Evidence map›Paper›PMID 26423252›Full record

ArticleThe lancet. HIV2015

Monitoring and switching of first-line antiretroviral therapy in adult treatment cohorts in sub-Saharan Africa: collaborative analysis.

Andreas D Haas, Olivia Keiser, Eric Balestre, Steve Brown, Emmanuel Bissagnene, Cleophas Chimbetete, François Dabis, Mary-Ann Davies, Christopher J Hoffmann, Patrick Oyaro and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The lancet. HIV, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
82citing papers in PubMed, 3 pooled it
–field-weighted citation impact
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

82 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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22 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

18 authors.

Andreas D HaasInstitute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Olivia KeiserInstitute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Eric BalestreCentre de Recherche INSERM U897, Epidemiologie-Biostatistique, Institut de Santé Publique, Epidémiologie et Développement, Université de Bordeaux, Bordeaux, France.
Steve BrownIndiana University School of Medicine, Indianapolis, IN, USA.
Emmanuel BissagneneService de Maladies Infectieuses et Tropicales, Centre Hospitalier Universitaire de Treichville, Abidjan, Côte d'Ivoire.
Cleophas ChimbeteteNewlands Clinic, Harare, Zimbabwe.
François DabisCentre de Recherche INSERM U897, Epidemiologie-Biostatistique, Institut de Santé Publique, Epidémiologie et Développement, Université de Bordeaux, Bordeaux, France.
Mary-Ann DaviesCentre for Infectious Disease Epidemiology and Research, University of Cape Town, Cape Town, South Africa.
Christopher J HoffmannJohns Hopkins University, Baltimore, MD, USA; Aurum Institute, Johannesburg, South Africa.
Patrick OyaroKenya Medical Research Institute - RCTP FACES Program, Kisumu, Kenya.
Rosalind Parkes-RatanshiInfectious Diseases Institute, Mulago Hospital Complex, Kampala, Uganda.
Steven J ReynoldsRakai Health Sciences Program, Entebbe, Uganda; Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA; Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Izukanji SikazweCentre for Infectious Disease Research in Zambia, Lusaka, Zambia.
Kara Wools-KaloustianIndiana University School of Medicine, Indianapolis, IN, USA.
D Marcel ZannouFaculté des Sciences de la Santé de l'Université d'Abomey-Calavi, and Centre de Traitement Ambulatoire du Centre National Hospitalier Universitaire Hubert Koutoukou Maga, Cotonou, Benin.
Gilles WandelerInstitute of Social and Preventive Medicine, University of Bern, Bern, Switzerland; Department of Infectious Diseases, Bern University Hospital and University of Bern, Bern, Switzerland.
Matthias EggerInstitute of Social and Preventive Medicine, University of Bern, Bern, Switzerland; Centre for Infectious Disease Epidemiology and Research, University of Cape Town, Cape Town, South Africa. Electronic address: egger@ispm.unibe.ch.
IeDEA southern Africa, east Africa, and west Africa

Funding

East Africa International Epidemiology Database to evaluate AIDS (IeDEA) Regional ConsortiumU01AI069911 · NIAID · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI AGGREY SEMWENDERO SEMEERE, Kara Kay Wools-Kaloustian · 2006 to 2026
$57.7M
Observational Antiretroviral Studies In Southern Africa (OASIS) CollaborationU01AI069924 · NIAID · UNIVERSITAT BERN · PI Cleophas Chimbetete, Mary-Ann Davies · 2006 to 2026
$55.9M
WADA (West Africa Database on Antiretroviral Therapy) CollaborationU01AI069919 · NIAID · UNIVERSITY OF BORDEAUX II · PI Didier Koumavi Ekouevi, Antoine Jaquet · 2006 to 2026
$29.9M
Intramural NIH HHSNIAID NIH HHS 5U01-AI069924NIAID NIH HHS U01 AI069911NIAID NIH HHS U01AI069911NIAID NIH HHS U01 AI069919NIAID NIH HHS U01AI069919NIAID NIH HHS U01 AI069924
6 · The paper itself

Abstract

backgroundHIV-1 viral load testing is recommended to monitor antiretroviral therapy (ART) but is not universally available. The aim of our study was to assess monitoring of first-line ART and switching to second-line ART in sub-Saharan Africa.

methodsWe did a collaborative analysis of cohort studies from 16 countries in east Africa, southern Africa, and west Africa that participate in the international epidemiological database to evaluate AIDS (IeDEA). We included adults infected with HIV-1 who started combination ART between January, 2004, and January, 2013. We defined switching of ART as a change from a non-nucleoside reverse-transcriptase inhibitor (NNRTI)-based regimen to one including a protease inhibitor, with adjustment of one or more nucleoside reverse-transcriptase inhibitors (NRTIs). Virological and immunological failures were defined according to WHO criteria. We calculated cumulative probabilities of switching and hazard ratios with 95% CIs comparing routine viral load monitoring, targeted viral load monitoring, CD4 monitoring, and clinical monitoring, adjusting for programme and individual characteristics.

findingsOf 297,825 eligible patients, 10,352 (3%) switched to second-line ART during 782 ,412 person-years of follow-up. Compared with CD4 monitoring, hazard ratios for switching were 3·15 (95% CI 2·92-3·40) for routine viral load monitoring, 1·21 (1·13-1·30) for targeted viral load monitoring, and 0·49 (0·43-0·56) for clinical monitoring. Of 6450 patients with confirmed virological failure, 58·0% (95% CI 56·5-59·6) switched by 2 years, and of 15,892 patients with confirmed immunological failure, 19·3% (18·5-20·0) switched by 2 years. Of 10,352 patients who switched, evidence of treatment failure based on one CD4 count or viral load measurement ranged from 86 (32%) of 268 patients with clinical monitoring to 3754 (84%) of 4452 with targeted viral load monitoring. Median CD4 counts at switching were 215 cells per μL (IQR 117-335) with routine viral load monitoring, but were lower with other types of monitoring (range 114-133 cells per μL).

interpretationOverall, few patients switched to second-line ART and switching happened late in the absence of routine viral load monitoring. Switching was more common and happened earlier after initiation of ART with targeted or routine viral load testing.

fundingNational Institute of Allergy and Infectious Diseases, Swiss National Science Foundation.

Indexed as

Drug MonitoringAdultAfrica South of the SaharaAnti-HIV AgentsAntiretroviral Therapy, Highly ActiveCD4 Lymphocyte CountCohort StudiesDrug SubstitutionFemaleHIV-1HIV InfectionsHIV Protease InhibitorsHumansMaleReverse Transcriptase InhibitorsTreatment FailureAnti-HIV AgentsHIV Protease InhibitorsReverse Transcriptase Inhibitors

Identifiers

PMID26423252
PMCPMC4500741

What OpenQuestion holds

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Registered trials

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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.