Verification of out-of-control situations detected by "average of normal" approach
Author(s)
Liu, Jiakai
Tan, Chin Hon
Loh, Tze Ping
Badrick, Tony
Griffith University Author(s)
Year published
2016
Metadata
Show full item recordAbstract
Objectives: “Average of normal” (AoN) or “moving average” is increasingly used as an adjunct quality control tool in laboratory practice. Little guidance exists on how to verify if an out-of-control situation in the AoN chart is due to a shift in analytical performance, or underlying patient characteristics. Design and methods: Through simulation based on clinical data, we examined 1) the location of the last apparently stable period in the AoN control chart after an analytical shift, and 2) an approach to verify if the observed shift is related to an analytical shift by repeat testing of archived patient samples from the ...
View more >Objectives: “Average of normal” (AoN) or “moving average” is increasingly used as an adjunct quality control tool in laboratory practice. Little guidance exists on how to verify if an out-of-control situation in the AoN chart is due to a shift in analytical performance, or underlying patient characteristics. Design and methods: Through simulation based on clinical data, we examined 1) the location of the last apparently stable period in the AoN control chart after an analytical shift, and 2) an approach to verify if the observed shift is related to an analytical shift by repeat testing of archived patient samples from the stable period for 21 common analytes. Results: The number of blocks of results to look back for the stable period increased with the duration of the analytical shift, and was larger when smaller AoN block sizes were used. To verify an analytical shift, 3 archived samples from the analytically stable period should be retested. In particular, the process is deemed to have shifted if a difference of > 2 analytical standard deviations (i.e. 1:2 s rejection rule) between the original and retested results are observed in any of the 3 samples produced. The probability of Type-1 error (i.e., false rejection) and power (i.e., detecting true analytical shift) of this rule are < 0.1 and > 0.9, respectively. Conclusions: The use of appropriately archived patient samples to verify an apparent analytical shift is preferred to quality control materials. Nonetheless, the above findings may also apply to quality control materials, barring matrix effects.
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View more >Objectives: “Average of normal” (AoN) or “moving average” is increasingly used as an adjunct quality control tool in laboratory practice. Little guidance exists on how to verify if an out-of-control situation in the AoN chart is due to a shift in analytical performance, or underlying patient characteristics. Design and methods: Through simulation based on clinical data, we examined 1) the location of the last apparently stable period in the AoN control chart after an analytical shift, and 2) an approach to verify if the observed shift is related to an analytical shift by repeat testing of archived patient samples from the stable period for 21 common analytes. Results: The number of blocks of results to look back for the stable period increased with the duration of the analytical shift, and was larger when smaller AoN block sizes were used. To verify an analytical shift, 3 archived samples from the analytically stable period should be retested. In particular, the process is deemed to have shifted if a difference of > 2 analytical standard deviations (i.e. 1:2 s rejection rule) between the original and retested results are observed in any of the 3 samples produced. The probability of Type-1 error (i.e., false rejection) and power (i.e., detecting true analytical shift) of this rule are < 0.1 and > 0.9, respectively. Conclusions: The use of appropriately archived patient samples to verify an apparent analytical shift is preferred to quality control materials. Nonetheless, the above findings may also apply to quality control materials, barring matrix effects.
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Journal Title
Clinical Biochemistry
Volume
49
Issue
16-17
Subject
Medical biochemistry and metabolomics
Clinical sciences
Science & Technology
Life Sciences & Biomedicine
Medical Laboratory Technology
Analytical
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