Evidence map›Paper›PMID 31720409›Full record

ArticleJournal of clinical tuberculosis and other mycobacterial diseases2019

The performance and yield of tuberculosis testing algorithms using microscopy, chest x-ray, and Xpert MTB/RIF.

Jacob Creswell, Zhi Zhen Qin, Rajendra Gurung, Bikash Lamichhane, Deepak Kumar Yadav, Mohan Kumar Prasai, Nirmala Bista, Lal Mani Adhikari, Bishwa Rai, Santat Sudrungrot

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical tuberculosis and other mycobacterial diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
1.3field-weighted citation impact, top 20% 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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 20 citations in OpenAlex.

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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

10 authors at 1 institution in 1 country.

Jacob CreswellStop TB Partnership, Chemin du Pommier 40, 1218 Le Grand-Saconnex, Geneva, Switzerland.
Zhi Zhen QinStop TB Partnership, Chemin du Pommier 40, 1218 Le Grand-Saconnex, Geneva, Switzerland.
Rajendra GurungB.P. Koirala Institute of Health Sciences, Sunsari, Nepal.
Bikash LamichhaneTB Control Programme, Kathmandu, Nepal.
Deepak Kumar YadavB.P. Koirala Institute of Health Sciences, Sunsari, Nepal.
Mohan Kumar PrasaiTB Control Programme, Kathmandu, Nepal.
Nirmala BistaInternational Organization for Migration, Migration Health Department, Kathmandu, Nepal.
Lal Mani AdhikariInternational Organization for Migration, Migration Health Department, Kathmandu, Nepal.
Bishwa RaiInternational Organization for Migration, Migration Health Department, Kathmandu, Nepal.
Santat SudrungrotInternational Organization for Migration, Migration Health Department, Kathmandu, Nepal.
B.P. Koirala Institute of Health Sciences · NP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

settingThe introduction of Xpert MTB/RIF (Xpert) and renewed interest in chest x-ray (CXR) for tuberculosis testing has provided additional choices to the smear-based diagnostic algorithms used by TB programs previously. More programmatic data is needed to better understand the implications of possible approaches.

objectiveWe sought to evaluate how different testing algorithms using microscopy, Xpert and CXR impacted the number of people detected with TB in a district hospital in Nepal.

designConsecutively recruited patients with TB-related symptoms were offered smear microscopy, CXR and Xpert. We tested six hypothetical algorithms and compared yield, bacteriologically positive (Bac+) cases missed, and tests conducted.

resultsAmong 929 patients, Bac+ prevalence was 17.3% (

conclusionXpert detected a large proportion of Bac+ TB cases missed by microscopy. CXR was useful in greatly reducing the number of diagnostic tests needed even among presumptive TB patients. Loose CXR readings should be used to identify more people for TB testing. More analysis of costs and standardized CXR reading should be considered.

Indexed as

Case detectionDiagnostic algorithms Chest X-rayTuberculosisXpert MTB/RIF

Identifiers

PMID31720409
PMCPMC6830149
OpenAlexW2903141955

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.