Uncertainty in measurement for 43 biochemistry ... LTCV PBQ.pdf · try, this is particularly...

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 25 months 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,

α -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 é culaire, H ô pital de la Croix-Rousse, Hospices Civils de Lyon,

Lyon, France ; and ProBioQual, Lyon, France

Roland Meley: Service d ’ H é matologie, St Etienne, France ; and

ProBioQual, Lyon, France

Laurence Chardon: Service de Biochimie et Biologie mol é culaire,

H ô pital Edouard Herriot, Hospices Civils de Lyon, Lyon, France ; and

ProBioQual, Lyon, France

Karim Chikh: Service de Biochimie et Biologie mol é culaire, Centre

Hospitalier Lyon Sud, Hospices Civils de Lyon, Pierre-B é 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 é culaire,

Groupement Hospitalier Est, Hospices Civils de Lyon, Bron, France ;

and ProBioQual, Lyon, France

Richard Cohen: Service de Biochimie et Biologie mol é culaire,

H ô pital Edouard Herriot, Hospices Civils de Lyon, Lyon, France ;

Universit é Claude Bernard Lyon-1, ISPB Facult é de pharmacie,

MATEIS UMR CNRS 5510, France ; and ProBioQual, Lyon, France

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

2 Matar et al.: 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,

γ -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 et al. [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 et al.: Uncertainty in measurement estimates from external quality assessment 3

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

25 months (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 et al. [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/xs

LCVx

=

(2)

2 2 2

x

n 1(b 1) s ( y x )

nLTBx

− ⋅ − ⋅ + −=

(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 et al. [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)

WLs

u(IQC) CV 100m

= = ×

(6)

2

2

E

| E |u(EQA) s

3

⎛ ⎞= +⎜ ⎟⎝ ⎠

(7)

where CV = coeffi cient of variation, s WL

= within-

laboratory standard deviation, m = IQC laboratory mean,

− ⎛ ⎞−= = = ×⎜ ⎟− ⎝ ⎠∑ ∑ 2

i ii i

E(%) i

i

E (%) (E (%) E(%)) y xE(%) , S , E (%) 100

n 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 et al. [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 ( Figure 2 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 2

1s (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

χ 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

et al. [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 et al. (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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a

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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se

ALP

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 et al. [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 α -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, γ -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 Table 2 .

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 5

CEA, μ g/L 666 13 17.5 12 5

CA 125, kU/L 607 13 44.2 12 5

CA 15-3, kU/L 630 13 30.4 15 6

CA 19-9, kU/L 634 13 34.3 21 7

PSA, μ g/L 795 14 5.0 8 5

Free PSA, μ g/L 616 15 0.54 17 6

FSH, UI/L 667 12 26.7 12 4

LH, UI/L 732 12 16.2 14 5

hCG, UI/L 1081 17 31.1 9 6

Prolactin, mUI/L 578 14 507 16 6

TSH, mUI/L 918 14 4.56 8 4

Free T3, pmol/L 734 12 5.19 11 7

Free T4, pmol/L 828 12 17.0 11 6

Estradiol, 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.



AF

P

CA

125

CA

15-

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9

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!! 23.5 27.4 27.4 29.4 23.5 29.4 29.4 29.4 29.4 29.4 29.4 29.4 29.4 29.4 !! 11.6 11.0 10.7 11.1 12.5 11.8 9.7 10.4 9.6 11.3 12.4 11.4 11.6 10.8 10.6

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 et al. [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

0%

5%

10%

15%

20%

25%

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

0%

5%

10%

15%

20%

25%

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

A

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16.5 20.9 7.9 8.2 19.4 12.5 5.2 3.3 10.4 6

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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17.9 7.7 4.4 5.5 6.8 6.1 3.3 3.5 6.7 5.4 6.8 6.3 8.0 6.8

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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ium

Trig

lyce

ride

Ura

te

ALA

T

AS

AT

CK

GG

T

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

Bic

arbo

nate

s

Tot B

iliru

bin

Chl

orid

e

Cho

lest

erol

Cre

atin

ine

Iron

Pot

assi

um

Sod

ium

Trig

lyce

ride

Ura

te

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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