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Articles |
1
Istituto Scientifico H S Raffaele, Laboratorio Analisi, Via Olgettina 60, 20132 Milano, Italy.
2
IRCCS Centro Auxologico Italiano, Laboratorio
Analisi, Milano, Italy.
3
Università di Milano Istituto Scienze Biomediche,
Ospedale L.Sacco, Milano, Italy.
4
Dipartimento di Chimica e Biochimica Medica, Via
Saldini 50, Università di Milano, Italy.
a Author for correspondence. Fax +39-2-2643-2640; e-mail ceriotf{at}hsr.it
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Abstract |
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 Top Abstract IntroductionMaterials and MethodsResultsDiscussionReferences |
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2.20% and 4.70%, respectively). The overall imprecision obtained
was high (CV 6.5–20.0%) because of increased
interlaboratory–intermethod variability. A significant positive bias
(+9.2–+43.7%) was found for all the materials at lower creatinine
concentration. By using two human sera at different concentrations, we
could calculate the constant and the proportional calibration bias
displayed by each peer group. The majority of the lyophilized materials
showed a behavior divergent from the frozen pools, indicating
matrix-related problems. We propose a new algorithm for calculating
matrix bias correction factor instrument–reagent specific for each
material.
Key Words: indexing terms: quality control • reference method • control materials
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Introduction |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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2.2% for precision and 2.8%
for accuracy (1)). Imprecision is strictly dependent on
analyzer characteristics, and can be easily verified. On the contrary,
variables affecting inaccuracy (method specificity, type of
calibration, calibrator matrix, and value assignment) are more
difficult to identify and control. These variables lead to a wide
dispersion of results among different laboratories. As a result, the
measurement appears far from the desirable performances. Currently, there is more room for improvement in accuracy than for precision in creatinine determination. Through the use of reference methods and appropriate materials, it is possible to come closer to the "trueness" of the results. The availability of a definitive method is certainly a problem, but for accuracy of routine methods the real difficulty is the material used. The case of creatinine is particularly critical because the lack of commutability (2) is emphasized by the poor specificity and weakness of the majority of the routinely used picrate reaction-based methods (3)(4)(5).
This fact forces almost all the proficiency testing programs to use peer group target values without any means to verify the real accuracy of any single laboratory, and without progress toward improvement of the agreement between the different laboratories.
Here we describe the results of an external quality-assessment scheme (EQAS) from 51 laboratories of Lombardy region (Italy).1 We tried to focus on several aspects related to the accuracy of creatinine measurement. First, with a peculiar experimental design of replicate analyses, we could estimate components of variability. Second, through the use of frozen sera and the ultimate accuracy reference, an isotope dilution gas chromatography–mass spectrometry (ID GC-MS) method, we calculated constant and proportional components of the calibration error. Third, we verified the presence of matrix effects in most lyophilized sera and, with a modification of the algorithm proposed by Ross et al. (6), we calculated a matrix bias correction factor.
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Materials and Methods |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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Creatinine [Standard Reference Material (SRM) 914a, 99.8% purity] and lyophilized reference sera SRM 909a1 (certified value = 84 ± 1 µmol/L) and 909a2 (463 ± 6 µmol/L) were from NIST (October 13, 1993; revision of certificate dated February 24, 1993). Sera were reconstituted according to the NIST insert. [2H3]Creatinine (98 atom % excess) was from Isotec (Miamisburg, OH). N-methyl- N-(tert-butyldimethylsilyl)-trifluoroacetamide (MTBSTFA) was purchased from Fluka (Buchs, Switzerland). All solvents and general chemicals used were of analytical grade.
All solutions and sera were dispensed with known accuracy and imprecision as already reported (7). Calibrators were prepared by mixing various amounts of SRM 914a creatinine with [2H3]creatinine to provide a series of mixtures with known ratios of the two isotopomers between 0.8–1.2.
Weighed amounts of each serum were supplemented with a weighed aliquot of the [2H3]creatinine solution to get about a 1:1 ratio of [1H]creatinine:[2H]creatinine. After stirring, supplemented sera were kept at room temperature for 2 h to allow equilibration before protein precipitation obtained with acetone. The aqueous phase was separated and evaporated to dryness under reduced pressure. An isocratic separation of creatine from creatinine was achieved by HPLC with H2O containing 0.1% HCOOH (pH 5.5–5.7 with NH4OH) as mobile phase at 1 mL/min flow rate. Creatinine was monitored at 235 nm and the collected fraction was dried under vacuum at 40 °C. Creatinine was converted into its tert-butyldimethylsilyl derivative with 70 µL of CH3CN:MTBSTFA (2:1 by vol) at 70 °C for 30 min. Gas chromatographic separation was achieved with a 30-m SPB-35 column (Supelchem, Milan, Italy). The injector temperature was at 250 °C, the initial GC oven temperature was set at 170 °C for 1 min and subsequently increased to 180 °C at 2.5 °C/min, and to 270 °C at 30 °C/min. Injections of samples were alternated with duplicate analysis of calibrators having 1H:2H ratios of 0.8, 1.0, 1.2, 1.0, 0.8, etc. The isotopic ratio was determined by monitoring ions at m/z 298 and 301 for unlabeled and labeled creatinine, respectively.
Concentration of serum creatinine (µmol/L) was then computed from the measured isotopic ratio on the basis of the weight of each serum aliquot, the density, and the internal calibrator added, as already described (7).
experimental design
Eight different materials were sent to 51 clinical laboratories of
the Lombardy region: two fresh-frozen human serum pools (CON1 and CON2)
and six lyophilized materials (LYO1–LYO6). The frozen pools were
delivered in solid CO2, stored at -20 °C,
thawed on the day of analysis, and analyzed within 1 h.
Lyophilized sera were stored at 4 °C and reconstituted 1 h
before analysis. In each material, creatinine was measured in
quintuplicate in three consecutive days with the automated analyzers
routinely used (15 results per laboratory, per control material). The
study participants were asked to classify their analytical method
according to the chemical principle, the instrumentation, the source of
reagents, and the type of calibrator. According to this classification
we identified three homogeneous groups [Boehringer–Hitachi, Johnson &
Johnson (J&J), Beckman] and two miscellaneous groups.
control materials
CON1 and CON2 were prepared from sera obtained with Serum
Separator Tubes (SST Vacutainer; Becton Dickinson, Milan, Italy).
Concentration was adjusted by adding appropriate amounts of creatinine
(SRM 914a). LYO1–LYO6 were lyophilized commercial materials: LYO1
(Roche N, lot no. A 1136); LYO2 (Roche A, lot no. S 1135 2); LYO3
(Boehringer, Precinorm U, lot no. 177111 61); LYO4 (Boehringer,
Precipath A, lot no. 177481 71); LYO5 (Bio-Rad, Lyphochek 1, lot no.
15011); and LYO6 (Bio-Rad, Lyphochek 2, lot no. 15012).
instrumentation
Analytical instruments used in this experiment were: Boehringer
Hitachi analyzers 704 (2), 717 (7), 747 (6), 911 (3), (Boehringer
Mannheim, Milan, Italy); Beckman CX7 (4), CX3 (1), CX5 (1) (Beckman
Analytical, Cassina de Pecchi, Italy); Dax 24 (1) (Bayer, Cavenago,
Italy); Olympus AU 5000 (3), Au 510 (1) (Kontron Instruments, Milan,
Italy); Shimadzu CL 7000 (1), 7200 (1) (Shimadzu Italia, Milan, Italy);
IL 900 (4), ILAB 1800 (2), Monarch (1) and Phoenix (1) (Instrumentation
Laboratory, Milan, Italy); Ektachem analyzers 700 XR (8), 500 (3), 250
(1), (J&J, Cinisello Balsamo, Italy).
software
EQAS data were collected via an ad hoc computer program compiled
in CA-Clipper Version 5.2 (Computer Associates, Milan, Italy) and
distributed on floppy disk together with the samples. Data were
automatically transferred in a Lotus 1-2-3 spreadsheet (release 3.1;
Lotus Italia, Milan, Italy).
statistical analysis
The mean of each analytical run, the laboratory mean (mean of
three analytical runs), the group mean, and the grand mean (mean of
laboratory means) were calculated. SD and within-run CV
(CVw), between-run CV (CVb, containing
only the across-day component of variability), between-laboratories CV
(CVinter), and overall CV (CVovr) were
calculated with analysis of variance performed on a Lotus 1-2-3
spreadsheet.
Calibration bias line.
For each peer group we calculated
the equation of the line defined by the two frozen pools (CON1 and
CON2):
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Statistical verification of matrix effect occurrence.
Each laboratory mean, obtained for every lyophilized material, was
corrected for the calibration bias of the laboratory itself according
to the following formula:
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The statistical significance of the difference between corrected results, grouped according to the peer groups, and ID GC-MS value of each material was calculated (Student's t-test). A statistically significant difference indicates the presence of a matrix bias (i.e., noncommutability of the material).
Matrix bias correction factor.
A factor to correct bias
introduced by the matrix of the lyophilized control materials has been
obtained by modifying the formula proposed by Ross et al.
(6) to take into account the problem of the constant
component of the calibration bias, very common in creatinine
measurement with routine methods. The algorithm proposed by Ross et al.
(6) for the calculation of the matrix bias correction
factor of lyophilized sera is:
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Yp L is the peer group mean of lyophilized sera, CF is the GC-IDMS value of fresh frozen human pool, and CL is the GC-IDMS value of lyophilized sera.
Modified algorithm:
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Results |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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An overview of all results obtained on the six lyophilized materials
and the two frozen pools is shown in Table 1
. We report ID GC-MS target values and overall and peer group
means. Results obtained by the clinical laboratories (including overall
means and ANOVA) are also summarized. In some cases, such as LYO5, very
large discrepancies among method means are evident.
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The results obtained on the two frozen human serum pools were used to
calculate constant calibration bias and proportional calibration bias
of three homogeneous groups of analytical systems (we considered
homogeneous the groups constituted by instruments, reagents, and
calibrators from the same manufacturer). Table 2
shows the biases from the ID GC-MS values and the parameters of
the lines obtained.
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The results of the statistical verification of the occurrence of matrix
effect are presented in Table 3
. Only three of 18 material/analytical system combinations
exhibit commutable behavior.
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By using the formula illustrated in Materials
and Methods, one can calculate a "matrix bias correction
factor" taking into account the different components of the
calibration bias. Table 4
shows the matrix bias correction factor of each lyophilized
material for the different method groups. By multiplying the peer group
means by these factors, one can remove the component of intermethod
variability due to matrix effects from the results obtained on
lyophilized materials. Fig. 1
shows the peer group means obtained, for each material, before
and after results modification according to the matrix bias correction
factors. In Fig. 2
comparability of data achievable on fresh frozen sera and
lyophilized sera after correction is shown (J&J method group).
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Discussion |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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) and accuracy (bias of 0.36% and 1.51% from NIST target values on
909a1 and 909a2, respectively).
Our experiments (see Table 1
and Fig. 1a
) emphasize that:
(a) there are very discordant percent biases from the ID
GC-MS target values—very high for control materials with lower
creatinine concentrations, very small for sera with higher
concentrations; (b) the major component of variability is
the between-laboratories variability that is always very high;
(c) the intralaboratory variability can be considered
acceptable but, especially at the lower concentrations, it is far from
the analytical goal calculated on the basis of biological
intraindividual variability (1); and (d) the
frozen pools (CON1 and CON2), although showing very similar
intralaboratory variability, exhibit a lower interlaboratory
imprecision. The results are comparable (in terms of imprecision and
inaccuracy) with those obtained in a previous experiment
(9). Large differences among the method means were present
(Table 1
). In particular, LYO5 shows bias of >50% between enzymatic
and picrate methods, suggesting the presence of some noncreatinine
substance reacting with picrate. Unfortunately, 16 laboratories were
working with miscellaneous conditions [calibrators and (or) reagents
from manufacturers different from those of the instrumentation] or
with unique systems, and it was not possible to classify and treat
those data. Also, the enzymatic group is not homogeneous, with one
laboratory using the UV creatinine reaction and another using the
Trinder coupled reaction. For these reasons we performed further
calculations only for the three homogenous groups of analytical
systems: J&J analyzers, Beckman CX family, and Boehringer–Hitachi
family.
Assuming the frozen pools as not affected by any matrix effect, we used
them to calculate calibration bias (e.g., method bias observed relative
to ID GC-MS method) according to Ross et al. (6). Percent
biases obtained on CON1 were completely different from those on CON2,
thus suggesting the occurrence of a significant constant calibration
bias (Tables 1
and 2
). We decided to take into account constant
calibration bias by calculating the equation of the line defined by the
two pools. The data of slope and intercept (Table 2
) clearly
individuate a different behavior of the three peer groups. Note the
similarity of the parameters of our regression line for Hitachi systems
with the equation of the correlation between an HPLC reference method
and the Hitachi 911 results presented by Blijenberg et al.
(5). Clearly the methods based on the Jaffe reaction are
affected by an important positive constant calibration bias (Table 2
)
caused probably by an aspecific signal. This is particularly evident at
low creatinine concentrations or with some type of artificial material
such as LYO5. This positive bias, in the case of the
Boehringer–Hitachi group, can be almost completely attributed to the
picrate reactivity with proteins. The reading window of the Boehringer
method is quite long (~90 s), with a prolonged delay from the starter
addition (~90 s). This favors the interference from slow-reacting
interferents such as proteins (10). In fact, an
extensively dialyzed albumin solution (50 g/L) gives (on an Hitachi
747) an apparent creatinine value of 21 ± 0.9 µmol/L. The
apparent accuracy displayed for samples with intermediate concentration
is due to a concomitant negative proportional bias. Better performances
were obtained with enzymatic methods, both for dry and wet chemistry
(Table 1
). In particular, laboratories using wet chemistry enzymatic
methods provided very promising results. This finding is in agreement
with Blijenberg et al. (3)(4), but the very
limited number of participants using these methods (two) does not allow
any generalization.
Table 3
shows clearly that lyophilized sera behave differently from the
frozen pools. Only in three of 18 material/method combinations was the
difference between the two types of materials not significant. These
results imply that the use of target values on these types of materials
is useless and can lead to faulty considerations. The bias introduced
by the matrix is typical for a defined analytical system. Fig. 1a
shows
how different this effect is for the various materials and analytical
systems. With the application of the algorithm proposed, it is possible
to calculate factors (shown in Table 4
) that are able to correct for
the error introduced by the matrix. Fig. 1b
, in which the matrix effect
is corrected, shows almost identical behavior for the different
materials with similar creatinine content, whether frozen or
lyophilized. Indeed the bias/concentration profile of results obtained
on lyophilized sera after correction closely resembles behavior of
fresh frozen sera (Fig. 2
). The proposed algorithm has a more general
applicability than the previous one (6) and can give
reliable results even when a constant calibration bias is present.
All matrix bias correction factors were calculated with the peer group means, but we tried also to calculate the factors by using single laboratory data of the same peer group. The results obtained showed a noteworthy concordance among laboratories of the same group. The variability of the obtained factors, measured as CV, ranged between 0.80% and 3.75% according to the material and the group of methods. This homogeneity of data allows us to hypothesize the possibility of the use of a relatively small number of pilot laboratories to calculate the matrix bias correction factor for a defined lot of control material to be used in an EQAS.
The major problem of EQAS, when artificially manipulated control materials are involved, is the bias introduced by the materials themselves for the different types of methods. This fact forces the use of peer group means, but without any guarantee, apart from the producer declaration, of the real accuracy of the analytical system. However, it is not possible to verify whether the difference among the various analytical systems are caused by the characteristics of the material only or by real accuracy problems with a risk "of an implicit endorsement of methodologies that fail to satisfy fundamental accuracy goals" (11). Obviously the more straightforward approach to this problem should be the use of fully commutable material such as fresh or frozen sera, but the costs of distributing this type of material prevent its use, at least on a regular basis. The matrix-adjusted target values can be an acceptable compromise that allows the utilization of the lyophilized sera provided that two important limitations are adequately considered: (a) the matrix bias correction factor can be calculated only for well-defined analytical systems; (b) the serum pools used in generating the algebraic correction are the same as normal fresh serum specimens. The last one can be an important drawback; the probability that a minimally manipulated serum pool could exhibit a noncommutable behavior is low, but a check of the commutability, e.g., according to the College of American Pathologists' protocol (12), is advisable. Moreover, this approach is not intended to substitute the direct comparison with a Reference Method on fresh sera (13), but only to minimize the matrix effect, thus allowing the use of Reference Method target values for lyophilized materials.
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Acknowledgments |
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Footnotes |
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References |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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The following articles in journals at HighWire Press have cited this article:
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  S. Vickery, P. E. Stevens, R. N. Dalton, F. van Lente, and E. J. Lamb Does the ID-MS traceable MDRD equation work and is it suitable for use with compensated Jaffe and enzymatic creatinine assays? Nephrol. Dial. Transplant., September 1, 2006; 21(9): 2439 - 2445. [Abstract] [Full Text] [PDF]
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 G. L. Myers, W. G. Miller, J. Coresh, J. Fleming, N. Greenberg, T. Greene, T. Hostetter, A. S. Levey, M. Panteghini, M. Welch, et al. Recommendations for Improving Serum Creatinine Measurement: A Report from the Laboratory Working Group of the National Kidney Disease Education Program Clin. Chem., January 1, 2006; 52(1): 5 - 18. [Abstract] [Full Text] [PDF]
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, 
, LYO5; 
, 
, LYO1; 
, 
,
LYO3; 
, , LYO4; 
, 
, LYO2; 
, 
, LYO6; x, CON1 and
CON2).