Quality control failures exceeding the weekly limit (QC FEWL): a simple tool to improve assay error detection. (November 2019)

• Record Type: Journal Article
• Authors: Kilpatrick, Eric S
• Is Part Of: Annals of clinical biochemistry. Volume 56:Number 6(2019:Nov.)
• Page Start: 668
• Page End: 673
• Publication Date: 2019-11
• Subjects: Quality control -- error detection -- laboratory error -- power function graph; Clinical chemistry -- Periodicals; Clinical biochemistry -- Periodicals; 616.075
• DOI: 10.1177/0004563219869043
• Languages: English
• ISSNs: 0004-5632

Abstract

Background: Even when a laboratory analyte testing process is in control, routine quality control testing will fail with a frequency that can be predicted by the number of quality control levels used, the run frequency and the control rule employed. We explored whether simply counting the number of assay quality control run failures during a running week, and then objectively determining if there was an excess, could complement daily quality control processes in identifying an out-of-control assay. Methods: Binomial statistics were used to determine the threshold number of quality control run failures in any rolling week which would statistically exceed that expected for a particular test. Power function graphs were used to establish error detection (Ped ) and false rejection rates compared with popular control rules. Results: Identifying quality control failures exceeding the weekly limit (QC FEWL) is a more powerful means of detecting smaller systematic (bias) errors than traditional daily control rules (12s, 13s or 13s /22s /R4s ) and markedly superior in detecting smaller random (imprecision) errors while maintaining false identification rates below 2%. Error detection rates also exceeded those using a within- and between-run Westgard multirule (13s /22s /41s /10x ). Conclusions: Daily review of tests shown to statistically exceed their rolling week limit of expected quality control run failures is more powerful than traditional quality control tools at identifying potential systematic and random test errors and so offers a supplement to daily quality control practices that has no requirement for complex data extraction or manipulation.