine J Abrams, Landon Myer

Open access · goldAbstract read
In one paragraph

Article in Journal of the International AIDS Society, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
1.8field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 26 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 2 countries.

Maia LesoskyDivision of Epidemiology & Biostatistics, School of Public Health & Family Medicine, University of Cape Town, Cape Town, South Africa.
Tracy GlassDivision of Epidemiology & Biostatistics, School of Public Health & Family Medicine, University of Cape Town, Cape Town, South Africa.
Elton MukondaDivision of Epidemiology & Biostatistics, School of Public Health & Family Medicine, University of Cape Town, Cape Town, South Africa.
Nei-Yuan HsiaoNational Health Laboratory Services, Cape Town, South Africa.
Elaine J AbramsICAP, Mailman School of Public Health, Columbia University, New York, NY, USA.
Landon MyerDivision of Epidemiology & Biostatistics, School of Public Health & Family Medicine, University of Cape Town, Cape Town, South Africa.
University of Cape Town · ZAColumbia University · US

Funding

Strategies to optimize ART services for maternal&child health: the MCH-ART studyR01HD074558 · NICHD · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI ABRAMS, ELAINE JANINE, MYER, BENJAMIN LANDON · 2012 to 2014
$2.9M
Optimizing routine HIV viral load monitoring in pregnant and postpartum womenR21HD093463 · NICHD · UNIVERSITY OF CAPE TOWN · PI LESOSKY, MAIA ROSE · 2018 to 2019
$287k
NICHD NIH HHS R01 HD074558NICHD NIH HHS R21 HD093463
6 · The paper itself

Abstract

introductionHIV 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.

methodsWe 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.

conclusionsDevelopment 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.

Indexed as

Viral LoadAdultAnti-HIV AgentsCohort StudiesComputer SimulationFemaleFertilizationHIV InfectionsHumansInfectious Disease Transmission, VerticalPoint-of-Care SystemsPredictive Value of TestsPregnancyPregnancy Complications, InfectiousSerologic TestsSouth AfricaAnti-HIV Agentsantiretroviral therapyHIVmathematical modelpregnancysimulationviral load monitoring

Identifiers

PMID29171179
PMCPMC5978661
OpenAlexW2775355568

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.