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  • Understanding Patient-Based Real-Time Quality Control Using Simulation Modeling.

    Author(s)
    Bietenbeck, Andreas
    Cervinski, Mark A
    Katayev, Alex
    Loh, Tze Ping
    van Rossum, Huub H
    Badrick, Tony
    Griffith University Author(s)
    Badrick, Tony C.
    Year published
    2020
    Metadata
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    Abstract
    Background: Patient-based real-time quality control (PBRTQC) avoids limitations of traditional quality control methods based on the measurement of stabilized control samples. However, PBRTQC needs to be adapted to the individual laboratories with parameters such as algorithm, truncation, block size, and control limit. Methods: In a computer simulation, biases were added to real patient results of 10 analytes with diverse properties. Different PBRTQC methods were assessed on their ability to detect these biases early. Results: The simulation based on 460 000 historical patient measurements for each analyte revealed several ...
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    Background: Patient-based real-time quality control (PBRTQC) avoids limitations of traditional quality control methods based on the measurement of stabilized control samples. However, PBRTQC needs to be adapted to the individual laboratories with parameters such as algorithm, truncation, block size, and control limit. Methods: In a computer simulation, biases were added to real patient results of 10 analytes with diverse properties. Different PBRTQC methods were assessed on their ability to detect these biases early. Results: The simulation based on 460 000 historical patient measurements for each analyte revealed several recommendations for PBRTQC. Control limit calculation with "percentiles of daily extremes" led to effective limits and allowed specification of the percentage of days with false alarms. However, changes in measurement distribution easily increased false alarms. Box-Cox but not logarithmic transformation improved error detection. Winsorization of outlying values often led to a better performance than simple outlier removal. For medians and Harrell-Davis 50 percentile estimators (HD50s), no truncation was necessary. Block size influenced medians substantially and HD50s to a lesser extent. Conversely, a change of truncation limits affected means and exponentially moving averages more than a change of block sizes. A large spread of patient measurements impeded error detection. PBRTQC methods were not always able to detect an allowable bias within the simulated 1000 erroneous measurements. A web application was developed to estimate PBRTQC performance. Conclusions: Computer simulations can optimize PBRTQC but some parameters are generally superior and can be taken as default.
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    Journal Title
    Clinical Chemistry
    Volume
    66
    Issue
    8
    DOI
    https://doi.org/10.1093/clinchem/hvaa094
    Subject
    Medical biotechnology
    Medical biochemistry and metabolomics
    Clinical sciences
    average of normals
    exponentially weighted moving average
    moving average
    optimization
    quality control
    Publication URI
    http://hdl.handle.net/10072/396372
    Collection
    • Journal articles

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