Evidence map›Paper›PMID 29216851›Full record

ArticleBMC ophthalmology2017

What does IGRA testing add to the diagnosis of ocular tuberculosis? A Bayesian latent class analysis.

Rupesh Agrawal, Robert Grant, Bhaskar Gupta, Dinesh Visva Gunasekeran, Julio J Gonzalez-Lopez, Peter K F Addison, Mark Westcott, Carlos E Pavesio

Abstract read
In one paragraph

Article in BMC ophthalmology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–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

11 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Applications of artificial intelligence and bioinformatics methodologies in the analysis of ocular biofluid markers: a scoping review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024
    Article
  6. Article
  7. Review
  8. Review
  9. Ocular tuberculosis: Where are we today?Indian journal of ophthalmology · 2020
    Review
  10. Article
  11. 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.

Rupesh AgrawalMoorfields Eye Hospital, NHS Foundation Trust, London, UK. rupeshttsh@gmail.com.ORCID http://orcid.org/0000-0002-6662-5850
Robert GrantSt George's, University of London & Kingston University, London, UK.
Bhaskar GuptaRoyal Berkshire Hospital NHS Foundation Trust, Reading, UK.
Dinesh Visva GunasekeranNational Healthcare Group Eye Institute, Tan Tock Seng Hospital, Tan Tock Seng, Singapore.
Julio J Gonzalez-LopezMoorfields Eye Hospital, NHS Foundation Trust, London, UK.
Peter K F AddisonMoorfields Eye Hospital, NHS Foundation Trust, London, UK.
Mark WestcottMoorfields Eye Hospital, NHS Foundation Trust, London, UK.
Carlos E PavesioMoorfields Eye Hospital, NHS Foundation Trust, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo evaluate the contribution made to the diagnostic work-up for patients with suspected ocular tuberculosis (TB) by QuantiFERON-TB Gold In-Tube (QFT) tests using latent class analysis model.

methodsA single centre retrospective cohort study. A Bayesian latent class model was constructed on the basis of demographics, phenotypes and test results from patients attending a tertiary referral center in the UK. This estimated the probability of ocular TB for each patient in two versions, first with and then without QFT. The estimated probability of ocular TB was compared with treatment failure.

resultsFrom a database of 365 patients with clinical signs suggestive of ocular TB, 267 patients who had QFT and complete data were evaluated. Mean age was 45.0 ± 15.4 years with 141 (52.9%) male and 148 (50.5%) of Asian ethnicity. QFT was positive in 208 (70.1%) patients and ATT was instituted in 145 (49.5%) patients with 100 (34.1%) patients also having concurrent systemic corticosteroid therapy. The best estimate of a QFT level separating TB-positive and TB-negative patients was extremely low. This weak discrimination between TB and non-TB groups was reflected in poor positive and negative predictive values for treatment failure.

conclusionsThe latent class model did not successfully predict treatment failure, despite taking all variables into account. The threshold between TB and non-TB in QFT values was implausibly low and removing QFT from the model made prediction slightly worse. A larger prospective study is required to establish the role of all tests, demographics and phenotypes in diagnosis.

Indexed as

AdultBayes TheoremDiagnostic Techniques, OphthalmologicalFemaleHumansInterferon-gammaMaleMiddle AgedPredictive Value of TestsRetrospective StudiesTuberculin TestTuberculosis, OcularInterferon-gammaATTBayesian latent class analysisExtrapulmonary latent TBPositive QFTPresumed ocular tuberculosisUveitis

Identifiers

PMID29216851
PMCPMC5721607

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.