Diagnostic accuracy of Aspergillus-specific antibodies for chronic pulmonary aspergillosis: A systematic review and meta-analysis

We performed this study to provide the latest evidence of the diagnostic accuracy of all Aspergillus antibodies for chronic pulmonary aspergillosis (CPA). In this meta-analysis, we searched the Cochrane Central Register of Controlled Trials, MEDLINE, Embase, and other databases, until 19 March 2020, for studies that examined the diagnostic accuracy of each Aspergillus-specific antibody for CPA and assessed the risk of bias using the revised Quality Assessment of Diagnostic Accuracy Studies-2 tool. We integrated the results using a hierarchical summary receiver operating characteristic (HSROC) model and calculated the point estimates of specificity with sensitivity fixed at 0.90 using the HSROC curve. We identified 32 published and one unpublished studies, including 75 studies on five antibody test types: 18 of precipitin test (2810 participants), 46 of IgG (8197), three of IgA (283), six of IgM (733) and two of combined IgG and IgM (IgG + IgM) (920). The results of specificity with sensitivity fixed at 0.90 were as follows: precipitin test, 0.93 (95% credible intervals: 0.86, 1.00); IgG, 0.90 (0.86, 0.95); IgA, 0.74 (0.00, 1.00); IgM, 0.50 (0.37, 0.53); IgG + IgM, 0.47 (0.00, 1.00). However, the precipitin test showed imprecision and instability in the sensitivity analysis. Most studies had a high risk of bias due to the case-control design. Although there is lack of applicability for malignancy or immunosuppressive patients, our study suggests a preference for IgG over other antibody tests in CPA screening. Particularly, IgG should be used as an adjunct when ruling out CPA.

Automated summary

This study provides evidence on the diagnostic accuracy of different Aspergillus antibodies for chronic pulmonary aspergillosis. The results suggest a preference for IgG over other antibody tests in CPA screening, although most studies have a high risk of bias and the precipitin test showed imprecision and instability in sensitivity analysis.