Evidence map›Paper›PMID 26670929›Full record

ArticlePloS one2015

Implementing the Xpert® MTB/RIF Diagnostic Test for Tuberculosis and Rifampicin Resistance: Outcomes and Lessons Learned in 18 Countries.

Elisa Ardizzoni, Emmanuel Fajardo, Peter Saranchuk, Martina Casenghi, Anne-Laure Page, Francis Varaine, Cara S Kosack, Pamela Hepple

Abstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 4 pooled it
–field-weighted citation impact
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

39 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Closing gaps through innovation in the tuberculosis pathology value chain.Journal of clinical tuberculosis and other mycobacterial diseases · 2026
    Review
  6. Article
  7. Rifampicin-resistantJournal of clinical tuberculosis and other mycobacterial diseases · 2025
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Universal Access to Xpert MTB/RIF Testing for Diagnosis of Tuberculosis in Uzbekistan: How Well Are We Doing?International journal of environmental research and public health · 2021
    Article
  17. Review
  18. Article
  19. Article
  20. Article
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.

Elisa ArdizzoniMédecins Sans Frontières, Institute of Tropical Medicine, Antwerp, Belgium.
Emmanuel FajardoMédecins Sans Frontières, Southern Africa Medical Unit, Cape Town, South Africa.
Peter SaranchukMédecins Sans Frontières, Southern Africa Medical Unit, Cape Town, South Africa.
Martina CasenghiMédecins Sans Frontières, Geneva, Switzerland.
Anne-Laure PageEpicentre, Médecins Sans Frontières, Paris, France.
Francis VaraineMédecins Sans Frontières, Paris, France.
Cara S KosackMédecins Sans Frontières, Amsterdam, Netherlands.
Pamela HeppleMédecins Sans Frontières, Manson Unit, London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe Xpert® MTB/RIF (Xpert) is an automated molecular test for simultaneous detection of tuberculosis (TB) and rifampicin resistance, recommended by the World Health Organization as the preferred diagnostic method for individuals presumed to have multi-drug resistant TB (MDR-TB) or HIV-associated TB. We describe the performance of Xpert and key lessons learned during two years of implementation under routine conditions in 33 projects located in 18 countries supported by Médecins Sans Frontières across varied geographic, epidemiological and clinical settings.

methodsXpert was used following three strategies: the first being as the initial test, with microscopy in parallel, for all presumptive TB cases; the second being only for patients at risk of MDR-TB, or with HIV- associated TB, or presumptive paediatric TB; and the third being as the initial test for these high-risk patients plus as an add-on test to microscopy in others. Routine laboratory data were collected, using laboratory registers. Qualitative data such as logistic aspects, human resources, and tool acceptance were collected using a questionnaire.

findingsIn total, 52,863 samples underwent Xpert testing from April 2011 to December 2012. The average MTB detection rate was 18.5%, 22.3%, and 11.6% for the three different strategies respectively. Analysis of the results on samples tested in parallel showed that using Xpert as add-on test to microscopy would have increased laboratory TB confirmation by 49.7%, versus 42.3% for Xpert replacing microscopy. The main limitation of the test was the high rate of inconclusive results, which correlated with factors such as defective modules, cartridge version (G3 vs. G4) and staff experience. Operational and logistical hurdles included infrastructure renovation, basic computer training, regular instrument troubleshooting and maintenance, all of which required substantial and continuous support.

conclusionThe implementation of Xpert was feasible and significantly increased TB detection compared to microscopy, despite the high rate of inconclusive results. Xpert implementation was accompanied by considerable operational and logistical challenges. To further decentralize diagnosis, simpler, low-cost TB technologies well-suited to low-resource settings are still urgently needed.

Indexed as

Diagnostic Tests, RoutineDrug Resistance, BacterialReagent Kits, DiagnosticAdultChildHumansRifampinRisk FactorsSputumSurveys and QuestionnairesTreatment OutcomeTuberculosisReagent Kits, DiagnosticRifampin

Identifiers

PMID26670929
PMCPMC4682866

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.