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External quality assessment schemes for glucose measurements in Germany: factors for successful participation, analytical performance and medical impact.
Bietenbeck, Andreas Geilenkeuser, Wolf J Klawonn, Frank Spannagl, Michael Nauck, Matthias Petersmann, Astrid Thaler, Markus A Winter, Christof Luppa, Peter B
Bietenbeck, Andreas
Geilenkeuser, Wolf J
Klawonn, Frank
Spannagl, Michael
Nauck, Matthias
Petersmann, Astrid
Thaler, Markus A
Winter, Christof
Luppa, Peter B
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2018-07-26
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Determination of blood glucose concentration is one of the most important measurements in clinical chemistry worldwide. Analyzers in central laboratories (CL) and point-of-care tests (POCT) are both frequently used. In Germany, regular participation in external quality assessment (EQA) schemes is mandatory for laboratories performing glucose testing. Glucose testing data from the two German EQAs "Reference Institute for Bioanalytics" (RfB) and "INSTAND - Gesellschaft zur Förderung der Qualitätssicherung in medizinischen Laboratorien" (Instand) were analyzed from 2012 to 2016. Multivariable odds ratios (OR) for the probability to reach a "good" result were calculated. Imprecision and bias were determined and clinical risk of measurement errors estimated. The device employed was the most important variable required for a "good" performance in all EQAs. Additional participation in an EQA for CL automated analyzers improved performance in POCT EQAs. The reciprocal effect was less pronounced. New participants performed worse than experienced participants especially in CL EQAs. Imprecision was generally smaller for CL, but some POCT devices reached a comparable performance. Large lot-to-lot differences occurred in over 10% of analyzed cases. We propose the "bias budget" as a new metric to express the maximum allowable bias that still carries acceptable medical risk. Bias budgets were smallest and clinical risks of errors greatest in the low range of measurement 60-115 mg/dL (3.3-6.4 mmol/L) for most devices. EQAs help to maintain high analytical performances. They generate important data that serve as the foundation for learning and improvement in the laboratory healthcare system.
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1437-4331
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Attribution-NonCommercial-ShareAlike 3.0 United States