Summary
The variability between calibrations can be larger than the within calibration variation
for some measurement procedures, that is a large CVbetween:CVwithin ratio. In this study, we examined the false rejection rate and probability of bias
detection of quality control (QC) rules at varying calibration CVbetween:CVwithin ratios.
Historical QC data for six representative routine clinical chemistry serum measurement
procedures (calcium, creatinine, aspartate aminotransferase, thyrotrophin, prostate
specific antigen and gentamicin) were extracted to derive the CVbetween:CVwithin ratios using analysis of variance. Additionally, the false rejection rate and probability
of bias detection of three ‘Westgard’ QC rules (2:2S, 4:1S, 10X) at varying CVbetween:CVwithin ratios (0.1–10), magnitudes of bias, and QC events per calibration (5–80) were examined
through simulation modelling.
The CVbetween:CVwithin ratios for the six routine measurement procedures ranged from 1.1 to 34.5. With ratios
>3, false rejection rates were generally above 10%. Similarly for QC rules involving
a greater number of consecutive results, false rejection rates increased with increasing
ratios, while all rules achieved maximum bias detection.
Laboratories should avoid the 2:2S, 4:1S and 10X QC rules when calibration CVbetween:CVwithin ratios are elevated, particularly for those measurement procedures with a higher
number of QC events per calibration.
Key words
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References
- Calibration practices in clinical mass spectrometry: review and recommendations.Ann Lab Med. 2023; 43: 5-18
- Detecting long-term drift in reagent lots.Clin Chem. 2015; 61: 1292-1298
- Expected mean squares for the random effects one-way ANOVA model when sampling from a finite population.Thailand Statistician. 2012; 10: 121-128
- Establishing commonsense-based statistical quality control practices.Am J Clin Pathol. 2019; 151: 350-352
- An analysis of multirules for monitoring assay quality control.Lab Med. 2020; 51: 94-98
- Quality control practices for chemistry and immunochemistry in a cohort of 21 large academic medical centers.Am J Clin Pathol. 2018; 150: 96-104
- Internal quality control: moving average algorithms outperform Westgard rules.Clin Biochem. 2021; 98: 63-69
Article info
Publication history
Published online: March 08, 2023
Accepted:
November 23,
2022
Received in revised form:
November 15,
2022
Received:
September 27,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2023 Royal College of Pathologists of Australasia. Published by Elsevier B.V. All rights reserved.
