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  • 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)
    Badrick, Tony C.
    Year published
    2016
    Metadata
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    Abstract
    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 ...
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    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
    DOI
    https://doi.org/10.1016/j.clinbiochem.2016.07.012
    Subject
    Medical biochemistry and metabolomics
    Clinical sciences
    Science & Technology
    Life Sciences & Biomedicine
    Medical Laboratory Technology
    Analytical
    Error
    Publication URI
    http://hdl.handle.net/10072/407961
    Collection
    • Journal articles

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