Practical application of Westgard Sigma rules with run size in analytical biochemistry processes in clinical settings

Sigma metric used to evaluate the performance of haematology analysers: choosing an internal reference analyser for the laboratory

INTRODUCTION

The sigma metric offers a quantitative framework for evaluating process performance in clinical laboratories. This study aimed to evaluate the analytical performance of automated analysers in haematology laboratories, using the sigma metric to choose the best analyser as an internal reference analyser.

MATERIALS AND METHODS

internal quality control (IQC) data were collected for 6 months from SNCS, and the sigma value was calculated for 9 haematology analysers in the laboratory.

RESULTS

For the normal control level, a satisfactory mean sigma value ≥3 was observed for all of the studied parameters of all automated analysers. For the low control level, platelet (PLT) count by Instrument (Inst.) G performed poorly, with a mean sigma value <3. Inst. H, with all parameters' sigma values >4, performed best and was chosen as the internal reference analyser.

CONCLUSION

The sigma metric can be used as a guide to choose the QC strategy and plan QC frequency. It can facilitate the comparison of the same assay performed by multiple systems.

A comparative analysis of Sigma metrics using conventional and alternative formulas

BACKGROUND AND AIM

The Six Sigma approach, employing Sigma Metrics (SM), is commonly used to evaluate analytical performance in clinical laboratories. However, there is ongoing debate regarding the suitability of the conventional SM formula, which incorporates total allowable error (TEa) and bias. To address this, an alternative formula based on within-subject biological variation (CVI) as the tolerance range (TR) has been proposed. The study aimed to calculate and compare SM values using both formulas.

MATERIAL AND METHODS

Twenty clinical chemistry parameters were evaluated, and SM values were calculated using conventional formula with two TEa goals and the alternative formula. Intermediate precision (CVA%) values were obtained from internal quality control data, while bias values were derived from external quality assessment reports.

RESULTS

The results showed that using the conventional formula, 11 SM values based on CLIA TEa goals and 21 SM values based on BV TEa goals were deemed unacceptable (SM < 3). Additionally, 22 SM values calculated using the alternative formula were below 3.

CONCLUSION

The choice of TR had a substantial impact on the assessed analytical performance. Laboratories should carefully consider the appropriateness of each approach based on their specific quality objectives, analyte characteristics, and laboratory operations.

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.

A multi-test planning model for risk based statistical quality control strategies

BACKGROUND

Efforts to improve QC for multi-test analytic systems should focus on risk-based bracketed SQC strategies, as recommended in the CLSI C24-Ed4 guidance for QC practices. The objective is to limit patient risk by controlling the expected number of erroneous patient test results that would be reported over the period an error condition goes undetected.

METHODS

A planning model is described to provide a structured process for considering critical variables for the development of SQC strategies for continuous production multi-test analytic systems. The model aligns with the principles of the CLSI C24-Ed4 "roadmap" and calculation of QC frequency, or run size, based on Parvin's patient risk model. Calculations are performed using an electronic spreadsheet to facilitate application of the planning model.

RESULTS

Three examples of published validation data are examined to demonstrate the application of the planning model for multi-test chemistry and enzyme analyzers. The ability to assess "what if" conditions is key to identifying the changes and improvements that are necessary to simplify the overall system to a manageable number of SQC procedures.

CONCLUSIONS

The planning of risk based SQC strategies should align operational requirements for workload and reporting intervals with QC frequency in terms of the run size or the number of patient samples between QC events. Computer tools that support the calculation of run sizes greatly facilitate the planning process and make it practical for medical laboratories to quickly assess the effects of critical variables.

Practical application of Westgard Sigma rules with run size in analytical biochemistry processes in clinical settings

BACKGROUND

The performance of 18 routine chemical detection methods was evaluated by the sigma (σ) metric, and Westgard Sigma rules with run size were used to establish internal quality control (IQC) standards to reduce patient risks.

MATERIALS AND METHODS

External quality assessment (EQA) and internal quality control data from 18 assays in a biochemical laboratory were collected from January to June 2020. The sigma values of each assay were calculated, based on the bias, total error allowable, and coefficient of variation, appropriate quality control rules were selected. According to the quality goal index, the main causes of poor performance were determined to guide quality improvement.

RESULTS

At IQC material level 1, seven of the 18 assays achieved five sigma (excellent), and five assays (UA, Crea, AMY, TC and Na) showed world-class performance. At IQC material level 2, 14 of the 18 assays achieved 5 sigma (excellent), and thirteen assays (UA, ALT, CK, Crea, AMY, K, AST, ALP, Na, LDH, Mg, TC and GGT) showed world-class performance. The quality goal index (QGI) was calculated for items with analysis performance <5 sigma, and the main causes of poor performance were determined to guide quality improvement.

CONCLUSIONS

Westgard sigma rules with run size are an effective tool for evaluating the performance of biochemical assays. These rules can be used to more simply and intuitively select the quality control strategy of related items and reduce the risk to patients.