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Planning Risk-Based Statistical Quality Control Strategies: Graphical Tools to Support the New Clinical and
Hassan Bayat1, Sten A Westgard2, James O Westgard2,3
1Sina Medical Laboratory, Qaem Shahr, Iran.
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.
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