Open AccessApplication of Sigma metrics in the quality control strategies of immunology and protein analytes
Author(s) -
Luo Yanfen,
Yan Xingxing,
Xiao Qian,
Long Yifei,
Pu Jieying,
Li Qiwei,
Cai Yimei,
Chen Yushun,
Zhang Hongyuan,
Chen Cha,
Ou Songbang
Publication year - 2021
Publication title -
journal of clinical laboratory analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.536
H-Index - 50
eISSN - 1098-2825
pISSN - 0887-8013
DOI - 10.1002/jcla.24041
Subject(s) - quality (philosophy) , computational biology , sigma , chemistry , quality by design , control (management) , immunology , computer science , biology , artificial intelligence , physics , philosophy , epistemology , quantum mechanics , particle size
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 TEa NCCL , 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 σ NCCL that is calculated from TEa NCCL , Sigma Method Decision Charts were constructed. For Cys C, five multi‐rules (1 3s /2 2s /R 4s /4 1s /6 X , N = 6, R = 1, Batch length: 45) were adopted for QC management. The remaining analytes required only one QC rule (1 3s , 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.