Is interleukin-2 an optimal marker for diagnosing tuberculosis infection? A systematic review and meta-analysis

Latent tuberculosis infection (LTBI) is a huge reservoir for the deadlier TB disease. Accurate identification of LTBI is a key strategy to eliminate TB. Therefore, a systematic review and meta-analysis approach was used to assess diagnostic potential of IL-2 for LTBI. PubMed, Web of Science, the Cochrane Library and Embase were searched. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), area under the summary receiver operating characteristic curve (AUROC) and hierarchical summary receiver operating characteristic curve (HSROC) were estimated by bivariate and HSROC models. Twenty-seven studies including 1404 participants and 1986 samples met the inclusion criteria. The pooled sensitivity, specificity, PLR, NLR, DOR and AUROC of IL-2 were separately as 87%, 98%, 34.78, 0.14, 256.41 and 0.98, indicating a very powerful differentiating ability of IL-2 for LTBI from non-TB controls. For differentiating ATB from LTBI, the pooled sensitivity, specificity, PLR, NLR, DOR and AUROC of IL-2 were 83%, 76%, 3.41, 0.22, 15.47 and 0.87, respectively, suggesting a good differentiating ability of IL-2. These findings showed that IL-2 is a powerful marker for differentiating LTBI from non-TB controls and a good marker for differentiating ATB from LTBI individuals.

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PID https://www.doi.org/10.6084/m9.figshare.12849926.v1
PID https://www.doi.org/10.6084/m9.figshare.12849926
PID https://www.doi.org/10.1080/07853890.2020.1800073
URL http://dx.doi.org/10.1080/07853890.2020.1800073
URL http://dx.doi.org/10.6084/m9.figshare.12849926.v1
URL https://academic.microsoft.com/#/detail/3044776464
URL http://dx.doi.org/10.6084/m9.figshare.12849926
URL https://www.tandfonline.com/doi/full/10.1080/07853890.2020.1800073
URL https://www.tandfonline.com/doi/pdf/10.1080/07853890.2020.1800073
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Author Xia Qiu
Author Huiqing Wang
Author Ying Tang
Author Xiaojuan Su
Author Long Ge
Author Yi Qu
Author Dezhi Mu, 0000-0002-9148-069X
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Collected From Datacite; figshare; Crossref; Microsoft Academic Graph
Hosted By figshare; Annals of Medicine
Publication Date 2020-01-01
Publisher Taylor & Francis
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Language UNKNOWN
Resource Type Other literature type; Article
keyword FOS: Clinical medicine
system:type publication
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Source https://science-innovation-policy.openaire.eu/search/publication?articleId=dedup_wf_001::98b7184e75a9b4b87b7348b576e583a9
Author jsonws_user
Last Updated 26 December 2020, 03:08 (CET)
Created 26 December 2020, 03:08 (CET)