In one paragraphArticle in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 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 itWhat 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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
21 authors.
Marie WijkDivision of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa.ORCID 0000-0001-6013-8210 Paolo DentiDivision of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa.ORCID 0000-0001-7494-079X Kamunkhwala GausiDivision of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa.ORCID 0000-0002-9002-8087 Bronwyn MyersFaculty of Health Sciences, Curtin enAble Institute, Curtin University, Bentley, Western Australia, Australia.ORCID 0000-0003-0235-6716 Tara CarneyMental Health, Alcohol, Substance Use and Tobacco Research Unit, South African Medical Research Council, Cape Town, South Africa.ORCID 0000-0001-5400-0565 Laura F WhiteDepartment of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA.ORCID 0000-0002-0588-8235 Danie TheronDepartment of Medical Services, Brewelskloof Hospital, Worcester, South Africa.
Charles D H ParryMental Health, Alcohol, Substance Use and Tobacco Research Unit, South African Medical Research Council, Cape Town, South Africa.ORCID 0000-0001-9787-2785 C Robert HorsburghDepartment of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA.ORCID 0000-0001-6838-7895 Noorjahn RawootDSI-NRF Centre of Excellence for Biomedical Tuberculosis Research/South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
Robin M WarrenDSI-NRF Centre of Excellence for Biomedical Tuberculosis Research/South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.ORCID 0000-0001-5741-7358 Richard CourtDivision of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa.ORCID 0000-0002-7927-6506 Sanjana G KulkarniDepartment of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0002-9883-610X Maha R FarhatDepartment of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0002-3871-5760 Chané BuysDesmond Tutu Tuberculosis Centre, Department of Paediatrics and Child Health, Stellenbosch University, Cape Town, South Africa.ORCID 0000-0003-3284-7823 Samantha MalatestaDepartment of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA.ORCID 0000-0003-0849-2348 Sarah E WeberDepartment of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA.ORCID 0000-0001-5828-8500 Suchitra KulkarniSection of Infectious Diseases, Department of Medicine, Boston University Chobanian Avedisian School of Medicine and Boston Medical Center, Boston, Massachusetts, USA.
Helen McIlleronDivision of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa.ORCID 0000-0002-0982-6226 Karen R JacobsonSection of Infectious Diseases, Department of Medicine, Boston University Chobanian Avedisian School of Medicine and Boston Medical Center, Boston, Massachusetts, USA.ORCID 0000-0003-4239-3685 Funding
Investigating bacterial contributions to TB treatment response: a focus on in-host pathogen dynamicsR01AI155765 · NIAID · HARVARD MEDICAL SCHOOL · PI FARHAT, MAHA · 2020 to 2024
$3.7MThe impact of alcohol consumption on TB treatment outcomesR01AI119037 · NIAID · BOSTON MEDICAL CENTER · PI JACOBSON, KAREN R · 2016 to 2020
$3.3MNational Institute of Allergy and Infectious Diseases R01AI119037NIAID NIH HHS R01 AI119037NIAID NIH HHS R01 AI155765Royal Society 220587/Z/20/ZWellcome Trust
6 · The paper itselfAbstract
backgroundQuantitative markers of tuberculosis disease burden are essential for assessing treatment response and optimizing therapeutic strategies. This study evaluated the impact of antimicrobial plasma drug levels on time-to-positivity (TTP) trajectories in patients with drug-susceptible tuberculosis and explored the relationship between bacillary clearance and treatment outcomes.
methodsPatients with drug susceptible pulmonary tuberculosis initiating treatment were recruited in Worcester, South Africa. Weekly sputum samples were collected for 12 weeks, with bacterial load quantified using Mycobacterial Growth Indicator Tubes, yielding TTP. Nonlinear mixed-effects modeling was used to empirically describe longitudinal TTP, assessing rifampicin exposure and other participant characteristics as covariates. TTP trajectories were stratified by treatment outcomes to identify trends between change in TTP and outcome.
resultsA total of 402 participants were included, of whom 60% were male. Median age was 37 years (interquartile ratio 26-48) and 90% had successful treatment outcomes (were cured or completed treatment) at 6 months with 3% who experienced treatment failure, 2% died, 1% transferred, and 4% were lost to follow-up. Baseline smear grade and lung cavitation decreased baseline TTP. Higher rifampicin concentration area under the curve was associated with faster longitudinal change in TTP, whereas lung cavitation and older age were associated with slower longitudinal change. Furthermore, slower change in TTP during the first 12 weeks of treatment was linked to treatment failure at 6 months.
conclusionsOur findings highlight the role of rifampicin plasma exposure in optimizing bacillary clearance and improving treatment outcomes, even within standard dosing regimens for drug-susceptible tuberculosis.
Indexed as
Antibiotics, AntitubercularAntitubercular AgentsRifampinTuberculosis, PulmonaryAdultBacterial LoadFemaleHumansMaleMiddle AgedMycobacterium tuberculosisSouth AfricaSputumTreatment OutcomeAntibiotics, AntitubercularAntitubercular AgentsRifampinbacillary clearancepharmacokinetics-pharmacodynamicsrifampicin exposuretreatment outcometuberculosis
Identifiers
PMID41133922
PMCPMC13189679
What OpenQuestion holds
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LicenceCC BY-NC-ND
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