Uncertainty in measurement for 43 biochemistry ...static.probioqual.com/pdf/Doc/article LTCV...

Clin Chem Lab Med 2015; aop

Gladys Matar * , Bernard Poggi , Roland Meley , Chantal Bon , Laurence Chardon , Karim Chikh , Anne-Claude Renard , Catherine Sotta , Jean-Christophe Eynard , Regine Cartier and Richard Cohen

Uncertainty in measurement for 43 biochemistry, immunoassay, and hemostasis routine analytes evaluated by a method using only external quality assessment data

DOI 10.1515/cclm-2014-0942 Received September 24 , 2014 ; accepted February 19 , 2015

Abstract

Background: International organizations require from medical laboratories a quantitative statement of the uncertainty in measurement (UM) to help interpret patient results. The French accreditation body (COFRAC) recom-mends an approach (SH GTA 14 IQC/EQA method) using both internal quality control (IQC) and external quality assessment (EQA) data. The aim of this work was to vali-date an alternative way to quantify UM using only EQA results without any need for IQC data. This simple and practical method, which has already been described as the long-term evaluation of the UM (LTUM), is based on

linear regression between data obtained by participants in EQA schemes and target values. We used it for 43 routine analytes covering biochemistry, immunoassay, and hemo-stasis fields. Methods: Data from 50 laboratories participating in Pro-BioQual (PBQ) EQA schemes over 25months were used to obtain estimates of the median and 90th percentile LTUM and to compare them to the usual analytical goals. Then, the two UM estimation methods were compared using data from 20 laboratories participating in both IQC and EQA schemes. Results: Median LTUMs ranged from 2.9% (sodium) to 16.3% (bicarbonates) for biochemistry analytes, from 12.6% (prothrombin time) to 18.4% (factor V) for hemosta-sis analytes when using the mean of all participants, and were around 10% for immunoassays when using the peer-group mean. Median LTUMs were, in most cases, slightly lower than those obtained with the SH GTA 14 method, whatever the concentration level. Conclusions : LTUM is a simple and convenient method that gives UM estimates that are reliable and comparable to those of recommended methods. Therefore, proficiency testing (PT) organizers are allowed to provide participants with an additional UM estimate using only EQA data and which could be updated at the end of each survey.

Keywords: external quality assessment; internal quality control; long-term analytical coefficient of variation; pro-ficiency testing; uncertainty in measurement.

List of abbreviations: AB, accuracy bias; AFP, -fetoprotein; AL, acceptable limit; ALAT, alanine ami-notransferase; ALP, alkaline phosphatase; ANOVA, analy-sis of variance; AP, all participant results; aPTT, activated partial thromboplastin time; ASAT, aspartate aminotrans-ferase; CB, constant bias; CEA, carcinoembryonic antigen; CK, creatine kinase; CV WL , within-laboratory coefficient

*Corresponding author: Gladys Matar, ProBioQual, 9 rue Professeur Florence 69003, Lyon, France, E-mail: [email protected] Bernard Poggi and Chantal Bon: Service de Biochimie et Biologie mol culaire, H pital de la Croix-Rousse, Hospices Civils de Lyon, Lyon, France ; and ProBioQual, Lyon, France Roland Meley: Service d H matologie, St Etienne, France ; and ProBioQual, Lyon, France Laurence Chardon: Service de Biochimie et Biologie mol culaire, H pital Edouard Herriot, Hospices Civils de Lyon, Lyon, France ; and ProBioQual, Lyon, France Karim Chikh: Service de Biochimie et Biologie mol culaire, Centre Hospitalier Lyon Sud, Hospices Civils de Lyon, Pierre-B nite, France ; and ProBioQual, Lyon, France Anne-Claude Renard, Catherine Sotta and Jean-Christophe Eynard: ProBioQual, Lyon, France Regine Cartier: Service de Biochimie et Biologie mol culaire, Groupement Hospitalier Est, Hospices Civils de Lyon, Bron, France ; and ProBioQual, Lyon, France Richard Cohen: Service de Biochimie et Biologie mol culaire, H pital Edouard Herriot, Hospices Civils de Lyon, Lyon, France ; Universit Claude Bernard Lyon-1, ISPB Facult de pharmacie, MATEIS UMR CNRS 5510, France ; and ProBioQual, Lyon, France

Authenticated | [email protected] author's copyDownload Date | 3/27/15 10:16 AM

2Matar etal.: Uncertainty in measurement estimates from external quality assessment

of variation; COFRAC, French accreditation body; DBMS, database management system; EQA, external quality assessment; EUROLAB, European Federation of National Associations of Measurement, Testing, and Analyti-cal Laboratories; FSH, follicle stimulating hormone; FT3, free triiodothyronine; FT4, free thyroxine; GGT, -glutamyltransferase; GUM, guide to the expression of uncertainty in measurement; IFCC, International Federa-tion of Clinical Chemistry and Laboratory Medicine; ILAC, International Laboratory Accreditation Cooperation; IOLM, International Organization of Legal Metrology; IQC, internal quality control; ISO, International Organization for Standardization; hCG, human chorionic gonadotropin; LCV, long-term analytical coefficient of variation; LDH, lactate dehydrogenase; LH, luteinizing hormone; LTB, long-term bias; LTUM, long-term uncertainty in measure-ment; PB, proportional bias; PBQ, ProBioQual; PG, peer group; ProT, prothrombin time; PSA, prostate-specific antigen; PT, proficiency testing; RE, random error; SE, sys-tematic error; SH GTA 14, COFRAC Accreditation Technical Guide for UM; s WL , within-laboratory standard deviation; TE, total error; TSH, thyroid stimulating hormone; um, standard uncertainty in measurement; UM, uncertainty in measurement (or expanded uncertainty in measurement for a 95% level of confidence).

Introduction The National Institute of Standards and Technology speci-fies that: a measurement result is complete only when accompanied by a quantitative statement of its uncertainty. The uncertainty is required in order to decide if the result is adequate for its intended purpose and to ascertain if it is consistent with other similar results [1] . In clinical chemis-try, this is particularly important to ensure that test results are fit for their clinical purpose and do not compromise patient care. The uncertainty in measurement (UM) could, thereby, be used to assess the quantitative performances of analytical procedures [2] . Thus, medical laboratories are expected to estimate the uncertainty for test measure-ments, especially since many international organizations, such as International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) [3] , International Labora-tory Accreditation Cooperation (ILAC) [4] , International Organization of Legal Metrology (IOLM) [5] and Interna-tional Organization for Standardization (ISO) [6, 7] [ISO 17025 (2005) and ISO 15189 (2012)], required it. According to the ISO 15189 (2012) 5.5.1.4 standard: The laboratory shall determine the uncertainty of measurement for each

procedure in the analytical phase used to record the meas-ured values of patient samples .

In the aim to provide terminology and methodology for expressing UM, a joint working group consisting of experts from these and other organizations published a Guide to the expression of uncertainty in measurement (GUM) [8] , which establishes the general rules for evaluating and expressing UM. The GUM presents different approaches to estimate UM using a mathematical theory and experimental observation, so-called modeling approaches . These methods were compared to alternative empirical approaches in the tech-nical report published by European Federation of National Associations of Measurement, Testing, and Analytical Lab-oratories (EUROLAB) [9] , which classifies these different approaches in two categories: the intra- and inter-laboratory approach. In the first category, estimation of the UM is based on the GUM modeling approaches or on individual data combinations available from reference methods and inter-nal quality control (IQC), providing an estimation of repeat-ability, within-laboratory reproducibility, and bias. In the second category, the UM estimation is based on validation method or proficiency testing (PT) data from the labora-tory s results obtained during participation in an external quality assessment (EQA).

According to the empirical approaches , the UM is estimated from the imprecision and the bias. The impre-cision, in general, is quantified by the within-laboratory reproducibility standard deviation (s WL ), or coefficient of variation (CV WL ) obtained under within-laboratory repro-ducibility conditions (often called intermediate condi-tions ): different operators, reagent batches, calibrators, or long-time repetition. The bias is quantified by the devi-ations of measurement results from corresponding refer-ence values. Thus, a combination of the intralaboratory and the interlaboratory approaches is possible using IQC data to estimate the imprecision (s WL ), and the EQA labora-tory s results to estimate the bias. This combined method was suggested in the EUROLAB Technical report, and recommended in the SH GTA 14 [10] , the technical guide for the UM estimation, published by COFRAC (the French accreditation body) in 2011.

As the UM is used to assess the performances of quan-titative analytical procedures, Meijer etal. [11] proposed to express the UM as the long-term analytical performance (LTUM) of a laboratory using only its EQA results over a long period of time. The imprecision is then expressed by the long-term analytical CV (LCV) and the deviation of the laboratory s result from the measurand assigned value is determined by the long-term bias (LTB).

In order to provide participants with an estimate of the UM by a different method and to allow the PT organizer to


Matar etal.: Uncertainty in measurement estimates from external quality assessment3

include UM estimation in the reports established at the end of each survey, we quantified the LTUM of a large panel of routine analytes assayed in a medical laboratory. We used data of 50 laboratories participating in ProBioQual (PBQ) EQA programs in biochemistry, immunoassay, and hemo-stasis over a long period of time (25 months). Moreover, data from 20 laboratories participating in both IQC and EQA schemes were used to compare the LTUM and the SH GTA 14 (IQC/EQA) UM estimation methods and to point out the advantages and the drawbacks of each approach.

Materials and methods ProBioQual

PBQ (http://www.probioqual.com) is a French non-profi t, non-gov-ernmental association established in 1972 by biologists to promote training and quality control in medical laboratories. As a PT provider, PBQ proposes six IQC and 26 EQAs in biochemistry, immunology, hematology, serology, and drug tests, including over 200 diff erent analytes. Thus, almost 1700 international medical laboratories par-ticipate in at least one of these programs, allowing an assessment of their analytical procedures.

Survey specimens and laboratories

Laboratories received, per year, human sera lyophilized specimens (24 for biochemistry, or 12 for immunoassay), or 16 human citrated plasmas lyophilized specimens for hemostasis EQA schemes. Intra- and inter-vials homogeneity tests results showed that lyophilization procedure was successfully applied but the recovery of this procedure was not tested. All survey samples were shipped at room temperature and then stored in the laboratories at 2 C/8 C until reconstitution. Simulation tests on the survey specimens have proved their stability during all the shipment process. According to an established sched-ule, the participants processed each specimen as if it was a patient sample, using routine methods to determine the concentration of each measurand. The main commercial methods, reagents and devices used by the laboratories to assess the EQA schemes samples are listed in the Table 1 . Aft er results submission on the PBQ website, under anonymous laboratory code number, statistical evaluation was performed according to the ISO guideline 13528 (2005) [12] , as it is recommended in the PT-specifi c standard ISO 17043 (2010) [13] . The ISO 13528 (2005) robust algorithm A was applied to calculate the mean of submitted results, which was used as the consensus value. No further statistical analysis of outliers was performed. The iterative process was terminated when the mean and CV estimates were sta-ble and the percentage of values modifi ed then did not exceed 15%. Thereby, two consensus values were determined: the fi rst one was the mean of all participants results (AP) and the second was that of laboratories using the same assay type of method, the same reagents and the same device, forming a peer group (PG). A minimum of six participants was required to apply the robust algorithm. The labora-tories received a PT report including the results of all participants,

with an individual evaluation of the laboratory performance. PBQ provided advice on the interpretation of the statistical analysis and comments on participants performance.

Each laboratory participating to PBQ EQA Schemes had an anonymous code number. The laboratories participating to this study were selected randomly from the beginning, the middle, and the end of our anonymous code number list. The new ones had the latest numbers and the older had the fi rst numbers. Like that, the selected laboratories represented experienced and non-experienced labora-tory in our EQA schemes. Fift y laboratories participating in PBQ bio-chemistry (915 participants in 2013), immunoassay (892 participants in 2013) or hemostasis (848 participants in 2013) EQA schemes over 25months (January 2011 until January 2013) were selected to test the LTUM method estimation. This prolonged period of time was suffi -cient to take into account a number of PT surveys covering a wide concentration range of measurands, hence allowing a real long-term analytical performance evaluation. Since these programs did not have the same survey frequency (weekly, monthly, or 2-monthly), between eight and 45 results were included in the evaluation of the LTUM for each laboratory. For the comparison between LTUM and SH GTA 14 (IQC/EQA) methods, data from 20 laboratories participating in both IQC (153 and 170 participants in biochemistry and hemostasis 2013 IQC schemes, respectively) and EQA programs, were used over the same period of time.

LTUM method

The LTUM method used the linear regression model (y = bx + a), as described by Meijer etal. [11] , to express the laboratory value (y) as a function of the target value (x), where b is the slope and a is the intercept point ( Figure 1 ). The total error (TE) is presented in Figure 1 as the diff erence between each data point (the laboratory value) and the identity line y = x (the target value). Thus, it could be assimilated to the accuracy bias (AB) or to the UM. The TE is composed of a random error (RE) and a systematic error (SE), itself composed of a constant (CB) and a proportional biases (PB). The RE corresponds to the disper-sion of the data points around the regression line caused by routine operating conditions variability (as multiple calibrators and reagent batches, multiple operators, equipment maintenance, etc.) over the EQA period of time, and is quantifi ed by the LCV. SE is represented by the deviation of the regression line from the identity line and cor-responds to the LTB. The CB (vertical distance between parallel and identity lines on Figure 1) is equal to the intercept a of the regression line at every level within the concentration range. It could be due to the so-called matrix-eff ect . The PB is represented, at each concen-tration level, by the diff erence between the regression and parallel lines. Its intensity increases with concentration level and could be due to calibration errors. The LCV and the LTB represent the two compo-nents of the UM, which could be determined as follows [11] :

2 2UM 1.96 um 1.96 LCV LTB= = + (1)

y/xsLCVx

=

(2)

2 2 2x

n 1 (b 1) s ( y x )nLTB

x

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(3)



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combined data from IQC and calibration uncertainty. However, for convenient use in medical laboratories, the COFRAC recommends the third one which was well described by Fisicaro etal. [14, 15] . Briefl y, the in-house validation method or IQC data were used to determine imprecision, and the EQA laboratory data were used to calculate bias. Calculation could be made either using concentration values (s WL and bias expressed as concentration units) or using relative values (CV WL and bias expressed as a percentage of the assigned value) for the ran-dom and the systematic component of error, respectively. Here, both UM terms were expressed as percentages. Applying the uncertainty propagation law, an estimation of UM was given by the following equations:

2 2UM 1.96 u 1.96 u(IQC) u(EQA)= = + (5)

WLsu(IQC) CV 100m

= =

(6)

22E

| E |u(EQA) s3

= +

(7)

where CV = coeffi cient of variation, s WL = within- laboratory standard deviation, m = IQC laboratory mean,

= = = 2i ii i

E(%) ii

E (%) (E (%) E(%)) y xE(%) , S , E (%) 100n n 1 x

and n = number of laboratory EQA results. As required, UM should be determined at two concentration

levels at least corresponding to the critical clinical limits [16] . These limits were chosen as specifi ed by Vassault etal. [17] for biochemistry analytes and normal and pathologic levels were determined by PBQ for hemostasis analytes. Only the EQA control samples correspond-ing to the same concentration range as IQC samples were selected among those used during the 25-month surveys.

Finally, the median UMs, calculated from 20 laboratories either by the SH GTA 14 method or by the LTUM approach, were compared at each of the two concentration levels.

Results

Biochemistry EQA schemes

The results of 50 laboratories participating in the PBQ biochemistry EQA program were used to quantify LTUM for 23 routine analytes including ions, substrates, and enzymatic parameters, according to Equations 1 4. Two different target values were considered for either that of AP ( Figure2 A), or that of the PG (Figure 2B), except for the enzymatic analytes, for which only the PG target values were taken into account because of the high dispersion between AP results. To evaluate the performance of 50% and 90% of the laboratories, median and 90th percentile LTUM were calculated and compared (Figure 2) to two analytical goals commonly used by laboratories for the

2

y/x 21s (y y)

(n 2 )(x x)(y y)

(x x)

=

(4)

where um = standard uncertainty in measurement, UM = expanded uncertainty in measurement for a 95% level of confi dence (k = 1.96 is the coverage factor), x = consensus value; x = mean value for x; y = laboratory value; y = mean value for y; s x = standard error of x; b = slope; s y/x = variability of the regression line, based on the least-squares method and n = number of laboratory EQA results.

For each of the 50 laboratories chosen as described above, LTUM was evaluated using diff erent consensus values obtained from the AP or PG groups. Calculation of specimen number n, LCV, LTB, um, and expanded UM were realized by the database management sys-tem (DBMS) generated by the 4D soft ware (V. 2012). For each survey, any result deviating from the assigned value by > 3 standard devia-tions of the comparison group ( | z | > 3) was identifi ed as an outlier and discarded. This method is only valid for a normal distribution and the number of results removed was < 3. As expected, the hypoth-esis that data came from a normal distribution, appreciated by the 2 goodness of fi t and Kolmogorov-Smirnov tests, was not rejected when considering PG data and rejected when considering AP data for most analytes. The LTUM evaluation was performed only when n 8 results were available from a laboratory, except when EQA sam-ples had to be split in two concentration levels for the comparison study between the LTUM and SH GTA 14 methods (n 6). The reduc-tion in the minimum data number for linear regression analysis did not aff ect signifi cantly either the LTUM estimates or the conclusion of this part of our study.

SH GTA 14 (IQC/EQA) method

The SH GTA 14 suggests four diff erent methods for the UM assess-ment based on the GUM Modeling approach [8] , on data from method validation, on combined data from IQC and EQA, and on

Regression line y = bx+ a

Identity line y=x

Consensus value x

a

EQA laboratory results y

Parallel line

RE

TESE

PB

CB

Figure 1: Linear regression model for the assessment of UM: y = bx + a. CB, constant bias; PB, proportional bias; RE, random error; SE, systematic error; TE, total error.



validation method purpose. One was the expanded AB defined for the medium concentration level by Vassault etal. [17] from the state-of-the-art deduced from the expe-rience of several French EQA organizers in 1999 and the second was the desirable TE derived by Ricos etal. (http://www.westgard.com/biodatabase1.htm) from biological variations database. Analytical goals derived from the state-of-the-art are less strict than those defined by biolog-ical variations except for analytes whose biological vari-ations are very high [bilirubin, iron, triglyceride, alanine

aminotransferase (ALAT), creatine kinase (CK), and lipase]. Median AP LTUM was comprised between 2.9% (sodium) and 16.3% (bicarbonates), while the median PG LTUM range was 2.4% (sodium) to 13.7% (bicarbonates). As expected, AP LTUM was higher than PG LTUM for the majority of analytes. For some analytes (cholesterol, iron, glucose, phosphate, potassium, sodium, and urea), close estimates of the median LTUM were obtained whatever the target value used. When the AP consensus mean was used (Figure 2A), 90% or more laboratories fulfilled both LTUM

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19.6 23.5 4.5 4.9 13.7 17.6 19.6 9.8 11.8 11.0 6.9 3.5 15.7 13.7 15.7 16.3 13.8 6.2 6.2 7.1 12.8 6.0 6.2 10.0 5.4 3.5 2.9 10.1 6.4 8.9 23.3 24.5 9.7 9.0 10.4 18.7 12.0 10.1 13.4 8.7 5.3 4.6 15.4 13.4 15.1 4.9 26.9 2.6 1.5 9.0 8.9 30.7 7.0 4.8 10.1 5.6 0.7 26.0 12.0 15.6

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19.6 23.5 4.5 4.9 13.7 17.6 19.6 9.8 11.8 11.0 6.9 3.5 15.7 13.7 15.7 17.6 17.6 17.6 17.6 17.6 17.6 17.6 17.6

13.7 7.0 4.1 3.1 5.0 7.1 4.9 5.3 6.6 5.1 3.0 2.4 5.8 4.7 7.2 6.4 5.5 6.9 8.0 8.3 8.4 8.4 7.8

20.8 12.0 5.7 5.6 6.6 11.1 10.3 7.3 10.1 8.2 4.6 3.6 8.6 5.9 12.5 11.7 9.1 10.8 11.8 14.1 15.2 13.3 15.1 4.9 26.9 2.6 1.5 9.0 8.9 30.7 7.0 4.8 10.1 5.6 0.7 26.0 12,0 15,6 27.5 14.6 16.7 30.3 22.1 11.4 37.9 12.0

0%

5%

10%

15%

20%

25%

30%

35%

40% B

0%

5%

10%

15%

20%

25%

30%

35%

40% VassaultVV AB Median LTUM LL X 90th percentile LTUM Ricos TE A

Figure 2: Biochemistry analytes LTUM evaluation. Median (50th percentile) and 90th percentile (cross point) LTUM of 50 laboratories evaluated from AP (A), or from PG (B) target values, are compared to the Vassault etal. [17] intermediate concentration level AB, and to Ricos desirable TE (January 2014) for 23 routine analytes.



were not calculated by ANOVA, the difference between the AP-CV and the median PG-CV reflected the extent of between PG-biases and provided a good idea of analyte standardization. Table 2 shows that the lowest differences were obtained for -fetoprotein (AFP), prostate-specific antigen (PSA), human chorionic gonadotropin (hCG), thyroid stimulating hormone (TSH) and free triiodothy-ronine (FT3) with the more satisfactory standardiza-tion status at this concentration level. The UM for these 15 routine immunoassay analytes was estimated by the LTUM method described above ( Figure 3 ). Only the PG target value was considered here, since standardization of immunoassays is not presently sufficiently advanced to allow the use of the AP consensus mean without greatly overestimating LTB and, hence, AP LTUM. Except for FT3 and free thyroxine (FT4), intra- and inter-individual varia-tions are high, leading to a desirable TE (Figure 3) between 21% [CA 15-3, follicle stimulating hormone (FSH)] and 46% (CA 19-9). The AB established from the state-of-the-art for these analytes was also very high, reaching values between 24% and 29%. The median PG LTUMs were around 10% and did not exceed 12.5%, widely below both state-of-the-art and biological variation analytical goals. This is confirmed by the LTUM 90th percentile showing that over 90% laboratories fulfilled analytical goals for the majority of analytes, except for FT3 and FT4, which are very finely regulated in vivo and for TSH.

Hemostasis EQA schemes

Five hemostasis analytes [antithrombin, factor V, fibrin-ogen, prothrombin time (ProT), and activated partial thromboplastin time (aPTT)] were selected to determine LTUM by using 50 laboratory results oncoming from their participation in the PBQ hemostasis EQA scheme. As pre-viously, biological variation analytical goals were found in the Ricos et al. database (http://www.westgard.com/biodatabase1.htm), except for factor V. Since the hemo-stasis analytical goals derived from the state-of-the-art are not presently defined, we decided to compare median LTUM estimates to the acceptable limits (AL) set at PBQ by expert biologists in charge of the hemostasis EQA scheme. Median LTUMs were between 12.6% (ProT) and 18.4% (factor V) when the AP consensus value was used ( Figure 4 A) and between 6.4% (aPTT) and 17.1% (factor V) when the PG consensus value was used (Figure 4B). Except for aPTT, the use of the PG consensus mean lowered slightly median LTUM. Apart from fibrinogen, both LTUM analytical goals were not met by the majority of the 50 laboratories when the AP consensus value was used.

analytical goals for total bilirubin, iron, phosphate, potas-sium, triglyceride, urate, and urea; for calcium and chlo-ride, both analytical goals were satisfied only by < 50% laboratories; for cholesterol, creatinine, glucose, and magnesium, the state-of-the-art LTUM analytical goal was fulfilled by about 90% laboratories, but < 50% laborato-ries reached the biological variation analytical goal; lastly for sodium, according to its very low biological variation, the state-of-the-art analytical goal was met by 50% labo-ratories and the biological variation analytical goal by far < 50% laboratories. When using PG consensus value (Figure 2B), even more laboratories reached the two ana-lytical goals since LTUM was lower for most analytes; for enzymes, almost all laboratories fulfilled the state-of-the-art analytical goal while the biological variation goal, was reached by > 90% laboratories for ALAT, amylase, aspar-tate aminotransferase (ASAT), CK, -glutamyltransferase (GGT), lipase and by 70% laboratories for lactate dehydro-genase (LDH) and alkaline phosphatase (ALP).

Immunoassay EQA schemes

A representative example of results obtained during one immunoassay EQA 2013 survey for 15 routine analytes, including tumor markers and hormones, is given Table2 . Although the between- and within-method variability

Table 2 : An example of results obtained during one immunoassay EQA survey (2013).

Analyte n n PG AP consensus mean

AP CV, %

MedianPG CV, %

AFP, kUI/L 640 13 70.5 9 5CEA, g/L 666 13 17.5 12 5CA 125, kU/L 607 13 44.2 12 5CA 15-3, kU/L 630 13 30.4 15 6CA 19-9, kU/L 634 13 34.3 21 7PSA, g/L 795 14 5.0 8 5Free PSA, g/L 616 15 0.54 17 6FSH, UI/L 667 12 26.7 12 4LH, UI/L 732 12 16.2 14 5hCG, UI/L 1081 17 31.1 9 6Prolactin, mUI/L 578 14 507 16 6TSH, mUI/L 918 14 4.56 8 4Free T3, pmol/L 734 12 5.19 11 7Free T4, pmol/L 828 12 17.0 11 6Estradiol, pmol/L 738 14 563 13 8

AP consensus mean and CV: mean and CV calculated from the n values using the ISO 13528 (2005) robust algorithm A. n, number of values; n PG, number of peer groups for each analyte; median PG CV, median of the n peer group CVs.



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4

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22.0 18.3 15.5 20.1 18.2 25.8 15.8 19.0 19.5 22.4 18.8 19.3 20.0 14.4 31.1 21.9 35.4 20.8 46.3 24.7 26.9 21.2 27.9 33.6 29.4 11.3 8.7 23.7

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50% assaultVV AB Median LTUM 90th percentile LTUM Ricos TE

Figure 3: Immunoassay analytes LTUM evaluation. Median (50th percentile) and 90th percentile (cross point) LTUM of 50 laboratories evaluated from the peer group (PG) target values are compared to the Vassault etal. [17] intermediate concentration level AB, and to Ricos desirable TE (January 2014) for 15 tumor markers and hormones.

Even when PG consensus value was used, the situation was improved only for aPTT and ProT LTUM compared to PBQ AL, but not with Ricos TE. For factor V, both AP LTUM (18.4%) and PG LTUM (17.1%) were higher than the only existing analytical goal (PBQ AL 12%). Figure 4 shows the relative importance of the two components (LCV and LTB) of AP and PG LTUM expressed as percentages. For aPTT, LTB decreased from 79% (AP) to 25% (PG), for ProT from 44% (AP) to 21% (PG), and for fibrinogen from 31% to 19% (Figure 4C, D). These three tests are chronometric, so the coagulation time depends on reagent types and detection methods, which could explain the LTB decrease when considering PG instead of AP consensus value. In con-trast, factor V and antithrombin AP and PG LTB percent-ages were almost the same (about 10%), in keeping with the closeness of AP and PG LTUM (Figure 4C, D). However, LCV percentages were very high for antithrombin and for factor V (about 90%), indicating the heterogeneity of within-laboratory assay conditions. Thus, the ratio LCV/LTB (with LTB calculated from AP consensus mean) could be considered as a measure of the relevance of applying AP or PG consensus value for bias estimation.

SH GTA 14/LTUM comparison

In order to compare the COFRAC recommended method (SH GTA 14 IQC/EQA) to the LTUM approach, data from 20 laboratories, participating to both PBQ IQC and EQA schemes in biochemistry and hemostasis, were used to estimate the UM of 19 routines analytes. For immunoas-says, comparison between the two UM method assess-ments was impossible because of insufficient IQC data. The within-laboratory CV (Equation 6) was estimated for each of 20 laboratories from 10 to 15 results sent monthly and cumulated over the 25-month period, at two concen-tration levels close to the upper and the lower limits of the reference range (biochemistry analytes) or corresponding to normal and pathological levels (hemostasis analytes). The SH GTA 14 UM and the LTUM were evaluated for each concentration level, using the AP or PG target value.

For biochemistry analytes, SH GTA 14 UM was higher or similar to LTUM ( Figure 5 ) whatever the consen-sus mean (AP or PG) used. A slight decrease of UM was observed when the PG consensus mean was used instead of the AP consensus mean.



For hemostasis analytes, AP SH GTA 14 UM was slightly higher than AP LTUM, except for aPTT, at a normal level whereas they were very similar at a pathological level ( Figure 6 ). When PG target value was considered instead of AP results, SH GTA 14 UM and LTUM decreased for all the analytes, particularly for aPTT, at both concentration levels.

Discussion The aim of this work was to validate a simple and practical method of calculating the UM applicable to medical labo-ratories using only EQA results without any need for IQC data. For this purpose, we estimated LTUM for 43 routine analytes using data from our EQA schemes and compared it to analytical goals commonly used in laboratory medi-cine (state-of-the-art and biological variations). This esti-mation was then evaluated by comparison with a method based on a combination of IQC and EQA results and rec-ommended by COFRAC: the SH GTA 14 approach.

The LTUM approach is based on linear regression between laboratory data and comparison group means. Furthermore, the regression line parameters (slope and intercept) are estimations of proportional and constant biases respectively, allowing the use of control samples at different concentration levels. The quality of these estimates is better as the number of degrees of freedom (number of EQA samples minus two) increases. Con-versely with the SH GTA 14 method, control samples must be sorted according to their concentration level in order to avoid UM overestimation [s E term in Equation 7 represent-ing standard deviation of bias] due to PB if concentration units are used for calculation, or to CB if relative values (percentages) are used. Here, we observed an overestima-tion of SH GTA 14 UM mainly at a low concentration level (data not shown) when s WL and bias (expressed in concen-tration units) were used compared to UM obtained when relative values (CV WL and bias expressed as a percentage of the target value) were applied. This observation sug-gests the preponderance of a PB component in the EQA data processed.

0%

5%

10%

15%

20%

B

0%

5%

10%

15%

20%Median LTUM PBQ AL Ricos TEA

0%10%20%30%40%50%60%70%80%90%

100%

Antithromb III Factor V Fibrinogen ProT aPTT

D

0%10%20%30%40%50%60%70%80%90%

100%


LTB LCV C


13.4% 18.4% 13.4% 12.6% 18.1%

12.0% 12.0% 12.0% 10.0% 10.0%

8.3% - 13.6% 5.3% 4.5%


12.8% 17.1% 11.1% 9.2% 6.4%

12.0% 12.0% 12.0% 10.0% 10.0%

8.3% - 13.6% 5.3% 4.5%

Figure 4: Hemostasis analytes LTUM. Median LTUMs of 50 laboratories evaluated from AP (A), or PG (B) consensus values are compared to PBQ AL and to Ricos desirable TE (January 2014). Contribution of the two LTUM components (LTB, LCV) for AP (C) and PG (D) results, expressed as percentages. ProT is expressed in % and aPTT in seconds.



0%

5%

10%

15%

20%

25%

PG LTUM low concentration level PG SH GTA 14 low concentration level

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25%

PG LTUM high concentration level PG SH GTA 14 high concentration level

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AP LTUM low concentration level AP SH GTA 14 low concentration level

A

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19.7 17.6 7.2 8.8 21.4 12.8 5.5 4.4 14.2 7.3

B'

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18.9 11.9 5.0 7.8 10.7 7.7 4.4 4.0 10.0 6.0 10.4 8.7 13.2 9.2

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14.0 12.5 3.9 5.9 9.8 8.0 3.7 2.6 6.8 4.2 8.7 9.3 14.6 10.0

18.8 15.6 5.5 7.9 13.6 11.0 4.5 3.9 12.9 6.1 13.9 10.2 16.6 10.1

A'

0%

5%

10%

15%

20%

25%

AP LTUM high concentration level AP SH GTA 14 high concentration level

B

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18.7 17.2 6.9 8.9 12.3 9.4 3.9 4.3 11.0 6.3

22.5 15.2 7.4 9.4 17.0 9.4 5.4 4.3 12.4 8.9

Figure 5: SH GTA 14/LTUM comparison. Median UMs of 20 laboratories for 14 routine biochemistry analytes determined by SH GTA 14 and LTUM methods at low (left) and high (right) concentration level, using AP (A and B) and PG (A and B ) results from their participation to PBQ IQC and EQA schemes.

Several assumptions are necessary for the validity of the LTUM estimation: linearity between EQA laboratory values and target values, equality of variance in the con-centration range explored (homoscedasticity) and com-mutability of the control samples.

The LTUM method has the advantage of fully taking into account the evolution of the bias as a function of con-centration. However, it provides a single estimate of the variability of the regression line (s y/x ) for the whole con-centration range explored by the control samples while the trend of standard deviation to vary with concentration is well known (increase for biochemistry and immunoas-say analytes and decrease for coagulation parameters). Therefore, the estimation of the variance obtained by this method could be reliable only for analytes whose clini-cally relevant concentration range is limited. Otherwise, for hormones such as hCG or TSH, it will be necessary to regroup control samples according to their concentration level as in the case of the SH GTA 14 method. As expected, our results (Figures 5 and 6) showed no significant differ-ence between LTUM at low and high concentration levels.

In addition, UM values obtained by the two methods tested are very similar indicating that heteroscedasticity is not a limiting factor when applying the LTUM method to biochemistry and hemostasis routine measurands.

Another important condition to demonstrate the potential of this UM estimate approach is the commut-ability of EQA samples with those of patients. Only in this case, the bias estimate will be reliable reflecting the discrepancies between measurement procedures and not the matrix related bias. Indeed, the most desirable PT/EQA programs are those that use commutable samples with target values assigned by a reference method (cat-egories 1 and 2 in reference [18] ). Although fresh samples of human origin are distributed in some of our programs, data used in this study came only from processed (mostly lyophilized and spiked or diluted to adjust the concentra-tion levels) and therefore likely non-commutable control materials. IQC samples, used for the SH GTA 14 method, must also be commutable to provide the best estimate of within laboratory precision. So in order to validate the estimation of the UM by the method proposed here, it is



necessary to characterize control samples with respect to their commutability. This could be done by comparing their behavior to a representative panel of patient samples for combinations of the most used measurement pro-cedures as described in the CLSI document EP30-A [19] . A limit of our study is that the commutability of control samples was not tested and therefore a matrix related bias could not be excluded for some diagnostic systems. In this case, all methods relying on EQA/IQC data to estimate the systematic component of TE, are likely to give incorrect UM values. For this reason, the LTUM estimates presently provided to participants in PBQ EQA schemes only use the peer group target values. An alternative could be to use the GUM Modeling approach which is a method less prone to this drawback but more difficult to implement in medical laboratories. However, our main goal in this work was not to obtain a reliable estimate of bias but to test a convenient method for calculating the UM and to compare it to a recommended method which also requires the com-mutability of samples.

At last, the UM estimate is tightly dependent on the choice of the target value as shown by the different values

observed when the homogeneous group (PG) mean is used instead of the mean of AP. Of course, the use of a TE estimate based on APs rather than on peer group EQA data is more appropriate to obtain valid conclusions regarding the suitability of routine methods for meeting TE goals for interpretation of patient results. However, the present standardization status of some analytes (tumor markers) requires the use of the same method (in the same labora-tory) for the monitoring of patients and justifies our bias estimation by comparison to the peer group. Furthermore, the UM estimates obtained using both references were in close agreement for many measurands except those for which standardization status is still a matter of concern (enzymes, immunoassays).

In conclusion, this study allowed validation of UM calculation by a simple method that uses only the results of EQA schemes. It provided estimates comparable to methods recommended by official bodies and enabled biol-ogists to compare estimates obtained by different methods. As an EQA organizer, we provide all participants at the end of each survey, since January 2014, with an estimate of UM by this method using data from control samples assayed

0%

5%

10%

15%

20%

25%

Fibrinogen ProT aPTT

PG LTUM normal level PG SH GTA 14 normal level

0%

5%

10%

15%

20%

25%


AP LTUM normal level AP SH GTA 14 normal level

0%

5%

10%

15%

20%

25%


AP LTUM pathologic level

AP SH GTA 14 pathologic level B

11.3% 12.4%

15.6% 14.8%

15.9% 15.7%

A

11.7% 13.0%

11.8%

14.6%

19.6%

16.6%

A'

9.8%

12.5% 10.8% 11.6%

6.7%

9.5%

0%

5%

10%

15%

20%

25%


PG LTUM pathologic level PG SH GTA 14 pathologic level

B'

9.5% 11.7%

9.2%

11.8%

5.7%

9.3%

Figure 6: SH GTA 14/LTUM comparison for hemostasis analytes. Median UMs of 20 laboratories for three hemostasis analytes estimated by the SH GTA 14 and LTUM approaches at normal and pathologic levels from AP (A and B) and PG (A and B ) results.



during the current year and the previous 2 years. The accu-mulation of results over time for a large panel of analytes and a great number of clinical laboratories will allow better appreciation of its advantages and disadvantages.

Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission. Financial support: None declared. Employment or leadership: None declared. Honorarium: None declared. Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

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