Evidence map›Paper›PMID 30872692›Full record

SynthesisScientific reports2019

Accuracy of Commercial Molecular Diagnostics for the Detection of Pulmonary Tuberculosis in China: A Systematic Review.

Siwei Deng, Yixin Sun, Hui Xia, Zhike Liu, Le Gao, Jichun Yang, Yanlin Zhao, Fei Huang, Jingnan Feng, Lixia Wang and 2 more

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
2.2field-weighted citation impact, top 13% 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, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Review
  5. Review
  6. Novel automated AIMLAM for diagnosis ofFuture microbiology · 2024
    Article
  7. Review
  8. Rapid detection ofJournal of clinical microbiology · 2024
    Article
  9. Article
  10. Article
  11. Review
  12. Review
  13. Advances in Molecular Diagnosis of Tuberculosis.Journal of clinical microbiology · 2020
    Review
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

12 authors at 2 institutions in 1 country.

Siwei DengDepartment of Epidemiology and Biostatistics, Peking University School of Public Health, Beijing, China.
Yixin SunDepartment of Epidemiology and Biostatistics, Peking University School of Public Health, Beijing, China.ORCID 0000-0002-4879-0529
Hui XiaNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Zhike LiuDepartment of Epidemiology and Biostatistics, Peking University School of Public Health, Beijing, China.
Le GaoDepartment of Epidemiology and Biostatistics, Peking University School of Public Health, Beijing, China.
Jichun YangDepartment of Epidemiology and Biostatistics, Peking University School of Public Health, Beijing, China.
Yanlin ZhaoNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.ORCID 0000-0002-7890-4312
Fei HuangNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Jingnan FengDepartment of Epidemiology and Biostatistics, Peking University School of Public Health, Beijing, China.
Lixia WangNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Shitong HuanBill and Melinda Gates Foundation, Beijing, China.
Siyan ZhanDepartment of Epidemiology and Biostatistics, Peking University School of Public Health, Beijing, China. siyan-zhan@bjmu.edu.cn.ORCID 0000-0001-7252-5349
Peking University · CNChinese Center For Disease Control and Prevention · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This systematic review assesses the accuracy of molecular diagnostic methods for the detection of pulmonary tuberculosis in studies performed in China, published in Chinese and English. We searched for studies that assessed the accuracy of molecular diagnostics for pulmonary TB in China in the China National Knowledge Infrastructure, the Wanfang Database, SinoMed, VIP Information, Pubmed, Embase, and the Cochrane Library. For each index test, a summary estimation for sensitivity and specificity was calculated using the bivariate random-effects model. A total of 59 studies were included in our analysis. Loop-mediated isothermal amplifcation (LAMP) assay (six studies; pooled sensitivity 90%, 95% CI 78-95%; specificity 93%, 85-97%), line probe assay (LPA) (one study; 87%, 84-90%; 94%, 92-95%) and polymerase chain reaction (PCR) (FQ-PCR and RT-PCR) (four studies; 90%, 55-99%; 93%, 71-99%) showed good diagnostic performance in the meta-analysis. The highest pooled sensitivity was from Xpert MTB/RIF (20 studies; pooled sensitivity 91%, 95% CI 87-94%). The highest pooled specificity was from cross-priming amplification (CPA) (six studies; pooled specificity 97%, 95-99%). The lowest pooled sensitivity and specificity were from simultaneous amplification and testing (SAT)-TB (three studies; 79%, 66-88%; 72%, 48-88%). In subgroup analysis, molecular diagnostics demonstrated higher sensitivity for pulmonary TB detection in smear-positive specimens. Xpert MTB/RIF, LAMP, LPA, CPA and PCR demonstrated high accuracy overall for pulmonary tuberculosis detection, while SAT-TB had poor performance.

Indexed as

ChinaHumansMolecular Diagnostic TechniquesMycobacterium tuberculosisPathology, MolecularPredictive Value of TestsTuberculosis, Pulmonary

Identifiers

PMID30872692
PMCPMC6418248
OpenAlexW2922241006

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.