Horebeke, Belgium
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Stepman H.,Ghent University | Stockl D.,State Consulting
Klinicka Biochemie a Metabolismus | Year: 2012

Because the topic of this essay is often considered boring, we introduce here 3 friendly people who we may also use in later essays. They are the curious layperson, the "All-Knowing" Clinical Biochemist, and the "earth-bound" secretary. While the concept of the measurand seems to be easy, the discipline has surprisingly different opinions about certain specific measurands. This holds true, in particular, for what concerns the measurement of component mixtures. Nevertheless, the unit for mixture analysis should be mol and tests should measure equimolar to the medically relevant extent.


Stepman H.C.M.,Ghent University | Tiikkainen U.,Labquality | Stockl D.,State Consulting | Vesper H.W.,Centers for Disease Control and Prevention | And 4 more authors.
Clinical Chemistry | Year: 2014

BACKGROUND: External quality assessment (EQA) with commutable samples is essential for assessing the quality of assays performed by laboratories, particularly when the emphasis is on their standardization status and interchangeability of results. METHODS: We used a panel of 20 fresh-frozen single-donation serum samples to assess assays for the measurement of creatinine, glucose, phosphate, uric acid, total cholesterol, HDL cholesterol, LDL cholesterol, and triglycerides. The commercial random access platforms included: Abbott Architect, Beckman Coulter AU, Ortho Vitros, Roche Cobas, Siemens Advia, and Thermo Scientific Konelab. The assessment was done at the peer group level and by comparison against the all-method trimmed mean or reference method values, where available. The considered quality indicators were intraassay imprecision, combined imprecision (including sample-matrix interference), bias, and total error. Fail/pass decisions were based on limits reflecting state-of-the-art performance, but also limits related to biological variation. RESULTS: Most assays showed excellent peer performance attributes, except for HDL- and LDL cholesterol. Cases in which individual assays had biases exceeding the used limits were the Siemens Advia creatinine (-4.2%), Ortho Vitros phosphate (8.9%), Beckman Coulter AU triglycerides (5.4%), and Thermo Scientific Konelab uric acid (6.4%), which lead to considerable interassay discrepancies. Additionally, large laboratory effects were observed that caused interlaboratory differences of >30%. CONCLUSIONS: The design of the EQA study was well suited for monitoring different quality attributes of assays performed in daily laboratory practice. There is a need for improvement, even for simple clinical chemistry analytes. In particular, the interchangeability of results remains jeopardized both by assay standardization issues and individual laboratory effects. © 2014 American Association for Clinical Chemistry.


Stockl D.,State consulting | Van Uytfanghe K.,Ghent University | Van Aelst S.,Ghent University | Thienpont L.M.,Ghent University
Clinical Chemistry and Laboratory Medicine | Year: 2014

Background: Between-method equivalence ideally is achieved by calibration against an SI-traceable reference measurement procedure. For measurement of thyroid stimulating hormone (TSH), it is unlikely to accomplish this goal in mid-term. Therefore, we investigated a statistical alternative based on a factor analysis (FA) model. Methods: The FA model was applied to TSH results for 94 samples generated by 14 immunoassays (concentration range: 0.0005-78 mIU/L). The dataset did not fulfill the assumption of a homogeneous sample from an elliptically symmetric distribution, and, therefore, required standardization prior to application of the FA model. As outliers and missing values also occurred, the key quantities of the FA model had to be estimated with a method that can handle these complications. We selected a robust alternating regressions (RAR) method, which replaces in the minimization criterion of the fitting process the squared differences between results xy and model fit x̂y by a weighted absolute difference. The weights are adaptively determined in successive regressions, which down weighs the outliers. The weights for missing values are set to zero. Results: The quality of the estimated targets was reflected by their central position in the distributions, and description of the relationship between results and targets by a simple two-parameter regression equation with high correlation coefficients and low SDs of the percentage-residuals. Mathematical recalibration eliminated the method differences and improved the between-method CV from 11% to 6%. Conclusions: RAR applied to a multimethod comparison dataset hampered by outliers and missing values, is fit to the purpose of harmonization.


Stepman H.,Ghent University | Stockl D.,State Consulting
Klinicka Biochemie a Metabolismus | Year: 2012

In very basic terms, statistical power is the likelihood of achieving statistical significance. Three factors (effect-size, α, n), together with power, form a closed system - once any three are established, the fourth is completely determined. The goal of a power analysis is to find an appropriate balance among these factors by taking into account the substantive goals of a study. We exemplify the role of effect-size, α, and n on the power of a 1-sided F-test and give a general illustration of the power concept.


Stepman H.,Ghent University | Stockl D.,State Consulting
Klinicka Biochemie a Metabolismus | Year: 2012

This is the first article in a series of small, "loose" contributions to this journal. The articles touch, what we think, is important about analytical quality in the medical laboratory The articles will address topics such as metrology (philosophy of the measurand), statistics, internal quality control (IQC), external quality assessment (EQA), critiques to the discipline. All contributions follow the ASAP-concept, meaning As Simple As Possible. Here, we explore the "ASAP" concept for analytical quality specifications derived from biological variation. The application of the concept results for the great majority of the mainstream serum-, plasma-, and blood-analytes in CV a values within boundaries of 0.4 - 15 % and for Delta-SE within boundaries of 0.2 - 10 %. The take-home message is: analytical quality in the medical laboratory must NOT be viewed in absolute terms ("a CV a of 2 % is good"), BUT in relative terms. A CV a of 2 % is good for S-cholesterol analysis, but NOT for S-Na analysis!


De Grande L.A.C.,Ghent University | Goossens K.,Ghent University | Van Uytfanghe K.,Ghent University | Stockl D.,State Consulting | Thienpont L.M.,Ghent University
Clinical Chemistry and Laboratory Medicine | Year: 2015

Background: Manufacturers and laboratories might benefit from using a modern integrated tool for quality management/assurance. The tool should not be confounded by commutability issues and focus on the intrinsic analytical quality and comparability of assays as performed in routine laboratories. In addition, it should enable monitoring of long-term stability of performance, with the possibility to quasi "real-time" remedial action. Therefore, we developed the "Empower" project. Methods: The project comprises four pillars: (i) master comparisons with panels of frozen single-donation samples, (ii) monitoring of patient percentiles and (iii) internal quality control data, and (iv) conceptual and statistical education about analytical quality. In the pillars described here (i and ii), state-of-the-art as well as biologically derived specifications are used. Results: In the 2014 master comparisons survey, 125 laboratories forming 8 peer groups participated. It showed not only good intrinsic analytical quality of assays but also assay biases/non-comparability. Although laboratory performance was mostly satisfactory, sometimes huge between-laboratory differences were observed. In patient percentile monitoring, currently, 100 laboratories participate with 182 devices. Particularly, laboratories with a high daily throughput and low patient population variation show a stable moving median in time with good between-instrument concordance. Shifts/drifts due to lot changes are sometimes revealed. There is evidence that outpatient medians mirror the calibration set-points shown in the master comparisons. Conclusions: The Empower project gives manufacturers and laboratories a realistic view on assay quality/comparability as well as stability of performance and/or the reasons for increased variation. Therefore, it is a modern tool for quality management/assurance toward improved patient care. © 2015 by De Gruyter 2015.

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