Evidence map›Paper›PMID 31752713›Full record

ArticleBMC infectious diseases2019

Clinical effect of T-SPOT.TB test for the diagnosis of tuberculosis.

Yanfen Ma, Ruicheng Li, Jinghui Shen, Longmei He, Ying Li, Ning Zhang, Qian Wu, Jinling Zhang, Jie Zheng, Xiaoqin Wang

Open access · goldAbstract read
In one paragraph

Article in BMC infectious diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
1.9field-weighted citation impact, top 14% 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

19 citing papers in PubMed, 30 citations in OpenAlex.

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  19. Primary adrenal insufficiency due to adrenal tuberculosis: a case report.The Journal of international medical research · 2020
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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 4 institutions in 1 country.

Yanfen MaDepartment of Clinical Laboratory, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, China.
Ruicheng LiDepartment of Clinical Laboratory, The Second Affiliated Hospital of Air Force Medical University, Xi'an, 710038, Shaanxi Province, China.
Jinghui ShenDepartment of Clinical Laboratory, Xi'an Central Hospital, Xi'an, 710003, Shaanxi Province, China.
Longmei HeDepartment of Clinical Laboratory, Shaanxi Provincial Hospital of Traditional Chinese Medicine, Xi'an, 710061, Shaanxi Province, China.
Ying LiDepartment of Clinical Laboratory, Shaanxi KangFu Hospital, Xi'an, 710061, Shaanxi Province, China.
Ning ZhangDepartment of Clinical Laboratory, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, China.
Qian WuDepartment of Clinical Laboratory, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, China.
Jinling ZhangDepartment of Clinical Laboratory, Xi'an Encephalopathy Hospital, Xi'an, 710061, Shaanxi Province, China.
Jie ZhengClinical Research Center, The The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, China. jiezheng@xjtu.edu.cn.
Xiaoqin WangDepartment of Clinical Laboratory, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, China. pusu294jiaqiao@163.com.
First Affiliated Hospital of Xi'an Jiaotong University · CNAir Force Medical University · CNFirst Hospital of Xi'an · CNShaanxi Provincial Hospital of Traditional Chinese Medicine · CN

Funding

International Science and Technology Cooperation and Exchange Program of Shaanxi Province 2015KW-045
6 · The paper itself

Abstract

backgroundThe goal of this study was to further investigate the clinical effectiveness of the T-SPOT.TB test in diagnosing tuberculosis (TB), including the effects of T-SPOT.TB test on evaluating diverse TB types and locations.

methodsWe collected 20,332 specimens from patients suspected to have TB. Afterwards, we performed an integrative analysis of T-SPOT.TB results and clinical diagnoses, and evaluated the composition ratio and positive detection rate of the T-SPOT.TB test in various age groups, sample types, and hospital departments. In addition, we compared the spot number and composition rate between latent TB infection (LTBI), active TB infection, and old TB infection groups. The active TB group was then further divided into pulmonary TB (PTB), pulmonary and extrapulmonary TB (PETB), and extrapulmonary TB (EPTB) subgroups, and we evaluated whether there were statistical differences in spot number and composition rate between subgroups.

resultsPositive results from the T-SPOT.TB test were found across different age groups, specimen types, and hospital departments. Elderly patient groups, pleural effusion samples, and thoracic surgery departments showed the highest rates of positivity. There were no statistically significant differences in spot number of CFP-10 and ESAT-6 wells between disease groups or active TB subgroups. The composition rate, however, was significantly different when ESAT-6 and CFP-10 wells were double-positive. The spot number and composition rate were statistically different between the three disease groups, but showed no significant differences between the three subgroups of active TB.

conclusionsThe results of T-SPOT. TB test showed differences in LTBI, active TB and old TB. Additionally, a higher spot number level was observed in the active TB group.

Indexed as

AdolescentAdultAgedAged, 80 and overChildChild, PreschoolDiagnostic Tests, RoutineFemaleHumansInfantInterferon-gammaMaleMiddle AgedMycobacterium tuberculosisTreatment OutcomeTuberculosis, PulmonaryInterferon-gammaActive tuberculosisClinical diagnosisMycobacterium tuberculosisT cell spot test of tuberculosis infection

Identifiers

PMID31752713
PMCPMC6873440
OpenAlexW2989779218

What OpenQuestion holds

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LicenceCC BY
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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.