Evidence map›Paper›PMID 41921598›Full record

ArticleAnnals of epidemiology2026

Early adherence predicts treatment outcomes and mortality in people living with HIV and multidrug-resistant tuberculosis.

Kevin J Guzman, Rubeshan Perumal, Allison Wolf, Xuan Lu, Resha Boodhram, Boitumelo Seepamore, Karl Reis, Matthew J Cummings, K Rivet Amico, Ying Kuen Cheung and 6 more

Abstract read
In one paragraph

Article in Annals of epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

16 authors.

Kevin J GuzmanDivision of Pulmonary, Allergy, and Critical Care Medicine, Columbia University Irving Medical Center, New York City, NY, United States.
Rubeshan PerumalCentre for the AIDS Programme of Research in South Africa (CAPRISA), South African Medical Research Council (SAMRC)-CAPRISA-TB-HIV Pathogenesis and Treatment Research Unit, University of KwaZulu-Natal, Durban, South Africa; Department of Pulmonary and Critical Care, School of Clinical Medicine, University of KwaZulu-Natal, Durban, South Africa.
Allison WolfDivision of Pulmonary, Allergy, and Critical Care Medicine, Columbia University Irving Medical Center, New York City, NY, United States.
Xuan LuDivision of Pulmonary, Allergy, and Critical Care Medicine, Columbia University Irving Medical Center, New York City, NY, United States.
Resha BoodhramCentre for the AIDS Programme of Research in South Africa (CAPRISA), South African Medical Research Council (SAMRC)-CAPRISA-TB-HIV Pathogenesis and Treatment Research Unit, University of KwaZulu-Natal, Durban, South Africa.
Boitumelo SeepamoreCentre for the AIDS Programme of Research in South Africa (CAPRISA), South African Medical Research Council (SAMRC)-CAPRISA-TB-HIV Pathogenesis and Treatment Research Unit, University of KwaZulu-Natal, Durban, South Africa; School of Social Sciences, University of KwaZulu-Natal, Durban, South Africa.
Karl ReisDepartment of Medicine, University of Washington, Seattle, WA, United States.
Matthew J CummingsDivision of Pulmonary, Allergy, and Critical Care Medicine, Columbia University Irving Medical Center, New York City, NY, United States.
K Rivet AmicoUniversity of Michigan School of Public Health, Ann Arbor, MI, United States.
Ying Kuen CheungDepartment of Biostatistics, Mailman School of Public Health, Columbia University, New York City, NY, United States.
Gerald FriedlandDepartment of Medicine, Division of Infectious Disease, Yale University School of Medicine, New Haven, CT, United States.
Jennifer ZelnickGraduate School of Social Work, Touro University, New York City, NY, United States.
Amrita DaftaryCentre for the AIDS Programme of Research in South Africa (CAPRISA), South African Medical Research Council (SAMRC)-CAPRISA-TB-HIV Pathogenesis and Treatment Research Unit, University of KwaZulu-Natal, Durban, South Africa; School of Global Health and Dahdaleh Institue of Global Health Research, York University, Toranto, Canada.
Nesri PadayatchiCentre for the AIDS Programme of Research in South Africa (CAPRISA), South African Medical Research Council (SAMRC)-CAPRISA-TB-HIV Pathogenesis and Treatment Research Unit, University of KwaZulu-Natal, Durban, South Africa.
Kogieleum NaidooCentre for the AIDS Programme of Research in South Africa (CAPRISA), South African Medical Research Council (SAMRC)-CAPRISA-TB-HIV Pathogenesis and Treatment Research Unit, University of KwaZulu-Natal, Durban, South Africa.
Max R O'DonnellDivision of Pulmonary, Allergy, and Critical Care Medicine, Columbia University Irving Medical Center, New York City, NY, United States; Centre for the AIDS Programme of Research in South Africa (CAPRISA), South African Medical Research Council (SAMRC)-CAPRISA-TB-HIV Pathogenesis and Treatment Research Unit, University of KwaZulu-Natal, Durban, South Africa; Department of Epidemiology, Columbia University Irving Medical Center, New York City, NY, United States. Electronic address: mo2130@columbia.edu.

Funding

The Columbia University Training Program in Lung ScienceT32HL105323 · NHLBI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Jahar Bhattacharya, Wellington V. Cardoso · 2011 to 2026
$4.7M
Adaptive evaluation of mHealth and conventional adherence support interventions to optimize outcomes with new treatment regimens for drug-resistant tuberculosis and HIV in South AfricaR01AI167795 · NIAID · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Max O'Donnell · 2022 to 2026
$2.9M
Promoting Engagement in the Drug Resistant TB/HIV Care Continuum in South AfricaR01AI124413 · NIAID · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI O'DONNELL, MAX, PADAYATCHI, NESRI · 2016 to 2020
$2.7M
NHLBI NIH HHS T32 HL105323NIAID NIH HHS R01 AI124413NIAID NIH HHS R01 AI167795Wellcome Trust
6 · The paper itself

Abstract

purposeTreatment success for RR/MDR-TB remains suboptimal, particularly among people living with HIV. Electronic dose monitors support adherence, but strategies to act on these data are limited. We hypothesized that early adherence to bedaquiline predicts treatment outcomes and could guide individualized interventions.

methodsWe prospectively enrolled adults with RR/MDR-TB and HIV initiating bedaquiline-containing regimens in KwaZulu-Natal, South Africa. Adherence was measured using real-time electronic dose monitors. Weekly adherence data through week 24 trained time-aware XGBoost models to predict end-of-treatment outcomes, and latent class growth analysis (LCGA) at the earliest predictive time point identified distinct adherence trajectories.

resultsAmong 282 participants, model performance improved over time, achieving good discrimination by week 4 (AUC > 0.80), predictive of subsequent adherence. LCGA using 4-week data identified three adherence trajectories: high, moderate-stable, and early-declining. Favorable outcomes were lowest in the early-declining group (55% vs. 77% and 76%), which also had the highest mortality (36% vs. 12% and 12%). Cox models confirmed a higher risk of unfavorable outcomes among early decliners.

conclusionAdherence in the first four weeks of bedaquiline therapy provides a powerful, early signal of treatment outcomes. Real-time adherence monitoring could enable risk stratification and tailored interventions to improve RR/MDR-TB and HIV care.

Indexed as

Anti-HIV AgentsAntitubercular AgentsDiarylquinolinesDrug MonitoringHIV InfectionsMedication AdherenceTuberculosis, Multidrug-ResistantAdultFemaleHumansMaleProspective StudiesSouth AfricaTreatment OutcomeAnti-HIV AgentsAntitubercular AgentsbedaquilineDiarylquinolinesbedaquilineHIVlatent class growth analysismedication adherenceRR/MDR-TBXGBoost

Identifiers

PMID41921598
PMCPMC13401231

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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.