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
| Autor: | Ying Chen, Jingjing Han, Guangrong Bian, Xinkuan Chen, Qian Liu, Fumeng Yang, Menglin Wang |
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| Rok vydání: | 2021 |
| Předmět: |
Quality Control
Microbiology (medical) analytical performance Analyte Quality management Clinical Biochemistry risk‐based statistical quality control strategy Urinalysis External quality assessment Humans Immunology and Allergy Research Articles Mathematics Biochemistry (medical) six sigma Public Health Environmental and Occupational Health Six Sigma Sigma Hematology Reference Standards Statistical process control Reliability engineering Internal quality quality goal index Medical Laboratory Technology Multicenter study urinary biochemical analytes Biomarkers Laboratories Clinical Total Quality Management Research Article |
| Zdroj: | Journal of Clinical Laboratory Analysis |
| ISSN: | 1098-2825 0887-8013 |
| Popis: | 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. Flowchart of Westgard sigma rules with run sizes (cited from website http://www.clinet.com.cn/sigmapv/#sgm4) |
| Databáze: | OpenAIRE |
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