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Xpert MTB/RIF and Xpert MTB/RIF Ultra for pulmonary tuberculosis and rifampicin resistance in adults

2019· review· en· W2948492785 on OpenAlexaff
David Horné, Mikashmi Kohli, Jerry S. Zifodya, Ian Schiller, Nandini Dendukuri, Deanna Tollefson, Samuel G. Schumacher, Eleanor Ochodo, Madhukar Pai, Karen R Steingart

Bibliographic record

VenueCochrane Database of Systematic Reviews · 2019
Typereview
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill University Health CentreMcGill University
FundersNational Center for Advancing Translational SciencesWellcome Trust
KeywordsMedicineTuberculosisRifampicinMycobacterium tuberculosisInternal medicineDrug resistanceMulti-drug-resistant tuberculosisTuberculosis diagnosisMEDLINEPathology

Abstract

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BACKGROUND: Xpert MTB/RIF (Xpert MTB/RIF) and Xpert MTB/RIF Ultra (Xpert Ultra), the newest version, are the only World Health Organization (WHO)-recommended rapid tests that simultaneously detect tuberculosis and rifampicin resistance in persons with signs and symptoms of tuberculosis, at lower health system levels. A previous Cochrane Review found Xpert MTB/RIF sensitive and specific for tuberculosis (Steingart 2014). Since the previous review, new studies have been published. We performed a review update for an upcoming WHO policy review. OBJECTIVES: To determine diagnostic accuracy of Xpert MTB/RIF and Xpert Ultra for tuberculosis in adults with presumptive pulmonary tuberculosis (PTB) and for rifampicin resistance in adults with presumptive rifampicin-resistant tuberculosis. SEARCH METHODS: We searched the Cochrane Infectious Diseases Group Specialized Register, MEDLINE, Embase, Science Citation Index, Web of Science, Latin American Caribbean Health Sciences Literature, Scopus, the WHO International Clinical Trials Registry Platform, the International Standard Randomized Controlled Trial Number Registry, and ProQuest, to 11 October 2018, without language restriction. SELECTION CRITERIA: Randomized trials, cross-sectional, and cohort studies using respiratory specimens that evaluated Xpert MTB/RIF, Xpert Ultra, or both against the reference standard, culture for tuberculosis and culture-based drug susceptibility testing or MTBDRplus for rifampicin resistance. DATA COLLECTION AND ANALYSIS: Four review authors independently extracted data using a standardized form. When possible, we also extracted data by smear and HIV status. We assessed study quality using QUADAS-2 and performed meta-analyses to estimate pooled sensitivity and specificity separately for tuberculosis and rifampicin resistance. We investigated potential sources of heterogeneity. Most analyses used a bivariate random-effects model. For tuberculosis detection, we first estimated accuracy using all included studies and then only the subset of studies where participants were unselected, i.e. not selected based on prior microscopy testing. MAIN RESULTS: We identified in total 95 studies (77 new studies since the previous review): 86 studies (42,091 participants) evaluated Xpert MTB/RIF for tuberculosis and 57 studies (8287 participants) for rifampicin resistance. One study compared Xpert MTB/RIF and Xpert Ultra on the same participant specimen.Tuberculosis detectionOf the total 86 studies, 45 took place in high tuberculosis burden and 50 in high TB/HIV burden countries. Most studies had low risk of bias.Xpert MTB/RIF pooled sensitivity and specificity (95% credible Interval (CrI)) were 85% (82% to 88%) and 98% (97% to 98%), (70 studies, 37,237 unselected participants; high-certainty evidence). We found similar accuracy when we included all studies.For a population of 1000 people where 100 have tuberculosis on culture, 103 would be Xpert MTB/RIF-positive and 18 (17%) would not have tuberculosis (false-positives); 897 would be Xpert MTB/RIF-negative and 15 (2%) would have tuberculosis (false-negatives).Xpert Ultra sensitivity (95% confidence interval (CI)) was 88% (85% to 91%) versus Xpert MTB/RIF 83% (79% to 86%); Xpert Ultra specificity was 96% (94% to 97%) versus Xpert MTB/RIF 98% (97% to 99%), (1 study, 1439 participants; moderate-certainty evidence).Xpert MTB/RIF pooled sensitivity was 98% (97% to 98%) in smear-positive and 67% (62% to 72%) in smear-negative, culture-positive participants, (45 studies). Xpert MTB/RIF pooled sensitivity was 88% (83% to 92%) in HIV-negative and 81% (75% to 86%) in HIV-positive participants; specificities were similar 98% (97% to 99%), (14 studies).Rifampicin resistance detectionXpert MTB/RIF pooled sensitivity and specificity (95% Crl) were 96% (94% to 97%) and 98% (98% to 99%), (48 studies, 8020 participants; high-certainty evidence).For a population of 1000 people where 100 have rifampicin-resistant tuberculosis, 114 would be positive for rifampicin-resistant tuberculosis and 18 (16%) would not have rifampicin resistance (false-positives); 886 would be would be negative for rifampicin-resistant tuberculosis and four (0.4%) would have rifampicin resistance (false-negatives).Xpert Ultra sensitivity (95% CI) was 95% (90% to 98%) versus Xpert MTB/RIF 95% (91% to 98%); Xpert Ultra specificity was 98% (97% to 99%) versus Xpert MTB/RIF 98% (96% to 99%), (1 study, 551 participants; moderate-certainty evidence). AUTHORS' CONCLUSIONS: We found Xpert MTB/RIF to be sensitive and specific for diagnosing PTB and rifampicin resistance, consistent with findings reported previously. Xpert MTB/RIF was more sensitive for tuberculosis in smear-positive than smear-negative participants and HIV-negative than HIV-positive participants. Compared with Xpert MTB/RIF, Xpert Ultra had higher sensitivity and lower specificity for tuberculosis and similar sensitivity and specificity for rifampicin resistance (1 study). Xpert MTB/RIF and Xpert Ultra provide accurate results and can allow rapid initiation of treatment for multidrug-resistant tuberculosis.

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)0.006
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.005
Bibliometrics0.0120.010
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.119
GPT teacher head0.414
Teacher spread0.295 · 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 designSystematic review
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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Citations280
Published2019
Admission routes1
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