Background: Clinical and Laboratory Standards Institute (CLSI)'s new guideline for statistical quality control (SQC; C24-Ed4) (CLSI C24-Ed4, 2016; Parvin CA, 2017) recommends the implementation of risk-based SQC strategies. Important changes from earlier editions include alignment of principles and concepts with the general patient risk model in CLSI EP23A (CLSI EP23A, 2011) and a recommendation for optimizing the frequency of SQC (number of patients included in a run, or run size) on the basis of the expected number of unreliable final patient results. The guideline outlines a planning process for risk-based SQC strategies and describes 2 applications for examination procedures that provide 9-σ and 4-σ quality. A serious limitation is that there are no practical tools to help laboratories verify the results of these examples or perform their own applications.
Methods: Power curves that characterize the rejection characteristics of SQC procedures were used to predict the risk of erroneous patient results based on Parvin's MaxE(Nuf) parameter (Clin Chem 2008;54:2049-54). Run size was calculated from MaxE(Nuf) and related to the probability of error detection for the critical systematic error (Pedc).
Results: A plot of run size vs Pedc was prepared to provide a simple nomogram for estimating run size for common single-rule and multirule SQC procedures with Ns of 2 and 4.
Conclusions: The "traditional" SQC selection process that uses power function graphs to select control rules and the number of control measurements can be extended to determine SQC frequency by use of a run size nomogram. Such practical tools are needed for planning risk-based SQC strategies.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1373/jalm.2017.023192 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A spreadsheet tool for designing statistical quality control programs based on patient risk parameters.
Clin Biochem
June 2023
Brain Hospital of Hunan Province (The Second People's Hospital of Hunan Province), Changsha, Hunan, PR China; Hunan Center For Clinical Laboratory, Changsha, Hunan, PR China.
Background: Quality control (QC) in the laboratory aims to reduce the risk of harm to a patient due to erroneous results, as highlighted by the Clinical Laboratory Standards Institute (CLSI) guidance for Statistical Quality Control (SQC) (C24-Ed4). To effectively reduce patient risk, a convenient spreadsheet tool was developed to assist laboratories in SQC design based on patient risk parameters.
Methods: In accordance with Parvin's patient risk model and the mathematical formula for calculating the expected number of unreliable final patient results [E(N)], the function is edited using Excel software, and the maximum E(N) [MaxE(N)] value and other risk parameters based on the current QC strategy are calculated to assess the risk of the QC strategy.
Background: We developed a new practical tool and applied it to assess the performance of 14 biochemical assays and designed risk-based statistical quality control (SQC) procedures.
Methods: Two graphs were combined to develop the new tool. Data points of assays were plotted on the tool to determine their sigma performance and the risk-based SQC procedures.
How to evaluate fixed clinical QC limits vs. risk-based SQC strategies.
Clin Chem Lab Med
August 2022
Westgard QC, Inc., Madison WI, USA.
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.
J Clin Lab Anal
November 2021
Department of Laboratory Medicine, The Second People's Hospital of Lianyungang, Lianyungang, China.
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).
A multi-test planning model for risk based statistical quality control strategies.
Clin Chim Acta
December 2021
Westgard QC, Inc., Madison WI, USA; University of Wisconsin School of Public Health, Madison, WI, USA. Electronic address:
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.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!