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Evaluation of diagnostic accuracy of eight commercial assays for the detection of rubella virus specific IgM antibodies.

Joanne HiebertVanessa ZubachCarmen L CharltonJayne FentonGraham A TipplesKevin FonsecaAlberto Severini
Published in: Journal of clinical microbiology (2021)
Rubella and congenital rubella syndrome are caused by the rubella virus and are preventable through vaccination, making disease eradication possible. Monitoring of progress towards global eradication and local elimination requires high quality, sensitive disease surveillance that includes laboratory confirmation of cases. Previous evaluations of anti-rubella IgM detection methods resulted in the broad adoption of Enzygnost (most recently manufactured by Siemens) enzyme-linked immunosorbent assay (ELISA or EIA) kits within WHO's global measles and rubella laboratory network but they have been discontinued. This study evaluates seven comparable ELISA methods from six manufacturers (Trinity Biotech, Euroimmun, Clin-Tech, NovaTec and Virion\Serion) as well as one automated chemiluminescent assay (CLIA) from Diasorin. These methods consisted of three IgM capture methods and five indirect ELISA methods. A panel of 238 sera was used for the evaluation that included 38 archival rubella IgM positive sera and 200 sera collected from symptomatically similar cases, such as measles, dengue, parvovirus B19 and roseola. With this panel of sera, the sensitivity of the methods ranged from 63.2% to 100% and the specificity from 80.0% to 99.5%. No single method had both sensitivity and specificity >90%, unless sera with equivocal results were considered to be presumptive positive. Some methods, particularly the Serion ELISA, had a large number of false positives with parvovirus B19 IgM positive sera as well as sera from confirmed measles cases. The performance characteristics identified in this evaluation serve as a reminder to not rely solely on rubella IgM results for case confirmation in elimination settings.
Keyphrases
  • high throughput
  • public health
  • zika virus
  • emergency department
  • helicobacter pylori
  • deep learning
  • helicobacter pylori infection
  • case report
  • single cell
  • label free