Evaluation of the analytical performance of endocrine analytes using sigma metrics

Abstract

Background (a) To evaluate the clinical performance of endocrine analytes using the sigma metrics (σ) model. (b) To redesign quality control strategies for performance improvement. Methods The sigma values of the analytes were initially evaluated based on the allowable total error (TEa), bias, and coefficient of variation (CV) at QC materials level 1 and 2 in March 2018. And then, the normalized QC performance decision charts, personalized QC rules, quality goal index (QGI) analysis, and root causes analysis (RCA) were performed based on the sigma values of the analytes. Finally, the sigma values were re‐evaluated in September 2018 after a series of targeted corrective actions. Results Based on the initial sigma values, two analytes (FT3 and TSH) with σ > 6, only needed one QC rule (13S) with N2 and R500 for QC management. On the other hand, seven analytes (FT4, TT4, CROT, E2, PRL, TESTO, and INS) with σ < 4 at one QC material level or both needed multiple rules (13S/22S/R4S/41S/10X) with N6 and R10‐500 depending on different sigma values for QC management. Subsequently, detailed and comprehensive RCA and timely corrective actions were performed on all the analytes base on the QGI analysis. Compared with the initial sigma values, the re‐evaluated sigma metrics of all the analytes increased significantly. Conclusions It was demonstrated that the combination of sigma metrics, QGI analysis, and RCA provided a useful evaluation system for the analytical performance of endocrine analytes.

Application of Sigma metrics in the quality control strategies of immunology and protein analytes

“…According to the equation Sigma = (TEa% ‐ Bias%)/CV%, significantly different σ values are obtained using the same Bias% and CV% but different total allowable error targets similar to the previous studies, 10,11 the greater the TEa is, the greater the Sigma value is. In this study, initially five different TEa targets were included.…”

“…Further the Sigma Method Decision Charts were constructed in terms of TEa NCCL , providing us with a visual view of the analytes’ performance. For analytes with σ<6, the cause for poor performance was evaluated using QGI similar to previous studies 10,11 …”

“…A QGI value between 0.8 and 1.2 (0.8 ≤ QGI ≤1.2) indicates that both accuracy and precision need to be improved. Analytes with lower Sigma were analyzed according to QGI, that is, the problem was due to precision or accuracy or combination of both, helping the laboratories to take targeted measures 10,16‐18 …”

“…The target mean assigned by NCCL of each analyte was considered as target value. Excluding unqualified PT data (Data exceeding two standard deviations of the mean), the calculation equation was as follows 10 , 11 :…”

Application of a six sigma model to evaluate the analytical performance of urinary biochemical analytes and design a risk‐based statistical quality control strategy for these assays: A multicenter study

“…However, the performance specifications of urinary biochemical analytes have no data about biological variation, so we selected a quality goal of urinary bio- Previous studies have shown that the six sigma model has been widely used to evaluate the analytical performance of serum biochemical markers, immunological markers, and other analytes and to guide laboratories in designing risk-based SQC strategies and improvement measures. [18][19][20][21][22] However, the application of six sigma models in urinary biochemical analytes is rare at present. Therefore, we aimed to use the six sigma model to evaluate the analytical performance of urinary biochemical analytes across five laboratories, design risk-based SQC strategies and quality improvement measures, and provide more accurate and reliable analytical results for clinical application.…”