ArticleJournal of clinical laboratory analysis2021
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.
Article in Journal of clinical laboratory analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Risk-based redesign of internal quality control procedures for emergency immunoassays in clinical laboratories: a multicenter study from China.Frontiers in medicine · 2026Article
- Enhancing Laboratory Quality: A Comprehensive Sigma Metric Analysis for Diverse Biochemical Parameters.EJIFCC · 2025Article
- The Application of Six Sigma to Assess the Analytical Performance of Plasma Proteins and Design a Risk-Based Statistical Quality Control Strategy: A Multicenter Study.Journal of clinical laboratory analysis · 2025Article
- Potential use of Six Sigma metrics in the quality control review of hospital glucose meters.Heliyon · 2024Article
- An assessment of analytical performance using the six sigma scale in second-trimester maternal prenatal screening practices in China.Practical laboratory medicine · 2024Article
- 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.Journal of clinical laboratory analysis · 2021Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundThe 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.
methodsInternal 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).
resultsSigma 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.
conclusionsIn 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.
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