Application of Sigma metrics in the quality control strategies of immunology and protein analytes. Issue 11 (4th October 2021) - Luo, Yanfen; Yan, Xingxing; Xiao, Qian; Long, Yifei; Pu, Jieying; Li, Qiwei; Cai, Yimei; Chen, Yushun; Zhang, Hongyuan; Chen, Cha; Ou, Songbang
Abstract: Background: Six Sigma (6σ) is an efficient laboratory management method. We aimed to analyze the performance of immunology and protein analytes in terms of Six Sigma. Methods: Assays were evaluated for these 10 immunology and protein analytes: Immunoglobulin G (IgG), Immunoglobulin A (IgA), Immunoglobulin M (IgM), Complement 3 (C3), Complement 4 (C4), Prealbumin (PA), Rheumatoid factor (RF), Anti streptolysin O (ASO), C‐reactive protein (CRP), and Cystatin C (Cys C). The Sigma values were evaluated based on bias, four different allowable total error (TEa) and coefficient of variation (CV) at QC materials levels 1 and 2 in 2020. Sigma Method Decision Charts were established. Improvement measures of analytes with poor performance were recommended according to the quality goal index (QGI), and appropriate quality control rules were given according to the Sigma values. Results: While using the TEaNCCL, 90% analytes had a world‐class performance with σ>6, Cys C showed marginal performance with σ<4. While using minimum, desirable, and optimal biological variation of TEa, only three (IgG, IgM, and CRP), one (CRP), and one (CRP) analytes reached 6σ level, respectively. Based on σNCCL that is calculated from TEaNCCL, Sigma Method Decision Charts were constructed. For Cys C, five multi‐rules (13s /22s /R4s /41s /6X, N = 6, R = 1, Batch length: 45) were adopted for QC management. The remaining analytes required only one QC rule (13s, N = 2, R = 1, Batch length: 1000). Cys C need to improve precision (QGI = 0.12). Conclusions: The laboratories should choose appropriate TEa goals and make judicious use of Sigma metrics as a quality improvement tool. Abstract : We first evaluated the performance of 10 immunology and protein analytes based on four different sources of TEa. Further the Sigma Method Decision Charts were constructed in terms of TEaNCCL, providing us with a visual view of the analytes' performance. For Cys C with σ<6, the cause for poor performance was evaluated using QGI.