Evidence map›Paper›PMID 33755189›Full record

SynthesisThe Cochrane database of systematic reviews2021

Xpert MTB/RIF and Xpert Ultra assays for screening for pulmonary tuberculosis and rifampicin resistance in adults, irrespective of signs or symptoms.

Adrienne E Shapiro, Jennifer M Ross, Mandy Yao, Ian Schiller, Mikashmi Kohli, Nandini Dendukuri, Karen R Steingart, David J Horne

Open access · hybridAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in The Cochrane database of systematic reviews, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 14 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
52citing papers in PubMed, 14 pooled it
2.5field-weighted citation impact, top 9% 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

52 citing papers in PubMed, 14 syntheses or guidelines pooled it, 58 citations in OpenAlex.

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  11. Screening tests for active pulmonary tuberculosis in children.The Cochrane database of systematic reviews · 2021
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  17. Cost-effectiveness of community tuberculosis screening in South Africa.American journal of respiratory and critical care medicine · 2026
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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

8 authors at 4 institutions in 3 countries.

Adrienne E ShapiroDivision of Allergy & Infectious Diseases, Global Health & Medicine, University of Washington, Seattle, USA.
Jennifer M RossDivision of Allergy & Infectious Diseases, Global Health & Medicine, University of Washington, Seattle, USA.
Mandy YaoCentre for Outcomes Research, McGill University Health Centre - Research Institute, Montreal, Canada.
Ian SchillerCentre for Outcomes Research, McGill University Health Centre - Research Institute, Montreal, Canada.
Mikashmi KohliDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada.
Nandini DendukuriCentre for Outcomes Research, McGill University Health Centre - Research Institute, Montreal, Canada.
Karen R SteingartHonorary Research Fellow, Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK.
David J HorneDepartment of Medicine, Division of Pulmonary and Critical Care Medicine, and Firland Northwest TB Center, University of Washington, Seattle, WA, USA.
McGill University Health Centre · CAUniversity of Washington · USLiverpool School of Tropical Medicine · GBMcGill University · CA

Funding

Novel clinic-based TB diagnostics and testing algorithms for persons with HIVK23AI140918 · NIAID · UNIVERSITY OF WASHINGTON · PI SHAPIRO, ADRIENNE E · 2019 to 2024
$937k
NIAID NIH HHS K23 AI140918
6 · The paper itself

Abstract

backgroundTuberculosis is a leading cause of infectious disease-related death and is one of the top 10 causes of death worldwide. The World Health Organization (WHO) recommends the use of specific rapid molecular tests, including Xpert MTB/RIF or Xpert Ultra, as initial diagnostic tests for the detection of tuberculosis and rifampicin resistance in people with signs and symptoms of tuberculosis. However, the WHO estimates that nearly one-third of all active tuberculosis cases go undiagnosed and unreported. We were interested in whether a single test, Xpert MTB/RIF or Xpert Ultra, could be useful as a screening test to close this diagnostic gap and improve tuberculosis case detection.

objectivesTo estimate the accuracy of Xpert MTB/RIF and Xpert Ultra for screening for pulmonary tuberculosis in adults, irrespective of signs or symptoms of pulmonary tuberculosis in high-risk groups and in the general population. Screening "irrespective of signs or symptoms" refers to screening of people who have not been assessed for the presence of tuberculosis symptoms (e.g. cough). To estimate the accuracy of Xpert MTB/RIF and Xpert Ultra for detecting rifampicin resistance in adults screened for tuberculosis, irrespective of signs and symptoms of pulmonary tuberculosis in high-risk groups and in the general population. SEARCH

methodsWe searched 12 databases including the Cochrane Infectious Diseases Group Specialized Register, MEDLINE and Embase, on 19 March 2020 without language restrictions. We also reviewed reference lists of included articles and related Cochrane Reviews, and contacted researchers in the field to identify additional studies. SELECTION CRITERIA: Cross-sectional and cohort studies in which adults (15 years and older) in high-risk groups (e.g. people living with HIV, household contacts of people with tuberculosis) or in the general population were screened for pulmonary tuberculosis using Xpert MTB/RIF or Xpert Ultra. For tuberculosis detection, the reference standard was culture. For rifampicin resistance detection, the reference standards were culture-based drug susceptibility testing and line probe assays. DATA COLLECTION AND ANALYSIS: Two review authors independently extracted data using a standardized form and assessed risk of bias and applicability using QUADAS-2. We used a bivariate random-effects model to estimate pooled sensitivity and specificity with 95% credible intervals (CrIs) separately for tuberculosis detection and rifampicin resistance detection. We estimated all models using a Bayesian approach. For tuberculosis detection, we first estimated screening accuracy in distinct high-risk groups, including people living with HIV, household contacts, people residing in prisons, and miners, and then in several high-risk groups combined. MAIN

resultsWe included a total of 21 studies: 18 studies (13,114 participants) evaluated Xpert MTB/RIF as a screening test for pulmonary tuberculosis and one study (571 participants) evaluated both Xpert MTB/RIF and Xpert Ultra. Three studies (159 participants) evaluated Xpert MTB/RIF for rifampicin resistance. Fifteen studies (75%) were conducted in high tuberculosis burden and 16 (80%) in high TB/HIV-burden countries. We judged most studies to have low risk of bias in all four QUADAS-2 domains and low concern for applicability. Xpert MTB/RIF and Xpert Ultra as screening tests for pulmonary tuberculosis In people living with HIV (12 studies), Xpert MTB/RIF pooled sensitivity and specificity (95% CrI) were 61.8% (53.6 to 69.9) (602 participants; moderate-certainty evidence) and 98.8% (98.0 to 99.4) (4173 participants; high-certainty evidence). Of 1000 people where 50 have tuberculosis on culture, 40 would be Xpert MTB/RIF-positive; of these, 9 (22%) would not have tuberculosis (false-positives); and 960 would be Xpert MTB/RIF-negative; of these, 19 (2%) would have tuberculosis (false-negatives). In people living with HIV (1 study), Xpert Ultra sensitivity and specificity (95% CI) were 69% (57 to 80) (68 participants; very low-certainty evidence) and 98% (97 to 99) (503 participants; moderate-certainty evidence). Of 1000 people where 50 have tuberculosis on culture, 53 would be Xpert Ultra-positive; of these, 19 (36%) would not have tuberculosis (false-positives); and 947 would be Xpert Ultra-negative; of these, 16 (2%) would have tuberculosis (false-negatives). In non-hospitalized people in high-risk groups (5 studies), Xpert MTB/RIF pooled sensitivity and specificity were 69.4% (47.7 to 86.2) (337 participants, low-certainty evidence) and 98.8% (97.2 to 99.5) (8619 participants, moderate-certainty evidence). Of 1000 people where 10 have tuberculosis on culture, 19 would be Xpert MTB/RIF-positive; of these, 12 (63%) would not have tuberculosis (false-positives); and 981 would be Xpert MTB/RIF-negative; of these, 3 (0%) would have tuberculosis (false-negatives). We did not identify any studies using Xpert MTB/RIF or Xpert Ultra for screening in the general population. Xpert MTB/RIF as a screening test for rifampicin resistance Xpert MTB/RIF sensitivity was 81% and 100% (2 studies, 20 participants; very low-certainty evidence), and specificity was 94% to 100%, (3 studies, 139 participants; moderate-certainty evidence). AUTHORS'

conclusionsOf the high-risks groups evaluated, Xpert MTB/RIF applied as a screening test was accurate for tuberculosis in high tuberculosis burden settings. Sensitivity and specificity were similar in people living with HIV and non-hospitalized people in high-risk groups. In people living with HIV, Xpert Ultra sensitivity was slightly higher than that of Xpert MTB/RIF and specificity similar. As there was only one study of Xpert Ultra in this analysis, results should be interpreted with caution. There were no studies that evaluated the tests in people with diabetes mellitus and other groups considered at high-risk for tuberculosis, or in the general population.

Indexed as

Drug Resistance, BacterialAdultAntibiotics, AntitubercularBacteriological TechniquesBayes TheoremBiasCohort StudiesCross-Sectional StudiesFalse Negative ReactionsFalse Positive ReactionsHIV InfectionsHumansMycobacterium tuberculosisPolymerase Chain ReactionRifampinSensitivity and SpecificityAntibiotics, AntitubercularRifampin

Identifiers

PMID33755189
PMCPMC8437892
OpenAlexW3139016575

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.