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Record W2855009822 · doi:10.1371/journal.pmed.1002591

Treatment and outcomes in children with multidrug-resistant tuberculosis: A systematic review and individual patient data meta-analysis

2018· review· en· W2855009822 on OpenAlexaff
Elizabeth Harausz, Anthony J. Garcia‐Prats, Stephanie Law, H. Simon Schaaf, Tamara Kredo, James A. Seddon, Dick Menzies, Anna Turkova, Jay Achar, Farhana Amanullah, Pennan M. Barry, Mercedes C. Becerra, Edward D. Chan, Pei‐Chun Chan, Domnica Chiotan, Aldo Crossa, Peter Drobac, Lee Fairlie, Dennis Falzon, Jennifer Flood, Medea Gegia, Robert M. Hicks, Petros Isaakidis, SM Kadri, Beate Kampmann, Shabir A. Madhi, Elsé Marais, Andrei Mariandyshev, Ana Méndez‐Echevarría, Brittany K. Moore, Parpieva Nargiza, Iveta Ozere, Nesri Padayatchi, Saleem- ur-Rehman, Natasha Rybak, Neha Shah, Sangeeta Sharma, Tae Sun Shim, Alena Skrahina, Antoni Soriano‐Arandes, Martin van den Boom, Marieke J. van der Werf, Tjip S. van der Werf, Bhanu Williams, Elena Yablokova, Jae‐Joon Yim, Jennifer Furin, Anneke C. Hesseling

Bibliographic record

VenuePLoS Medicine · 2018
Typereview
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill University
FundersMedical Research CouncilCenters for Disease Control and PreventionFondazione Salvatore MaugeriOtsuka PharmaceuticalUniversity of Cape TownOtsuka Pharmaceutical Development and CommercializationIndus HospitalNew York City Department of Health and Mental HygieneWorld Health OrganizationNational Institute for Health and Care ResearchUnited States Agency for International Development
KeywordsTuberculosisMeta-analysisMedicineSystematic reviewExtensively drug-resistant tuberculosisMEDLINEMultiple drug resistanceIntensive care medicinePediatricsMycobacterium tuberculosisInternal medicineDrug resistancePathologyBiologyGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: An estimated 32,000 children develop multidrug-resistant tuberculosis (MDR-TB; Mycobacterium tuberculosis resistant to isoniazid and rifampin) each year. Little is known about the optimal treatment for these children. METHODS AND FINDINGS: To inform the pediatric aspects of the revised World Health Organization (WHO) MDR-TB treatment guidelines, we performed a systematic review and individual patient data (IPD) meta-analysis, describing treatment outcomes in children treated for MDR-TB. To identify eligible reports we searched PubMed, LILACS, Embase, The Cochrane Library, PsychINFO, and BioMedCentral databases through 1 October 2014. To identify unpublished data, we reviewed conference abstracts, contacted experts in the field, and requested data through other routes, including at national and international conferences and through organizations working in pediatric MDR-TB. A cohort was eligible for inclusion if it included a minimum of three children (aged <15 years) who were treated for bacteriologically confirmed or clinically diagnosed MDR-TB, and if treatment outcomes were reported. The search yielded 2,772 reports; after review, 33 studies were eligible for inclusion, with IPD provided for 28 of these. All data were from published or unpublished observational cohorts. We analyzed demographic, clinical, and treatment factors as predictors of treatment outcome. In order to obtain adjusted estimates, we used a random-effects multivariable logistic regression (random intercept and random slope, unless specified otherwise) adjusted for the following covariates: age, sex, HIV infection, malnutrition, severe extrapulmonary disease, or the presence of severe disease on chest radiograph. We analyzed data from 975 children from 18 countries; 731 (75%) had bacteriologically confirmed and 244 (25%) had clinically diagnosed MDR-TB. The median age was 7.1 years. Of 910 (93%) children with documented HIV status, 359 (39%) were infected with HIV. When compared to clinically diagnosed patients, children with confirmed MDR-TB were more likely to be older, to be infected with HIV, to be malnourished, and to have severe tuberculosis (TB) on chest radiograph (p < 0.001 for all characteristics). Overall, 764 of 975 (78%) had a successful treatment outcome at the conclusion of therapy: 548/731 (75%) of confirmed and 216/244 (89%) of clinically diagnosed children (absolute difference 14%, 95% confidence interval [CI] 8%-19%, p < 0.001). Treatment was successful in only 56% of children with bacteriologically confirmed TB who were infected with HIV who did not receive any antiretroviral treatment (ART) during MDR-TB therapy, compared to 82% in children infected with HIV who received ART during MDR-TB therapy (absolute difference 26%, 95% CI 5%-48%, p = 0.006). In children with confirmed MDR-TB, the use of second-line injectable agents and high-dose isoniazid (15-20 mg/kg/day) were associated with treatment success (adjusted odds ratio [aOR] 2.9, 95% CI 1.0-8.3, p = 0.041 and aOR 5.9, 95% CI 1.7-20.5, p = 0.007, respectively). These findings for high-dose isoniazid may have been affected by site effect, as the majority of patients came from Cape Town. Limitations of this study include the difficulty of estimating the treatment effects of individual drugs within multidrug regimens, only observational cohort studies were available for inclusion, and treatment decisions were based on the clinician's perception of illness, with resulting potential for bias. CONCLUSIONS: This study suggests that children respond favorably to MDR-TB treatment. The low success rate in children infected with HIV who did not receive ART during their MDR-TB treatment highlights the need for ART in these children. Our findings of individual drug effects on treatment outcome should be further evaluated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.050
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.022
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.050
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0220.040
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.214
GPT teacher head0.410
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations150
Published2018
Admission routes1
Has abstractyes

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