Planning Risk-Based Statistical Quality Control Strategies: Graphical Tools to Support the New Clinical and Laboratory Standards Institute C24-Ed4 Guidance

Hassan Bayat, Sten A. Westgard, James O. Westgard
2017 The Journal of Applied Laboratory Medicine  
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
more » ... ted 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. 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). 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. 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.
doi:10.1373/jalm.2017.023192 pmid:32630969 fatcat:bh3xavirdna6tmh44geum7algi