SynthesisBioMed research international2022
Performance of HCV Antigen Testing for the Diagnosis and Monitoring of Antiviral Treatment: A Systematic Review and Meta-Analysis.
Synthesis in BioMed research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed.
- Amplification of RNA for identification of Zika and HCV in whole blood.Science advances · 2026Article
- The usefulness of a simultaneous immunoassay for hepatitis C virus antigen-antibody using the ELECSYS HCV Duo assay.Hepatology forum · 2026Article
- Article
- Protective role of flavonoids quercetin and silymarin in the viral-associated inflammatory bowel disease: an updated review.Archives of microbiology · 2023Review
- Quality Assurance for Hepatitis C Virus Point-of-Care Diagnostics in Sub-Saharan Africa.Diagnostics (Basel, Switzerland) · 2023Review
- Impact of direct-acting antiviral regimens on hepatic and extrahepatic manifestations of hepatitis C virus infection.World journal of hepatology · 2022Review
- Article
- HCV Core-antigen Assay as an Effective Alternative to HCV RNA Quantification in Patients With Hepatitis C.In vivo (Athens, Greece)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimsActive 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.
methodsThe 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.
resultsOf 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.
conclusionsAccording 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.