Evidence map›Paper›PMID 28042217›Full record

ArticleJournal of laboratory physicians

Performance of Xpert MTB/RIF on Ascitic Fluid Samples for Detection of Abdominal Tuberculosis.

Syed Beenish Rufai, Sarman Singh, Amit Singh, Parveen Kumar, Jitendra Singh, Anand Vishal

Open access · goldAbstract read
In one paragraph

Article in Journal of laboratory physicians. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 4 pooled it
5.0field-weighted citation impact, top 5% 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

20 citing papers in PubMed, 4 syntheses or guidelines pooled it, 58 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. XpertThe Cochrane database of systematic reviews · 2018
    Pooled it
  5. Trial
  6. Article
  7. Diagnostic yield of Xpert MTB/RIF, Xpert MTB/RIF Ultra, and Truenat MTB assays on non-pulmonary samples from suspected cases of extra-pulmonary tuberculosis (EPTB).European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2025
    Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Abdominal Tuberculosis Mimicking Ovarian Cancer: A Diagnostic Dilemma.Journal of obstetrics and gynaecology of India · 2020
    Article
  14. Article
  15. Article
  16. Article
  17. A Peculiar Peritoneum: A Case of Tuberculosis in a Male Without Known Risk Factors.Journal of investigative medicine high impact case reports
    Article
  18. Tuberculosis Diagnosis and Management: Recent Advances.Journal of global infectious diseases
    Review
  19. Tuberculosis in Cirrhosis - A Diagnostic and Management Conundrum.Journal of clinical and experimental hepatology
    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

6 authors at 2 institutions in 1 country.

Syed Beenish RufaiDivision of Clinical Microbiology and Molecular Medicine, Department of Laboratory Medicine, All India Institute of Medical Sciences, New Delhi, India.
Sarman SinghDivision of Clinical Microbiology and Molecular Medicine, Department of Laboratory Medicine, All India Institute of Medical Sciences, New Delhi, India.
Amit SinghDivision of Clinical Microbiology and Molecular Medicine, Department of Laboratory Medicine, All India Institute of Medical Sciences, New Delhi, India.
Parveen KumarDivision of Clinical Microbiology and Molecular Medicine, Department of Laboratory Medicine, All India Institute of Medical Sciences, New Delhi, India.
Jitendra SinghDivision of Clinical Microbiology and Molecular Medicine, Department of Laboratory Medicine, All India Institute of Medical Sciences, New Delhi, India.
Anand VishalDepartment of Medicine, Dr. Ram Manohar Lohia Hospital, PGIMER, New Delhi, India.
All India Institute of Medical Sciences · INAtal Bihari Vajpayee Institute of Medical Sciences and Dr. Ram Manohar Lohia Hospital · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiagnosis of abdominal tuberculosis (TB) from ascitic fluid samples using routinely available diagnostic methods is challenging due to its paucibacillary nature. Although performance of Xpert MTB/RIF assay has been evaluated extensively on pulmonary samples, its performance on extrapulmonary samples is still under evaluation.

objectivesThe objective of this study was to find out the performance of Xpert MTB/RIF on ascitic fluid samples obtained from suspected cases of abdominal TB. Performance was compared with MATERIALS AND

methodsSixty-seven patients suspected of probable/possible abdominal TB were included in this observational, prospective study. All samples were tested by Ziehl-Neelsen staining, MGIT-960 culture, in-house multiplex PCR, and Xpert MTB/RIF assay.

resultsAll 67 samples were smear negative. Seventeen (25.4%) were MGIT-960 culture positive while 12 (17.9%) were detected positive by the Xpert MTB/RIF assay and 9 (13.4%) by in-house multiplex PCR. Sensitivity and specificity of the Xpert MTB/RIF assay compared with the MGIT-960 culture were 70.6% (95%, confidence interval [CI]: 44.1-89.7) and 100% (95%, CI: 92.8-100) and that of in-house multiplex PCR were 52.9% (95%, CI: 30.9-73.8) and 100% (95%, CI: 92.8-100), respectively.

conclusionsDiagnostic yield of Xpert MTB/RIF assay on ascitic fluid samples was lower than MGIT-960 culture. We thus emphasize on the need for urgent discovery of new biomarkers for paucibacillary TB.

Indexed as

Abdominal tuberculosisascitic fluidmultiplex polymerase chain reactionMycobacterium growth indicator tube-960 cultureXpert MTB/RIF

Identifiers

PMID28042217
PMCPMC5015498
OpenAlexW2513475519

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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.