Diagnostic test accuracy of anti-glycopeptidolipid-core IgA antibodies for Mycobacterium avium complex pulmonary disease: systematic review and meta-analysis

Citations Over TimeTop 12% of 2016 papers

Abstract

Currently, an anti-glycopeptidolipid (GPL)-core IgA antibody assay kit for diagnosing Mycobacterium avium complex (MAC) is commercially available. We conducted this systematic review and meta-analysis to reveal the precise diagnostic accuracy of anti-GPL-core IgA antibodies for MAC pulmonary disease (MAC-PD). We systematically searched reports that could provide data for both sensitivity and specificity by anti-GPL-core IgA antibody for clinically diagnosed MAC-PD. Diagnostic test accuracy was estimated using the bivariate model. Of the 257 articles that we had found through primary search, we finally included 16 reports consisted of 1098 reference positive subjects and 2270 reference negative subjects. The diagnostic odds ratio was 24.8 (95% CI 11.6-52.8, I(2) = 5.5%) and the area under the hierarchical summary receiver operating characteristic curves was 0.873 (95% CI 0.837-0.913). With a cutoff value of 0.7 U/mL, the summary estimates of sensitivity and specificity were 0.696 (95% CI 0.621-0.761) and 0.906 (95% CI 0.836-0.951), respectively. The positive and negative likelihood ratios were 7.4 (95% CI 4.1-13.8) and 0.34 (95% CI 0.26-0.43), respectively. The demanding clinical diagnostic criteria may be a cause of false positive of the index test. The index test had good overall diagnostic accuracy and was useful to ruling in MAC-PD with the cutoff value.

Related Papers

• → Diagnostic Value of Circulating Chromogranin A for Neuroendocrine Tumors: A Systematic Review and Meta-Analysis(2015)99 cited
• → Clinical utility of microRNA-451 as diagnostic biomarker for human cancers(2018)13 cited
• → Diagnostic accuracy of MOC-31 for malignant effusions: a meta-analysis(2014)12 cited
• Diagnostic accuracy of triggering receptor expressed on myeloid cells-1 for pneumonia by meta-analysis(2012)
• → Value of ultrasonography in predicting difficult airway: a meta-analysis(2018)