Linde A.C. De Grande

The Empower project – a new way of assessing and monitoring test comparability and stability

Abstract 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.

A Fresh Look at the Relationship between TSH and Free Thyroxine in Cross-Sectional Data

Dear Editor, The classical log-linear relationship between thyroid-stimulating hormone (TSH) and free thyroxine (FT4) generally reflects on the reasonable correlation between both hormones, while particularly emphasizing that small changes in FT4 are accompanied by larger changes in TSH [1]. Recently, refining this relationship in cross-sectional data has received new interest [2,3,4]. Different from the classical monotonous relationship over the whole thyroid function range, the referred authors propose three different relationships in the hypo-, eu-, and hyperthyroid range. While they use different mathematical models to describe the relationships, they have in common that they work with functions that interconnect at the transition from the hypo-/euthyroid and eu-/hyperthyroid states. Although the real clinical relevance of knowing the exact mathematical relationship may be debated, it cannot be denied that the relation between TSH and FT4 is discussed in view of more precisely defining the subclinical state of thyroid dysfunction, and/or even brought up by some scientists as evaluation criterion for the validity of a FT4 assay [2,5]. Here we take a fresh look at the TSH/FT4 relationship, based on previously described data [1]. They were from 8,152 unselected patients (median age: 61 years, range: 18-100) from the Department of Endocrinology or Nuclear Medicine of the Klinikum Ludenscheid in Germany, who presented with various thyroid disorders. Data from pregnant women and patients with pituitary or hypothalamic disorders were excluded, as well as from patients with conditions that potentially interfere with thyroid testing. Note that corresponding to the limits of the Abbott enzyme immunoassay, which was used for measuring both TSH and FT4, the TSH data are truncated at 0.001 and 100 mIU/l, respectively. Regression and correlation results were calculated by Microsoft Excel 2010. Different from current practice, we plotted log TSH on the x-axis and FT4 on the y-axis (fig. ​(fig.1).1). We did not define upfront the different concentration categories because we preferred to inspect the correlation of the complete dataset without any prejudice. Visual inspection of the plot reveals three clusters of TSH values from 0.001 to <0.045 mIU/l (‘low’), 0.045 to <23 mIU/l (‘mid’), and 23 to 100 mIU/l (‘high’). Instead of a continuum, a clear break in the TSH/FT4 relationship for the high-TSH data group can be seen. This is also reflected in the partial regression equations (y = −2.50x + 14.9, ‘low’; y = −2.60x + 14.1, ‘mid’; y = −1.11x + 6.5, ‘high’). The regression equation over the whole range (y = −3.04x + 13.9, ‘all’) is similar to the low- and mid-range data. The correlation data, in our opinion, are misleading and are mainly influenced by the TSH range (note: correlation over the whole range is r = −0.577, while it is r = −0.044 in the high range). Although the precision and accuracy of the FT4 measurement results in the low concentration range might be jeopardized by the limit of quantitation of the used assay, we consider that the effect of increased random error would result in a higher scatter around the line representing the TSH/FT4 relationship, though without affecting the regression coefficients. Regarding the inaccuracy of measurement, we know from a previous study that most FT4 assays tend to have a positive calibration bias in the low concentration range versus a significantly negative one in the mid- to high-concentration range [6]. This allows us to infer that after correction of this bias, the break in the TSH/FT4 relationship would become even more obvious. Fig. 1 log TSH is plotted on the x-axis and FT4 on the y-axis. This reveals three clusters depending on the TSH-concentration: ◻: ‘low’, –: ‘mid’, and Δ: ‘high’. The blue line is the linear ... Our ‘fresh’ presentation of TSH/FT4 data suggests that the log-linear relationship between both hormones holds also in cross-sectional data up to TSH concentrations of ∼23 mIU/l (FT4 of ∼10 pmol/l; note: the actual values will depend on the assays used). However, from this concentration on, there is a clear ‘break’ in the relationship with little correlation between TSH and FT4. Consequently, curve fittings for the TSH/FT4 relationship in both ranges should not be interconnected.

Standardize the serum albumin assay now: calibrate it to 60% of the serum total protein assay

*Corresponding author: Linda M. Thienpont , Laboratory for Analytical Chemistry, Faculty of Pharmaceutical Sciences, Ghent University, Harelbekestraat 72, 9000 Ghent, Belgium, Phone: + 32 9 264 8104, Fax: + 32 9 264 8198, E-mail: linda.thienpont@ugent.be Hedwig C.M. Stepman and Linde A.C. De Grande: Laboratory for Analytical Chemistry, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium Dietmar St ö ckl: STT-Consulting, Horebeke, Belgium

Using "big data" to describe the effect of seasonal variation in thyroid-stimulating hormone.

aCurrent affiliation: Thienpont & Stöckl Wissenschaftliches Consulting GbR, 86643 Rennertshofen (OT Bertoldsheim), Germany. *Corresponding author: Linda M. Thienpont, Department of Pharmaceutical Analysis, Faculty of Pharmaceutical Sciences, Ghent University, Ottergemsesteenweg 460, 9000 Ghent, Belgium, Phone: +49 8434 94 365 22, E-mail: linda.thienpont@ugent.be Linde A.C. De Grande and Kenneth Goossens: Department of Pharmaceutical Analysis, Faculty of Pharmaceutical Sciences, Ghent University, Ottergemsesteenweg 460, 9000 Ghent, Belgium Katleen Van Uytfanghe: Ref4U, Laboratory of Toxicology, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium Ian Halsall: The Pathology Partnership, Cambridge University Hospitals, Cambridge, UK Jaeduk Yoshimura Noh: Ito Hospital, Tokyo, Japan Koen Hens: Algemeen Medisch Laboratorium (AML), Antwerpen, Belgium Letter to the Editor

Value Assignment of Vitamin D Metabolites in Vitamin D Standardization Program (VDSP) Serum Samples | NIST

Assay variability has been cited as an obstacle to establishing optimal vitamin D exposure. As part of the Vitamin D Standardization Program (VDSP) effort to standardize the measurement of total 25-hydroxyvitamin D [25(OH)D], the value assignment of total 25(OH)D in 50 single-donor serum samples was performed using two isotope-dilution LC with tandem MS methods. Both methods are recognized as reference measurement procedures (RMPs) by the Joint Committee for Traceability in Laboratory Medicine. These samples and their assigned values serve as the foundation for several aspects of the VDSP. To our knowledge, this is the first time that two RMPs have been used to assign 25(OH)D values to such a large number of serum samples.