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  • van Rossum, Huub H  (3)
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  • 1
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2020
    In:  Clinical Chemistry Vol. 66, No. 9 ( 2020-09-01), p. 1140-1145
    In: Clinical Chemistry, Oxford University Press (OUP), Vol. 66, No. 9 ( 2020-09-01), p. 1140-1145
    Type of Medium: Online Resource
    ISSN: 0009-9147 , 1530-8561
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    Location Call Number Limitation Availability
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  • 2
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2019
    In:  Clinical Chemistry Vol. 65, No. 8 ( 2019-08-01), p. 962-971
    In: Clinical Chemistry, Oxford University Press (OUP), Vol. 65, No. 8 ( 2019-08-01), p. 962-971
    Abstract: For many years the concept of patient-based quality control (QC) has been discussed and implemented in hematology laboratories; however, the techniques have not been widely implemented in clinical chemistry. This is mainly because of the complexity of this form of QC, as it needs to be optimized for each population and often for each analyte. However, the clear advantages of this form of QC, together with the ongoing realization of the shortcomings of “conventional” QC, have driven a need to provide guidance to laboratories to assist in deploying patient-based QC. This overview describes the components of a patient-based QC system (calculation algorithm, block size, truncation limits, control limits) and the relationship of these to the analyte being controlled. We also discuss the need for patient-based QC system optimization using patient data from the individual testing laboratory to reliably detect systematic errors while ensuring that there are few false alarms. The term patient-based real-time quality control covers many activities that use data from patient samples to detect analytical errors. These activities include the monitoring of patient population parameters such as the mean or median analyte value or using single within-patient changes such as the delta check. In this report, we will restrict the discussion to population-based parameters. This overview is intended to serve as a guide for the implementation of a patient-based QC system. The report does not cover the clinical evaluation of the population.
    Type of Medium: Online Resource
    ISSN: 0009-9147 , 1530-8561
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 3
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2020
    In:  Clinical Chemistry Vol. 66, No. 8 ( 2020-08-01), p. 1072-1083
    In: Clinical Chemistry, Oxford University Press (OUP), Vol. 66, No. 8 ( 2020-08-01), p. 1072-1083
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 0009-9147 , 1530-8561
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
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