MétaCan
Menu
Retour à la cohorte
Enregistrement W2950500516 · doi:10.1002/14651858.cd013359.pub2

Xpert MTB/RIF and Xpert MTB/RIF Ultra assays for active tuberculosis and rifampicin resistance in children

2020· review· en· W2950500516 sur OpenAlexfundno aff
Alexander Kay, Lucía González Fernández, Yemisi Takwoingi, Michael Eisenhut, Anne Detjen, Karen R Steingart, Anna M. Mandalakas

Notice bibliographique

RevueCochrane Database of Systematic Reviews · 2020
Typereview
Langueen
DomaineMedicine
ThématiqueTuberculosis Research and Epidemiology
Établissements canadiensnon disponible
Organismes subventionnairesMcGill UniversityRice UniversityWorld Health OrganizationUnited States Agency for International Development
Mots-clésMedicineTuberculosisRifampicinMycobacterium tuberculosisGeneXpert MTB/RIFTuberculosis diagnosisDrug resistanceDiagnostic testInternal medicineVirologyImmunologyPediatricsPathologyMicrobiology

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Every year, at least one million children become ill with tuberculosis and around 200,000 children die. Xpert MTB/RIF and Xpert Ultra are World Health Organization (WHO)-recommended rapid molecular tests that simultaneously detect tuberculosis and rifampicin resistance in adults and children with signs and symptoms of tuberculosis, at lower health system levels. To inform updated WHO guidelines on molecular assays, we performed a systematic review on the diagnostic accuracy of these tests in children presumed to have active tuberculosis. OBJECTIVES: Primary objectives • To determine the diagnostic accuracy of Xpert MTB/RIF and Xpert Ultra for (a) pulmonary tuberculosis in children presumed to have tuberculosis; (b) tuberculous meningitis in children presumed to have tuberculosis; (c) lymph node tuberculosis in children presumed to have tuberculosis; and (d) rifampicin resistance in children presumed to have tuberculosis - For tuberculosis detection, index tests were used as the initial test, replacing standard practice (i.e. smear microscopy or culture) - For detection of rifampicin resistance, index tests replaced culture-based drug susceptibility testing as the initial test Secondary objectives • To compare the accuracy of Xpert MTB/RIF and Xpert Ultra for each of the four target conditions • To investigate potential sources of heterogeneity in accuracy estimates - For tuberculosis detection, we considered age, disease severity, smear-test status, HIV status, clinical setting, specimen type, high tuberculosis burden, and high tuberculosis/HIV burden - For detection of rifampicin resistance, we considered multi-drug-resistant tuberculosis burden • To compare multiple Xpert MTB/RIF or Xpert Ultra results (repeated testing) with the initial Xpert MTB/RIF or Xpert Ultra result SEARCH METHODS: We searched the Cochrane Infectious Diseases Group Specialized Register, MEDLINE, Embase, Science Citation Index, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, the WHO International Clinical Trials Registry Platform, ClinicalTrials.gov, and the International Standard Randomized Controlled Trials Number (ISRCTN) Registry up to 29 April 2019, without language restrictions. SELECTION CRITERIA: Randomized trials, cross-sectional trials, and cohort studies evaluating Xpert MTB/RIF or Xpert Ultra in HIV-positive and HIV-negative children younger than 15 years. Reference standards comprised culture or a composite reference standard for tuberculosis and drug susceptibility testing or MTBDRplus (molecular assay for detection of Mycobacterium tuberculosis and drug resistance) for rifampicin resistance. We included studies evaluating sputum, gastric aspirate, stool, nasopharyngeal or bronchial lavage specimens (pulmonary tuberculosis), cerebrospinal fluid (tuberculous meningitis), fine needle aspirates, or surgical biopsy tissue (lymph node tuberculosis). DATA COLLECTION AND ANALYSIS: Two review authors independently extracted data and assessed study quality using the Quality Assessment of Studies of Diagnostic Accuracy - Revised (QUADAS-2). For each target condition, we used the bivariate model to estimate pooled sensitivity and specificity with 95% confidence intervals (CIs). We stratified all analyses by type of reference standard. We assessed certainty of evidence using the GRADE approach. MAIN RESULTS: For pulmonary tuberculosis, 299 data sets (68,544 participants) were available for analysis; for tuberculous meningitis, 10 data sets (423 participants) were available; for lymph node tuberculosis, 10 data sets (318 participants) were available; and for rifampicin resistance, 14 data sets (326 participants) were available. Thirty-nine studies (80%) took place in countries with high tuberculosis burden. Risk of bias was low except for the reference standard domain, for which risk of bias was unclear because many studies collected only one specimen for culture. Detection of pulmonary tuberculosis For sputum specimens, Xpert MTB/RIF pooled sensitivity (95% CI) and specificity (95% CI) verified by culture were 64.6% (55.3% to 72.9%) (23 studies, 493 participants; moderate-certainty evidence) and 99.0% (98.1% to 99.5%) (23 studies, 6119 participants; moderate-certainty evidence). For other specimen types (nasopharyngeal aspirate, 4 studies; gastric aspirate, 14 studies; stool, 11 studies), Xpert MTB/RIF pooled sensitivity ranged between 45.7% and 73.0%, and pooled specificity ranged between 98.1% and 99.6%. For sputum specimens, Xpert Ultra pooled sensitivity (95% CI) and specificity (95% CI) verified by culture were 72.8% (64.7% to 79.6%) (3 studies, 136 participants; low-certainty evidence) and 97.5% (95.8% to 98.5%) (3 studies, 551 participants; high-certainty evidence). For nasopharyngeal specimens, Xpert Ultra sensitivity (95% CI) and specificity (95% CI) were 45.7% (28.9% to 63.3%) and 97.5% (93.7% to 99.3%) (1 study, 195 participants). For all specimen types, Xpert MTB/RIF and Xpert Ultra sensitivity were lower against a composite reference standard than against culture. Detection of tuberculous meningitis For cerebrospinal fluid, Xpert MTB/RIF pooled sensitivity and specificity, verified by culture, were 54.0% (95% CI 27.8% to 78.2%) (6 studies, 28 participants; very low-certainty evidence) and 93.8% (95% CI 84.5% to 97.6%) (6 studies, 213 participants; low-certainty evidence). Detection of lymph node tuberculosis For lymph node aspirates or biopsies, Xpert MTB/RIF pooled sensitivity and specificity, verified by culture, were 90.4% (95% CI 55.7% to 98.6%) (6 studies, 68 participants; very low-certainty evidence) and 89.8% (95% CI 71.5% to 96.8%) (6 studies, 142 participants; low-certainty evidence). Detection of rifampicin resistance Xpert MTB/RIF pooled sensitivity and specificity were 90.0% (67.6% to 97.5%) (6 studies, 20 participants; low-certainty evidence) and 98.3% (87.7% to 99.8%) (6 studies, 203 participants; moderate-certainty evidence). AUTHORS' CONCLUSIONS: We found Xpert MTB/RIF sensitivity to vary by specimen type, with gastric aspirate specimens having the highest sensitivity followed by sputum and stool, and nasopharyngeal specimens the lowest; specificity in all specimens was > 98%. Compared with Xpert MTB/RIF, Xpert Ultra sensitivity in sputum was higher and specificity slightly lower. Xpert MTB/RIF was accurate for detection of rifampicin resistance. Xpert MTB/RIF was sensitive for diagnosing lymph node tuberculosis. For children with presumed tuberculous meningitis, treatment decisions should be based on the entirety of clinical information and treatment should not be withheld based solely on an Xpert MTB/RIF result. The small numbers of studies and participants, particularly for Xpert Ultra, limits our confidence in the precision of these estimates.

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)0,007
score de la tête « metaresearch » (Gemma)0,024
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict), Méta-épidémiologie (sens large)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,254
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0070,024
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0150,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
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 Opus0,097
Tête enseignante GPT0,409
Écart entre enseignants0,312 · 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'étudeRevue systématique
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

Citations128
Publié2020
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueCochrane Database of Systematic ReviewsMême sujetTuberculosis Research and EpidemiologyTravaux en français237 207