Clinica Chimica Acta 440 (2015) 55–56

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Clinica Chimica Acta

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Letter to the Editor

Referencemeasurement procedure correctedall method trimmedmean — The best of

two worlds

Keywords:Data dispersionMethod comparisonTraceability

Reference measurement procedures (RMPs) are a vital part of themetrological traceability chain [1]. They have one essential feature:they can break the commutability barrier for trueness transfer from ar-tificial calibration solutions (e.g., ethanolic standards) to the matrix ofpatient samples. RMPs often apply instrumental analytical procedures(e.g., mass spectrometry), require extensive sample clean-up, and in-volve manual steps (e.g., RMPs for enzymes) [2]. These features maymake them vulnerable to increased measurement imprecision, low-throughput and high measurement costs. Particularly the latter is pro-hibitive for RMPs performing a significant number of replicatemeasure-ments to reduce the analytical random error component. In contrast,routine procedures generally are characterized by very low within-runmeasurement imprecision in the order of 1–2% and performance at arelatively low cost, which favors a high number of measurements.From this perspective, method comparison studies between a RMPand several routine procedures have the potential of combining “thebest of two worlds”, i.e., the high trueness provided by the RMP andthe low dispersion of the all method trimmed mean (AMTM) inferredfrom the results by the routine procedures. This can be accomplishedby correcting the bias of the method comparison AMTM on the basisof its relationship to the RMP, to result in the so-called RMP-correctedAMTM. This requires that first linear regression is performed betweentheRMP andAMTMmeasurement results for the samples of themethodcomparison study. Then the regression equation is used to calculate theRMP-corrected AMTM values. This approach requires that the AMTM issufficiently reliable and has a reduced random error component. Thelatter is accomplished if the AMTM is calculated from the results of a suf-ficient number of assays, i.e., 6 to 8. It can indeed be assumed that underthese conditions the random sample-related effects in the measure-ment results are canceled out.

Here, we investigated the potential of using the above describedRMP-corrected AMTM for the assessment of imprecision and randomsample-related effects of individual routine procedures. The data weused are from a method comparison study using 20 single donationsamples from apparently healthy volunteers. The samples were mea-sured for γ-glutamyltransferase (GGT) in singlet on the platforms of 8manufacturers by a representative number of laboratories and theIFCC RMPwas performed in triplicate [2]. The platformswere theAbbott

Abbreviations: AMTM, all method trimmed mean, GGT, γ-glutamyltransferase, RMP,reference measurement procedure.

http://dx.doi.org/10.1016/j.cca.2014.11.0080009-8981/© 2014 Elsevier B.V. All rights reserved.

Architect (used by 21 laboratories), Beckman AU (n = 19), BeckmanDxC (n = 11), Ortho Vitros (n = 19), Roche Cobas (n = 26), RocheModular (n = 9), Siemens Advia (n = 11) and Siemens Vista (n =8). The AMTM for each sample was calculated as mean of the 8 peergroup means, after investigation for outlying assays [3]. Performing aGrubbs test did not identify any outlying assay. Also no single outlyingvalues (z-value N 4) were identified, which means that trimming inthis specific case was not necessary. Linear regression of the AMTMand the RMP values gave the following equation (including the 95% CIof slope and intercept): AMTM = 0.924 (±0.045) RMP − 0.816(±1.25) (R2 = 0.99), hence the RMP-corrected AMTM was obtainedas (AMTM + 0.816) / 0.924. We want to clarify that, because the databelow are a comparison between RMP and RMP-corrected AMTM, theabsolute values for both are practically the same.

Fig. 1 shows the %-difference plots of the GGT results obtained by 2routine procedures installed on 2 differentmodern platforms comparedto the RMP (left) and the RMP-corrected AMTM values (right) as target.Using the magnitude of the R2-values as a criterion, the relationship ofthe procedures' differences to the respective targets was best describedby a logarithmic equation. However, the logarithmic relation was onlyused for the purpose of fitting, and should not be interpreted as a causalconnection between the two methods. Data distribution and R2-valuesindicate the superiority of using RMP-corrected AMTM values astarget over the RMP-values as such. Indeed, from visual inspection ofthe %-difference plots the decrease of the scatter around the regressionline is obviouswhen comparing the left and right part of the figure. Alsothe increase of the R2-values is spectacular, i.e., from0.17 to 0.73 for oneplatform and from 0.34 to 0.94 for the other. The dispersion of the datafor regression to the RMP values (left) can mainly be attributed to theuncertainty of the RMP (in the order of 2–10%). In consequence, underthese circumstances, random error effects specifically related to routineprocedure (from high analytical imprecision and/or random sample-related effects) cannot be uncovered due to the overriding imprecisioncomponent of the RMP. In contrast, they can, if the RMP-correctedAMTM values are used for comparison. The %-difference plots againstthe RMP-corrected AMTM values (Fig. 1, right) suggest the absence ofsample-related effects for the GGT measurements on the shown plat-forms (note, their within-run CV was ~1%). While the bias informationis not relevant for the assessment of combined random error effects, it isconserved in the RMP-corrected AMTM approach because of the highquality of the regression equation relating the AMTM and the RMPvalues (R2 = 0.99).

In conclusion, we demonstrated the superiority of using the RMP-corrected AMTM over RMP values as such when investigating causesof dispersion of results from routine procedures (imprecision and/orrandom sample-related effects) in a method comparison study. It isalso an interesting approach in commutability studies in which one ofthe procedures is a RMP. These studies indeed may be limited by ahigh scatter of the results for the patient samples around the regressionline due to the potentially highermeasurement imprecision of the RMP,if not performed with sufficient replication because of too high costs.

Fig. 1.Demonstration of the effect of using RMP and RMP-corrected AMTM values on data dispersion around regression lines (logarithmic relationship); %-difference plots of results for γ-glutamyltransferase (GGT) by 2 different routine procedures compared to RMP values as such (left part) and RMP-corrected AMTMvalues (right part). The squared symbols and trianglesused in the plots represent the differences of the respective routine procedures.

56 Letter to the Editor

Acknowledgments

The authors are grateful for the helpful advice given by D. Stöckl(STT-Consulting) and for the RMP values provided by D. Ducrocq andA. Thomas (WEQAS Quality Laboratory).

References

[1] Vesper HW, Thienpont LM. Traceability in laboratory medicine. Clin Chem 2009;55:1067–75.

[2] Schumann G, Bonora R, Férard G, et al. IFCC primary reference procedures for themeasurement of catalytic activity concentrations of enzymes at 37 °C. Clin ChemLab Med 2002;40:734–8.

[3] Stepman HC, Tiikkainen U, Stöckl D, et al. Measurements for 8 common analytes innative sera identify inadequate standardization among 6 routine laboratory assays.Clin Chem 2014;60:855–63.

Kenneth GoossensLinda M. Thienpont⁎

Laboratory for Analytical Chemistry, Faculty of Pharmaceutical Sciences,Ghent University, Ghent, Belgium

⁎Corresponding author at: Laboratory for Analytical Chemistry, Facultyof Pharmaceutical Sciences, Ghent University, Ottergemsesteenweg

460, B-9000 Ghent, Belgium. Tel.: +32 9 264 81 04;fax: +32 9 264 81 98.

E-mail address: linda.thienpont@ugent.be (L. M. Thienpont).

27 October 2014Available online 13 November 2014