Evaluation of the analytical performance of endocrine analytes using sigma metrics

Application of a six sigma model to evaluate the analytical performance of cerebrospinal fluid biochemical analytes and the design of quality control strategies for these assays: A single-centre study

BACKGROUND

In this study, we applied a six sigma model to examine cerebrospinal fluid (CSF) biochemical analytes for the first time. Our goal was to evaluate the analytical performance of various CSF biochemical analytes, design an optimized internal quality control (IQC) strategy, and formulate scientific and reasonable improvement plans.

METHODS

The sigma values of CSF total protein (CSF-TP), albumin (CSF-ALB), chloride (CSF-Cl), and glucose (CSF-GLU) were calculated using the following formula: sigma = [TEa(%)-|bias(%)|]/CV(%). The analytical performance of each analyte was shown using a normalized sigma method decision chart. Individualized IQC schemes and improvement protocols for CSF biochemical analytes were formulated using the Westgard sigma rule flow chart with batch size and quality goal index (QGI).

RESULTS

The distribution of sigma values for CSF biochemical analytes ranged from 5.0 to 9.9, and the sigma values varied for different concentrations of the same analyte. The analytical performance of the CSF assays at the two QC levels is displayed visually in normalized sigma method decision charts. Individualized IQC strategies for CSF biochemical analytes were as follows: for CSF-ALB, CSF-TP and CSF-Cl, use 1_3s with N = 2 and R = 1000; for CSF-GLU, use 1_3s/2_2s/R_4s with N = 2 and R = 450. In addition, priority improvement measures for analytes with sigma values less than 6 (CSF-GLU) were formulated based on the QGI, and their analytical performance was improved after the corresponding improvement measures were taken.

CONCLUSIONS

The six sigma model has significant advantages in practical applications involving CSF biochemical analytes and is highly useful for quality assurance and quality improvement.

Sigma Metrics in Laboratory Medicine: A Call for Harmonization

BACKGROUND AND AIM

Sigma metrics are applied in clinical laboratories to assess the quality of analytical processes. A parameter associated to a Sigma >6 is considered "world class" whereas a Sigma <3 is "poor" or "unacceptable". The aim of this retrospective study was to quantify the impact of different approaches for Sigma metrics calculation.

MATERIAL AND METHODS

Two IQC levels of 20 different parameters were evaluated for a 12-month period. Sigma metrics were calculated using the formula: (allowable total error (TEa) (%) - bias (%))/(coefficient of variation (CV) (%)). Method precision was calculated monthly or annually. The bias was obtained from peer comparison program (PCP) or external quality assessment program (EQAP), and 9 different TEa sources were included.

RESULTS

There was a substantial monthly variation of Sigma metrics for all combinations, with a median variation of 32% (IQR, 25.6-41.3%). Variation across multiple analyzers and IQC levels were also observed. Furthermore, TEa source had the highest impact on Sigma calculation with proportions of Sigma >6 ranging from 17.5% to 84.4%. The nature of bias was less decisive.

CONCLUSION

In absence of a clear consensus, we recommend that laboratories calculate Sigma metrics on a sufficiently long period of time (>6 months) and carefully evaluate the choice of TEa source.

"Six Sigma" standard as a level of quality of biochemical laboratories

The principal role of biochemical laboratories is responsibility for reliable, reproducible, accurate, timely, and accurately interpreted analysis results that help in making clinical decisions, while ensuring the desired clinical outcomes. To achieve this goal, the laboratory should introduce and maintain quality control in all phases of work. The importance of applying the Six SIGMA quality model has been analyzed in a large number of scientific studies. The purpose of this review is to highlight the importance of using six SIGMA metrics in biochemical laboratories and the current application of six SIGMA metrics in all laboratory work procedures. It has been shown that the six SIGMA model can be very useful in improving all phases of laboratory work, as well as that a detailed assessment of all procedures of the phases of work and improvement of the laboratory's quality control system is crucial for the laboratory to have the highest level of six SIGMA. Clinical laboratories should use SIGMA metrics to monitor their performance, as it makes it easier to identify gaps in their performance, thereby improving their efficiency and patient safety. Medical laboratory quality managers should provide a systematic methodology for analyzing and correcting quality assurance systems to achieve Six SIGMA quality-level standards.

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

Background

The six sigma model has been widely used in clinical laboratory quality management. In this study, we first applied the six sigma model to (a) evaluate the analytical performance of urinary biochemical analytes across five laboratories, (b) design risk‐based statistical quality control (SQC) strategies, and (c) formulate improvement measures for each of the analytes when needed.

Methods

Internal quality control (IQC) and external quality assessment (EQA) data for urinary biochemical analytes were collected from five laboratories, and the sigma value of each analyte was calculated based on coefficients of variation, bias, and total allowable error (TEa). Normalized sigma method decision charts for these urinary biochemical analytes were then generated. Risk‐based SQC strategies and improvement measures were formulated for each laboratory according to the flowchart of Westgard sigma rules, including run sizes and the quality goal index (QGI).

Results

Sigma values of urinary biochemical analytes were significantly different at different quality control levels. Although identical detection platforms with matching reagents were used, differences in these analytes were also observed between laboratories. Risk‐based SQC strategies for urinary biochemical analytes were formulated based on the flowchart of Westgard sigma rules, including run size and analytical performance. Appropriate improvement measures were implemented for urinary biochemical analytes with analytical performance lower than six sigma according to the QGI calculation.

Conclusions

In multilocation laboratory systems, a six sigma model is an excellent quality management tool and can quantitatively evaluate analytical performance and guide risk‐based SQC strategy development and improvement measure implementation.

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

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.

Evaluation of the analytical performance of endocrine analytes using sigma metrics

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 (1_3S) 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 (1_3S/2_2S/R_4S/4_1S/10_X) 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.