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Objectives: This paper further explores the Sigma Metric (SM) and its application in clinical chemistry. It discusses the SM, assay stability, and control failure relationship., Content: : 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 P f or shift in probability will have a higher expected number of unacceptable results. Incorporating P f into the QC design process presents significant challenges despite the proactive quality control (PQC) methodology., Summary: 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., (© 2024 Walter de Gruyter GmbH, Berlin/Boston.)

The Sigma metric is widely used in laboratory medicine., (© 2024 Walter de Gruyter GmbH, Berlin/Boston.)

Objectives: 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., Methods: 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_Constellation, and http://qcconstellation.com, respectively., Results: 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., Conclusions: 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., (© 2024 Walter de Gruyter GmbH, Berlin/Boston.)