Background and Aims. Active hepatitis C virus (HCV) infection is based on the detection of HCV RNA that it is effective but presents high cost and the need to hire trained personnel. This systematic review and meta-analysis is aimed at evaluating the diagnostic accuracy of HCV Ag testing to identify HCV cases and to monitor antiviral treatment including DAA treatment. Methods. The studies were identified through a search in PubMed, Lilacs, and Scopus from 1990 through March 31, 2020. Cohort, cross-sectional, and randomized controlled trials were included. Two independent reviewers extracted data and assessed quality using an adapted Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Our primary outcome was to determine the accuracy of HCV Ag detection for the diagnosis, which we estimated using random-effects meta-analysis. Results. Of 3,062 articles identified, 54 met our eligibility criteria. The studies described cohorts from 20 countries, including 14,286 individuals with chronic HCV individuals. Studies for ECLIA technology demonstrated highest quality compared to studies that used ELISA. The pooled sensitivity and specificity (95% CI) for HCV Ag detection of active HCV infection were 98.82% ( 95 % CI = 98.04 %; 99.30%) and 98.95% ( 95 % CI = 97.84 %; 99.49%), respectively. High concordance was found between HCV Ag testing and HCV RNA detection 89.7% and 95% to evaluate antiviral treatment. Conclusions. According to our findings, HCV Ag testing could be useful to identify HCV active cases in low-resource areas. For antiviral treatment, HCV Ag testing will be useful at the end of treatment.

No abstract