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The letter explores the use of sigma metrics in laboratory medicine, specifically examining the relationship between analytical imprecision and bias. Data from an external quality assurance program was used to analyze this correlation in participating laboratories. The study found that the correlation between 1/imprecision and sigma metric was stronger than that of bias and sigma metric, underscoring the significance of analytical imprecision in evaluating laboratory performance. The research emphasizes the importance of assessing both bias and imprecision separately to ensure accurate evaluation of laboratory methods for clinical use. [Extracted from the article]

This paper further explores the Sigma Metric (SM) and its application in clinical chemistry. It discusses the SM, assay stability, and control failure relationship.: SM is not a valid measure of assay stability or the likelihood of failure. When an out-of-control event occurs for an assay with a higher SM value, the same QC rule will have greater power to detect error than assays with a lower SM value. Thus, it is easier to prevent errors from happening for higher SM assays. This rationale encourages using more frequent QC events and more QC samples for a QC scheme of a low SM assay or simply more QC cost for low SM assays. A laboratory can have a high-precision instrument that frequently fails and a low-precision instrument that hardly ever fails. Parvin’s patient risk model presumes the bracketed continuous mode (BCM) testing workflow. If overlooked when designing QC schemes, this leads to the common misconception of the SM that one can save the cost of QC since assays with high SM require less frequent QC to ensure patient risk. There is no evidence that an assay’s precision is correlated with its failure rate. Schmidt et al., in a series of papers, showed that an assay with a higher Pf or shift in probability will have a higher expected number of unacceptable results. Incorporating Pf into the QC design process presents significant challenges despite the proactive quality control (PQC) methodology.Unfortunately, TEa Six Sigma, as widely practiced in Clinical Chemistry, is not based on classical Six Sigma mathematical statistics. Classical Six Sigma would facilitate comparing results across activities where the principles of Six Sigma are employed. [ABSTRACT FROM AUTHOR]

Clinical laboratories face limitations in implementing advanced quality control (QC) methods with existing systems. This study aimed to develop a web-based application to addresses this gap, and improve QC practices. QC Constellation, a web application built using Python 3.11, integrates various statistical QC modules. These include Levey-Jennings charts with Westgard rules, sigma-metric calculations, exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts, and method decision charts. Additionally, it offers a risk-based QC section and a patient-based QC module aligning with modern QC practices. The codes and the web application links for QC Constellation were shared at https://github.com/hikmetc/QC%5fConstellation, and http://qcconstellation.com, respectively. Using synthetic data, QC Constellation demonstrated effective implementation of Levey-Jennings charts with user-friendly features like checkboxes for Westgard rules and customizable moving averages graphs. Sigma-metric calculations for hypothetical performance values of serum total cholesterol were successfully performed using allowable total error and maximum allowable measurement uncertainty goals, and displayed on method decision charts. The utility of the risk-based QC module was exemplified by assessing QC plans for serum total cholesterol, showcasing the application's capability in calculating risk-based QC parameters including maximum unreliable final patient results, risk management index, and maximum run size and offering risk-based QC recommendations. Similarly, the patient-based QC and optimization modules were demonstrated using simulated sodium results. In conclusion, QC Constellation emerges as a pivotal tool for laboratory professionals, streamlining the management of quality control and analytical performance monitoring, while enhancing patient safety through optimized QC processes. [ABSTRACT FROM AUTHOR]

The letter discusses the application of Six Sigma methodology in laboratory medicine, focusing on the calculation method of the Sigma Metric (SM) proposed by Westgard. It highlights the flaws in the equation proposed by Westgard, which leads to an underestimation of process performance and incorrect application to quality control rules. The letter emphasizes that the incorrect SM calculation method has contributed to confusion and hindered the successful implementation of Six Sigma in laboratory medicine. The authors suggest caution in using the flawed equation and propose alternative approaches for evaluating process performance accurately. [Extracted from the article]

The Sigma metric is widely used in laboratory medicine. [ABSTRACT FROM AUTHOR]

Sigma metric can be calculated using a simple equation. However, there are multiple sources for the elements in the equation that may produce different Sigma values. This study aimed to investigate the importance of different bias estimation approaches for Sigma metric calculation.

The application of Sigma metrics can be used for assessing the performance of diagnostic laboratories. Clinical laboratories are confronted with the trouble of having to select the best and suitable quality specifications that are required for quality planning. In this regard, our study aims at evaluating the performance of Sysmex XN-1000 hematology analyzer by using Sigma metrics based on different total allowable error (TEa) source and to determine the effects of these variations in Sigma metric evaluation.

Background: A pilot external quality assurance (EQA) survey for the free light chain (FLC) assay was developed and implemented in Korea. Methods: Survey data over 6 years (2010–2015) were collected retrospectively and Sigma metrics were calculated for method-specific peer groups. Results: Nineteen to 29 laboratories participated in the EQA survey, and nephelometric (20%) and turbidimetric (80%) methods were used. Using a previously published clinically relevant reference change value (RCV) of 54.5% as the tolerance limit, the method-specific median Sigma metrics of kappa (κ) and lambda (λ) FLC achieved greater than Three-Sigma for 86–97% of all EQA distributions, and Five-Sigma for 48–72% of all distributions. Conclusions: This EQA analysis of FLC assay applied clinically relevant quality specifications using Sigma metrics. During the 6-year EQA survey, we found that most of the results from participating laboratories meet clinically relevant quality specifications. In addition, method-specific differences were noted for λ FLC, at FLC concentrations above the initial measuring range that require a sample dilution. [ABSTRACT FROM AUTHOR]

The article focuses on a study on the extra-analytical phase, no information is provided regarding the counted (discrete) variables and portion of extra-analytical phase variables consist of counted data. It mentions Sigma metric (SM) is reflected in defects per million opportunity (DPMO) and extra-analytical phase might follow a binomial rather than a normal distribution. It also mentions normal distribution is one of the most frequently used distribution types.

A letter to the editor is presented in response to the article "Evaluation of the performance of INDEXOR® in the archive unit of a clinical laboratory: a step to Lean laboratory" by Abdurrahman Coskun and colleagues in the previous issue.

A letter to the editor in response to the article "To err is human, Building a safer health system," By L. Kohn and colleagues in the previous issue is presented.