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Research Article Open Access
Background: Analytical precision of immunoassays (IA) may be affected by several factors. In this study, we developed and used a graphical exploratory data analysis tool which provided increased understanding of the relationship between the results of quality control (QC) and donor samples tested in an IA.
Methods and Findings: Hepatitis B surface antigen (HBsAg) results from 712 QC tests and 177,910 negative samples collected from blood donors over a period of nine-months were analysed. The current study describes a simple visualization tool, quilt-plots (or heat maps), which can compare the extent of changes in donors’ test results with associated changes in QC reactivity levels by using changing shades of colour. The QC and donor test results were obtained using the Abbott PRISM HBsAg chemiluminescent immunoassay (PRISM HBV); there was a shift towards lower values in the donors’ results when QC reactivity was low. However, these effects were small and only detected due to the large sample size. Visualization of the accumulated data from sequential monitoring of donor and QC test results using quilt-plots (also known as heat-maps) enhances the interpretation of the relationship between QC and donor test results.
Conclusions: Results of the quilt-plots can be used as an historical reference to predict the future performance of the IA and demonstrates that changes in QC reactivity do not necessarily predict changes in test results of donors found to be negative for HBsAg.
Quality control process, Graphical assessment, Blood donors, Clinical Data Management, Immunology Research, Hepatitis B