Evidence map›Paper›PMID 9854099›Full record

ArticleJournal of clinical microbiology1999

Evaluation of the Abbott LCx Mycobacterium tuberculosis assay for direct detection of Mycobacterium tuberculosis complex in human samples.

M G Garrino, Y Glupczynski, J Degraux, H Nizet, M Delmée

Abstract read
In one paragraph

Article in Journal of clinical microbiology, 1999. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. 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

5 authors.

M G GarrinoMicrobiology Laboratory, University Hospital Mont-Godinne, Catholic University of Louvain, Yvoir, Belgium. garrino@mblg.ucl.ac.be
Y Glupczynski
J Degraux
H Nizet
M Delmée

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Seven hundred thirty-seven clinical samples from 460 patients were processed for direct detection of Mycobacterium tuberculosis complex by a semiautomated ligase chain reaction commercial assay, the LCx Mycobacterium tuberculosis Assay (LCx assay) from Abbott Laboratories. Results were compared to those of direct microscopy and standard microbiological culture. Of 26 patients (5.7%) with a culture positive for M. tuberculosis, 22 (84.6%) were found positive by the LCx assay. The sensitivity of the LCx assay was 98% for smear-positive samples and 27% for smear-negative samples. With an overall culture positivity rate for M. tuberculosis of 8.3% (61 of 737 samples) and after resolution of discrepant results according to clinical data, the sensitivity, specificity, and positive and negative predictive values of the LCx assay were 78, 100, 95, and 98%, respectively, compared to 85, 100, 100, and 98%, respectively, for culture and 67, 99, 87, and 97%, respectively, for acid-fast staining. In conclusion, the LCx assay proved satisfactory and appears to be an easy-to-use 1-day test which must be used with standard culture methods but can considerably reduce diagnosis time versus culture. However, its clinical interest appears to be limited in our population with low mycobacterial prevalence because of its cost considering the small gain in sensitivity versus direct microscopy.

Indexed as

Reagent Kits, DiagnosticBacteriological TechniquesEvaluation Studies as TopicHumansMycobacterium tuberculosisProspective StudiesSensitivity and SpecificityTuberculosisReagent Kits, Diagnostic

Identifiers

PMID9854099
PMCPMC84218

What OpenQuestion holds

Textmetadata
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.