Evidence map›Paper›PMID 34543329›Full record

ArticlePloS one2021

Predictors of unfavorable responses to therapy in rifampicin-sensitive pulmonary tuberculosis using an integrated approach of radiological presentation and sputum mycobacterial burden.

Narendran Gopalan, Vignes Anand Srinivasalu, Ponnuraja Chinnayan, Banurekha Velayutham, Adhin Bhaskar, Ramesh Santhanakrishnan, Thirumaran Senguttuvan, Sridhar Rathinam, Mahilmaran Ayyamperumal, Kumar Satagopan and 17 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
0.4field-weighted citation impact, top 36% 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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.

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  12. PET/CT guided tuberculosis treatment shortening: a randomized trial.medRxiv : the preprint server for health sciences · 2024
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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

27 authors at 4 institutions in 2 countries.

Narendran GopalanDepartment of Clinical Research, National Institute for Research in Tuberculosis (formerly Tuberculosis Research Centre), Indian Council of Medical Research, Chennai, Tamil Nadu, India.ORCID 0000-0002-8049-7541
Vignes Anand SrinivasaluDepartment of Clinical Research, National Institute for Research in Tuberculosis (formerly Tuberculosis Research Centre), Indian Council of Medical Research, Chennai, Tamil Nadu, India.ORCID 0000-0002-4932-0617
Ponnuraja ChinnayanDepartment of Clinical Research, National Institute for Research in Tuberculosis (formerly Tuberculosis Research Centre), Indian Council of Medical Research, Chennai, Tamil Nadu, India.
Banurekha VelayuthamDepartment of Clinical Research, National Institute for Research in Tuberculosis (formerly Tuberculosis Research Centre), Indian Council of Medical Research, Chennai, Tamil Nadu, India.
Adhin BhaskarDepartment of Clinical Research, National Institute for Research in Tuberculosis (formerly Tuberculosis Research Centre), Indian Council of Medical Research, Chennai, Tamil Nadu, India.
Ramesh SanthanakrishnanDepartment of Clinical Research, National Institute for Research in Tuberculosis (formerly Tuberculosis Research Centre), Indian Council of Medical Research, Chennai, Tamil Nadu, India.
Thirumaran SenguttuvanDepartment of Clinical Research, National Institute for Research in Tuberculosis (formerly Tuberculosis Research Centre), Indian Council of Medical Research, Chennai, Tamil Nadu, India.ORCID 0000-0003-1497-8678
Sridhar RathinamDepartment of Thoracic Medicine, Government Hospital of Thoracic Medicine Tambaram, Chennai, Tamil Nadu, India.
Mahilmaran AyyamperumalDepartment of Thoracic Medicine, Institute of Thoracic Medicine, Madras Medical College, Chennai, Tamil Nadu, India.
Kumar SatagopanDepartment of Thoracic Medicine, Government Hospital of Thoracic Medicine Tambaram, Chennai, Tamil Nadu, India.
Dhanalakshmi RajendranDepartment of Clinical Research, National Institute for Research in Tuberculosis (formerly Tuberculosis Research Centre), Indian Council of Medical Research, Chennai, Tamil Nadu, India.
Tamizhselvan ManoharanDepartment of Clinical Research, National Institute for Research in Tuberculosis (formerly Tuberculosis Research Centre), Indian Council of Medical Research, Chennai, Tamil Nadu, India.
Sekar LakshmananDepartment of Clinical Research, National Institute for Research in Tuberculosis (formerly Tuberculosis Research Centre), Indian Council of Medical Research, Chennai, Tamil Nadu, India.
Paulkumaran ParamasivamDepartment of Clinical Research, National Institute for Research in Tuberculosis (formerly Tuberculosis Research Centre), Indian Council of Medical Research, Chennai, Tamil Nadu, India.
Dhanalakshmi AngamuthuDepartment of Clinical Research, National Institute for Research in Tuberculosis (formerly Tuberculosis Research Centre), Indian Council of Medical Research, Chennai, Tamil Nadu, India.
Mangalambal GanesanDepartment of Clinical Research, National Institute for Research in Tuberculosis (formerly Tuberculosis Research Centre), Indian Council of Medical Research, Chennai, Tamil Nadu, India.
John Washington Easudoss ArockiaDepartment of Clinical Research, National Institute for Research in Tuberculosis (formerly Tuberculosis Research Centre), Indian Council of Medical Research, Chennai, Tamil Nadu, India.
Ramesh Babu VenkatesanDepartment of Clinical Research, National Institute for Research in Tuberculosis (formerly Tuberculosis Research Centre), Indian Council of Medical Research, Chennai, Tamil Nadu, India.
Venkatesan LakshmipathyDepartment of Clinical Research, National Institute for Research in Tuberculosis (formerly Tuberculosis Research Centre), Indian Council of Medical Research, Chennai, Tamil Nadu, India.
Shivakumar ShanmughamDepartment of Clinical Research, National Institute for Research in Tuberculosis (formerly Tuberculosis Research Centre), Indian Council of Medical Research, Chennai, Tamil Nadu, India.
Balaji SubramanyamDepartment of Clinical Research, National Institute for Research in Tuberculosis (formerly Tuberculosis Research Centre), Indian Council of Medical Research, Chennai, Tamil Nadu, India.
Shakila ShankarDepartment of Clinical Research, National Institute for Research in Tuberculosis (formerly Tuberculosis Research Centre), Indian Council of Medical Research, Chennai, Tamil Nadu, India.
Jawahar Mohideen ShaheedDepartment of Clinical Research, National Institute for Research in Tuberculosis (formerly Tuberculosis Research Centre), Indian Council of Medical Research, Chennai, Tamil Nadu, India.
Baskaran DhanarajDepartment of Clinical Research, National Institute for Research in Tuberculosis (formerly Tuberculosis Research Centre), Indian Council of Medical Research, Chennai, Tamil Nadu, India.
Narayanan Paranji RamiyengarDepartment of Clinical Research, National Institute for Research in Tuberculosis (formerly Tuberculosis Research Centre), Indian Council of Medical Research, Chennai, Tamil Nadu, India.
Soumya SwaminathanChief Scientist, World Health Organization, Geneva, Switzerland.
Padmapriyadarsini ChandrasekaranDepartment of Clinical Research, National Institute for Research in Tuberculosis (formerly Tuberculosis Research Centre), Indian Council of Medical Research, Chennai, Tamil Nadu, India.
Indian Council of Medical Research · INGovernment Hospital of Thoracic Medicine · INMadras Medical College · INWorld Health Organization · CH

Funding

World Health Organization 001
6 · The paper itself

Abstract

introductionDespite the exalted status of sputum mycobacterial load for gauging pulmonary tuberculosis treatment and progress, Chest X-rays supplement valuable information for taking instantaneous therapeutic decisions, especially during the COVID-19 pandemic. Even though literature on individual parameters is overwhelming, few studies have explored the interaction between radiographic parameters denoting severity with mycobacterial burden signifying infectivity. By using a sophisticated approach of integrating Chest X-ray parameters with sputum mycobacterial characteristics, evaluated at all the three crucial time points of TB treatment namely pre-treatment, end of intensive phase and completion of treatment, utilizing the interactive Cox Proportional Hazards model, we aimed to precisely deduce predictors of unfavorable response to TB treatment. MATERIALS AND

methodWe extracted de-identified data from well characterized clinical trial cohorts that recruited rifampicin-sensitive Pulmonary TB patients without any comorbidities, taking their first spell of anti-tuberculosis therapy under supervision and meticulous follow up for 24 months post treatment completion, to accurately predict TB outcomes. Radiographic data independently obtained, interpreted by two experienced pulmonologists was collated with demographic details and, sputum smear and culture grades of participants by an independent statistician and analyzed using the Cox Proportional Hazards model, to not only adjust for confounding factors including treatment effect, but also explore the interaction between radiological and bacteriological parameters for better therapeutic application.

resultsOf 667 TB patients with data available, cavitation, extent of involvement, lower zone involvement, smear and culture grade at baseline were significant parameters predisposing to an unfavorable TB treatment outcome in the univariate analysis. Reduction in radiological lesions in Chest X-ray by at least 50% at 2 months and 75% at the end of treatment helped in averting unfavorable responses. Smear and Culture conversion at the end of 2 months was highly significant as a predictor (p<0.001). In the multivariate analysis, the adjusted hazards ratios (HR) for an unfavorable response to TB therapy for extent of involvement, baseline cavitation and persistence (post treatment) were 1.21 (95% CI: 1.01-1.44), 1.73 (95% CI: 1.05-2.84) and 2.68 (95% CI: 1.4-5.12) respectively. A 3+ smear had an HR of 1.94 (95% CI: 0.81-4.64). Further probing into the interaction, among patients with 3+ and 2+ smears, HRs for cavitation were 3.26 (95% CI: 1.33-8.00) and 1.92 (95% CI: 0.80-4.60) while for >2 zones, were 3.05 (95% CI: 1.12-8.23) and 1.92 (95% CI: 0.72-5.08) respectively. Patients without cavitation, zonal involvement <2, and a smear grade less than 2+ had a better prognosis and constituted minimal disease.

conclusionBaseline Cavitation, Opacities occupying >2 zones and 3+ smear grade individually and independently forecasted a poorer TB outcome. The interaction model revealed that Zonal involvement confined to 2 zones, without a cavity and smear grade up to 2+, constituting "minimal disease", had a better prognosis. Radiological clearance >50% along with smear conversion at the end of intensive phase of treatment, observed to be a reasonable alternative to culture conversion in predicting a successful outcome. These parameters may potentially take up key positions as stratification factors for future trials contemplating on shorter TB regimens.

Indexed as

AdultFemaleHumansKaplan-Meier EstimateMaleMultivariate AnalysisMycobacterium tuberculosisProportional Hazards ModelsRifampinSputumTreatment OutcomeTuberculosis, PulmonaryYoung AdultRifampin

Identifiers

PMID34543329
PMCPMC8452066
OpenAlexW3200996066

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