Antinuclear antibodies detection: A comparative study between automated recognition and conventional visual interpretation

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Abstract

Background: The indirect immunofluorescence assay (IIFA) for the detection of antinuclear antibodies (ANA) was firstly described in 1958 and is still considered the reference method for ANA screening. Currently, an automated processing and recognition system for standardized and efficient ANA interpretation by human epithelial (HEp-2) cell-based immunofluorescence (IIF; EUROPattern Suite, Euroimmun) is available in China. Methods: In this study, the performance of this novel system for positive/negative classification, pattern recognition (including homogenous, speckled, nucleolar, nuclear dots, cytoplasmic, and centromeres patterns) and titers evaluation was evaluated by comparing to visual interpretation. Results: Referring to the total of 3681 collected samples, there was an agreement of 98.7% (κ = 0.973) between the visual and automated examination regarding positive/negative discrimination. In sera with single pattern, correct pattern recognition was observed in 94.6% of the samples. The efficiency of automated recognition for single pattern varied for the different patterns. The automatically determined patterns were correct and complete in 1071 of 1620 cases and correct and meaningful but not complete (“main pattern”) in another 405 cases, enabling main pattern recognition in 91.1% of all cases. Referring to the titers evaluation, the results within the next titer were considered to be consistent. In 1603 positive sera both by visual and automated evaluation, titers of 1514 sample were consistent, accounting for 94.4%. Conclusion: Attributed to the performance characteristics, EUROPattern system is suitable for clinical use as its high degree of automation and result reliability, and may help clinical laboratories to standardize of IIF evaluation.

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Li, Z. Y., Han, R. L., Yan, Z. L., Li, L. J., & Feng, Z. R. (2019). Antinuclear antibodies detection: A comparative study between automated recognition and conventional visual interpretation. Journal of Clinical Laboratory Analysis, 33(1). https://doi.org/10.1002/jcla.22619

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