Multirule procedures vs moving average algorithms for IQC: An appropriate comparison reveals how best to combine their strengths

• Published in: Clinical biochemistry 2022-04, Vol.102, p.50-55
• Main Authors: Bayat, Hassan, Westgard, Sten A., Westgard, James O.
• Format: Article
• Language: English
• Subjects: Algorithms; Humans; Moving average algorithms; Multirule QC; Quality Control; Statistical Quality Control
• ISSN: 0009-9120; 1873-2933
• DOI: 10.1016/j.clinbiochem.2022.01.001

Moving Average Algorithms (MAA) have been widely recommended for use in Patient Based Real Time Quality Control applications (PBRTQC) to supplement or replace traditional Internal Quality Control (IQC) techniques. A recent “proof of concept” study recommends applying MAAs to IQC data to replace traditional IQC procedures because they “outperform Westgard Rules,” which is a current standard of practice for IQC. We generated power curves for multi-rule procedures with 2 and 4 control measurements per QC event, as well as a Simple Moving Average having block sizes of 5, 10, and 20 control measurements. We also assessed time to detection in terms of the Average Number of QC Events required to detect different sizes of systematic errors. As expected, the more control measurements included in the control technique, the better the error detection. However, when QC performance is considered on the Sigma Scale, high Sigma methods require only 1 or 2 control measurements to detect medically important systematic errors. MAAs have very low ability to detect error at the first few QC events following shift, so they suffer a lag phase in detecting medically important errors. MAAs are most useful for methods having 4.0 Sigma performance or less. Even then, large systematic shifts are more quickly detected by simple single and multirule procedures. Choice of control techniques (rules, means, ranges, etc.) should consider the Sigma-metric of the method. For methods having Sigmas of 4 or greater, traditional single rule and multirule procedures with Ns up to 4 are most effective; below 4 Sigma, a multirule coupled with a Simple Moving Average (SMA) rule with Ns of 4 to 8 can improve error detection.