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Enregistrement 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 sur 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

Notice bibliographique

RevuePLoS Medicine · 2018
Typereview
Langueen
DomaineMedicine
ThématiqueTuberculosis Research and Epidemiology
Établissements canadiensMcGill University
Organismes subventionnairesMedical 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
Mots-clésTuberculosisMeta-analysisMedicineSystematic reviewExtensively drug-resistant tuberculosisMEDLINEMultiple drug resistanceIntensive care medicinePediatricsMycobacterium tuberculosisInternal medicineDrug resistancePathologyBiologyGenetics

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Méta-épidémiologie (sens large)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Méta-analyse · Signal consensuel: Méta-analyse
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,342
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,003
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0200,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,214
Tête enseignante GPT0,410
Écart entre enseignants0,197 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeMéta-analyse
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations150
Publié2018
Routes d'admission1
Résumé présentoui

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