Comparison of intermethod analytical variability of patient sera and commercial quality control sera

335

Clinica Chimica Acta, 93 (1979) 335-347 0 Elsevier/North-Holland Biomedical Press

CCA 10049

COMPARISON OF INTERMETHOD ANALYTICAL VARIABILITY OF PATIENT SERA AND COMMERCIAL QUALITY CONTROL SERA

W.C.H. VAN HELDEN *, R.W.J. VISSER, F.A.J.-T.M. VAN DEN BERGH and J.H.M. SOUVERIJN

of Clinical Chemistry,

Department

University

Hospital,

Leiden

(The Netherlands)

(Received September 22nd, 1978)

summary The intermethod analytical variability of 59 commercially available control sera was compared with that of patient sera. For this purpose, patient and control sera were assayed with respect to ten constituents (albumin, alkaline phosphatase, a-amylase, cholesterol, glucose, iron, lactate dehydrogenase, creatinine, protein, and urea), each with two analytical methods. Only 6 of the 59 control materials showed an in&method analytical variability comparable to that of the patient sera for all of the determinations. The use of patient sera for the calibration of routine analytical methods is recommended.

Introduction Quality control and calibration materials are in common use in clinical chemistry for calibration purposes, and to determine intra- and interlaboratory accuracy and precision [l-3]. In the analysis of serum components, the use of different methods may yield different results. Therefore, some manufacturers mention different values for the constituents of their control materials, depending on the method used for analysis. In the literature, however, there are reports that question the reliability of these values assigned by the manufacturer [4,5]. Other authors have reported variability between vials of the same batch of a given control material [6] or the fundamental problem of a lack of commutability (the ability of an enzyme reference material to show interassay activity changes comparable to those of the same enzyme in human serum [7]) of some control materials in enzyme determinations [ 8-101. * To whom

correspondence should be addressed.

IQ.

I.

PATIENT

Schematic

SERUM

representation

PHOS.

1

of the difference

I

ALU.

UREA

PROTL IN

LOH

IRON

CREATININE

CHOLESTEROL

AMYLASL

in intermethod

I

WAC

analvtical

1

variability

I

RESULT

I

between

sera

materials.

populations

with

of analysis

compatible

RESULTS

methods

of analysis

sera and control

the patient

patient

methods

RESULTS

not always

DIFFERENT

may yield

different

SIMILAR

yielding

different

337

This lack of commutability, which has also been found for non-enzyme determinations, has become a familiar feature of control materials in our laboratory since the introduction of automation. The results of the automatic analyzers employed were adjusted to those of the existing manual methods for the analysis of patient sera parameters. However, in similar assays of the commercially available control materials in use, discrepancies were found in the results obtained for some serum constituents (see Fig. 1). To investigate whether this phenomenon occurs generally, and, if so, to what degree, fresh patient sera were assayed for a number of constituents. Two analytical methods were used for the determination of each constituent. Similarly, a number of commonly used quality control materials were assayed and the results were compared with those of the patient sera. The case of a-amylase, being representative for the results obtained with all of the serum constituents, will be used as an example throughout. Materials and methods Fresh patient sera were assayed for ten constituents, i.e., albumin, alkaline phosphatase (orthophosphoric .monoester phosphohydrolases, EC 3.1.3.1), cr-amylase (alpha-1,4-glucan-4glucanohydrolase, EC 3.2.1.1), cholesterol, glucose, lactate dehydrogenase (L-1actate:NAD oxidoreductase, EC 1.1.1.27, abbreviated as LDH), iron, creatinine, urea, and total protein, with the methods of analysis used routinely in our laboratory. These methods were as follows: LDH was analyzed with a Model 8600 Reaction Rate Analyzer (LKB Produkter AB, S-161 25 Bromma, Sweden), using KABI reagents in a modification of the Wroblewski/LaDue method [ 111; iron was analyzed according to the Ferrozine method of Hiinteler et al. with a Technicon Auto-Analyzer I System [12]; cr-amylase was determined manually, according to the Phadebas method [ 131. The determinations of the other constituents were routinely carried out with a Technicon SMAC (Sequential Multiple Auto-Analyzer with Computer), using the appropriate methods and reagents provided by the manufacturer (see Table I). Albumin was also determined manually by electrophoresis on cellulose acetate (in 0.04 M sodium barbiturate/acetate buffer, pH 8.6) and densitometric scanning of the bands after treatment with Ponceau S coloring agent [ 141. In addition, determinations of the same serum fonstituents were carried out in these patient sera with a DuPont ACA (Automatic Clinical Analyzer) using the methods and reagents of the manufacturer (see Table I). The ten serum constituents were also assayed in quality control materials with exactly the same analytical techniques and apparatus. Fifty-nine different control and calibration materials were included (see Table II). Most of them are in common use in clinical chemistry laboratories; the others were control materials used in two interlaboratory quality control schemes *. Each patient serum was assayed once with the analytical techniques under study, as were the SKKCZ, Wellcome and Technicon ILCS control materials. * The Wellcome Group Quality Control Programme. using specially produced WelIcome materials. and that of the Stichting Kwaliteitsbewaking Klinisch Cheniische Ziekenhuislaboratoria (abbreviated as SKKCZ), which has provided a nation-wide interlaboratory quality control scheme for The Netherlands [Sl. using control materials obtained from either commercial manufacturers or other sources.

Total protein Urea

Lactate dehydrogenase

Glucose

Biuret reaction Diacetyl monoxlme reaction

Jaffe reaction Glucose oxldasa. with S-methyl-2benzothiarolinone hydrazone and dimethylaniline

Creatiine

Roterln tranaferrtn reaction BathophenantroIine color reaction Laetate-pyruvate system NADH absorbance Biuret reaction UmaZm Glutamate dehydrogenase

Llebermann-Burchard

buffer

Cholesterol

cu-Amylase

Bromocresol green reaction p-nitrophenyl phosphate 2-Amino-2-methyl-1-propanol buffer Maltopentaose hydrolysis Hexokinase reactton Cholesterol e&erase WV-DietkylaniEne r HCI Jaffe reaction Hexokinaw Glucose-S-phosphate dehydrogenase

Bromocresol green reaction p-Nitrophenul phosphate 2-Amino-2-methyl-l-prop~ol -

MATERIALS

Albumin Alkaline phosphatase

SERA AND CONTROL Dupont-ACA

OF THE PATIENT

Technicon-SMAC

USED IN THE ANALYSIS

Determination

METHODS AND APPARATUS

TABLE I

Ferrozine reaction on Auto-Analyzer I LKB reaction rate analyzer NASH absorbance -

-

-

Phadebas

Electrophoresis cellulose-acetate -

Manual

TABLE

II

CALIBRATION

AND CONTROL

MATERIALS

USED

NO.

Manufacturer

Trade-name

Lot No.

Source

1 2 3 4 5 6 I a 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59

Bio M#xieux

Lyo-Trol AX Lyo-Trol NX Lyo-Trol PX Precinorm U Precinorm u Monitrol-IE Monitrol-HE SKKCZ * SKKCZ Chemistry Control Normal Chemistry Control Elevated Bovine Control Serum Quality Assurance Serum L-I Quality Assurance Serum L-II Versatol Automated Lo Versatol Automated Lo Versatol Automated Hi Versatol Automated Hi SKKCZ SKKCZ QPAKI Q PAK II Q PAK II SKKCZ SKKCZ Autonorm Autonorm Seronorm SKKCZ SKKCZ RIV SKKCZ SKKCZ SKKCZ SKKCZ SKKCZ SKKCZ ILCS I ILCS I ILCS I ILCS I ILCS II ILCS II ILCS II ILCS II SMAC Reference SMAC Reference Quality Control Programme *** Quality Control Programme Quality Control Programme Quality Control Programme Quality Control Programme QuaIity Control Programme Quality Control Programme Quality Control Programme Quality Control Programme Quality Control Programme SKKCZ SKKCZ

6242111706

Pie

6239111707 6243111707 715 716 LTD 147 A PTD 51 A 30-I 30-11 702833 703847

Human

Boehringer Dade

DuPont General Diagnostics

Hyland

Nyegaard

RIV ** SKKCZ

Technicon

Wekome

2802114A~809094A 4Dl61/4B690 7043169076 125106/7042 4Dl45/4B389 148106/7040 241 2411 NO4 PO 2 P II 25-I 25-11 214 216 128 26-I 26-11 P 50415 27-I 27-11 29-I 29-11 31-I 31-11 A71637 A25022 AlS026 B7K737 B2S066 BlS048 B6K788 B8A049 December 19. 1977 January 2.1978 January 16.1978 January 30,1978 February 13.1978 February 27.1978 May 29.1978 June 12.1978 June 26.1978 July lo,1978 28-I 28-11

Human

Human Human Human Human Human Bovine Bovine Bovine Human Human Human Human Human Human

Human Human Human

Bovine Bovine Home

Horse

Human HtmmJl Human Human Human Human Human Human Human Human Bovine Bovine Bovine Bovine Bovine Bovine Bovine Bovine Bovine Bovine

* SKKCZ uses control materials obtained from either commercial manufacturers or other sources. As Lot No. the quality control scheme number is used. * *RIV: Dutch National Institute of Public Health. *** WeBcome Group Quality Control Programme. using specially produced WeBcome sera. As Lot No. the samole date of the control oroaramme is used.

340

The other quality control materials were assayed at least in duplicate; the average values of the replicate results are given. The control materials were reconstituted according to the manufacturer’s directions and allowed to stand at room temperature for at least 2 h before use. All determinations in each serum or control-material sample were carried out on the same day, whereas the replicate control-sera determinations were carried out on different days in control-serum samples from different vials. Results Patient set-a For each of the ten serum constituents we chose at least 11 patient sera containing different amounts of that particular constituent, the levels ranging from (be)low normal to high pathological. These sera were then re-assayed with the DuPont ACA. Because neither the routine methods nor the ACA method may be considered a reference method, ordinary regression analysis of these data was not possible [15]. Therefore, under the assumption that the two methods of analysis have the same variance in each case, functional regression analysis [ 151 was used to evaluate the patient-sera data. In functional regression analysis the line is drawn, through the point (Q) such that the sum of the squares of the perpendicular distances from the points to this line is minimal. An example is shown in Fig. 2, i.e., the results obtained for a-amylase. cr-Amylase was assayed in 23 patient sera with both the manual Phadebas technique and the ACA. When the Phadebas results were plotted on the ordinate and the ACA results on the abscissa, the equation of the functional regression line was Y = 1.01X + 4.14. The functional regression line represents the population of patient-sera results. This line and the correlation coefficient (R = 0.994) show that there is good agreement and correlation between these two methods. The standard deviation. (S.D.) of the perpendicular distances, which is a measure of the amount of scattering of the points around the line, was small. For a-amylase, the S.D. was 21 U/liter. Of all determinations the results obtained in patient sera are given in Table III; the procedure was the same as for a-amylase.

Fig. 2. Results of the oramylase determinations in patient sera. Equation of the functional regression line: Y = 1.01X + 4.14 (R = 0.994, S.1). = 21 lJ/liter). The most extreme outliers on either side of the line are indicated.

341 TABLE

III

PATIENT-SERA

RESULTS

SfXUlll constituent

Number of sera assayed

Methods compared (see Table I)

Equation of functional regression line

Correlation coefficient

Albumin

20 29 46 23 16 11 18 16 11 21 19

ACAlSMAC ACA/ManuaI ACA/SMAC ACA/Phadebas ACA/SMAC ACA/SMAC ACA/SMAC ACA/AA-I ACA/LKB ACA/SMAC ACA/SMAC

Y Y Y Y Y Y Y Y Y Y Y

0.962

1.2 g/l

0.990 0.990 0.994 0.997 0.998 0.999 0.994 0.997 0.996

0.9 a/I 4.6 U/l 21 IJ/l 0.15 mmol/l 7 pmol/I 0.2 mmol/l 1.1 pmol/I 7.5 up

Alkaline phosphatase a-Amylase Cholesterol Creatinine Glucose Iron Lactate dehydrogenase Protein Urea

= = = = = = = = = = =

0.96X 1.03X 0.89X 1.01x 0.89X 0.96X 1.04X 1.04x 1.02X 0.95X 0.98X

+ 1.47 - 1.72 - 7.29 + 4.14 + 0.45 + 12.45 -0.56 + 3.40 + 0.41 + 1.84 - 0.01

0.998

Standard deviation

1.6 0 0.3 mmol/l

Con trol materials

Fifty-nine control materials were assayed for the same serum constituents with the same routine techniques as used for the patient sera. These constituents were then assayed in the control materials with the ACA as well. When the results of the determinations in the control materials are included in the plots of the patient-sera values, marked scattering around the patient-sera regression lines is observed in some cases. Fig. 3 shows the results obtained for cu-amylase. cu-Amylase was determined in 55 control materials with both the Phadebas technique and the AC-A. The perpendicular distance between a point representing control-material results and the patient-sera regression line is a measure of the divergency of these results from the population of patient-sera values. To estimate this divergency, the perpendicular distance is divided by the standard deviation of the patientsera regression line. In Fig. 3 only those 34 control materials for which this divergence amounts to 3 or more times the standard deviation are shown. Table IV shows the results for all of the control materials. As can be seen

8 0

0

250

0

500

750

1

loo0 ACA

A#

1250’2000 DJ/ll

A_

2251) 2900

Fig. 3. Results of the cv-amylase determinations in control materials shown in the plot of the patient-sera values. The most extreme outliers on either side of the line are indicated.

VARIABILITY

OF THE CONTROL

MATERIALS

IN RELATION

TO THAT OF PATIENT SERA

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33

Control mater&I No. *

2 0 0

1

1

4 10 9 -

-

-

0 -

1 -

-1

-

-

-2

0 0 2 1

2 -

2 1 1 2 2 1*** 4 ***

-2 -1

Electrophoresisl ACA

0 0 1 4

-

12 *** 17 *** 2

-

1 0 0 0 0 3 1

SMAC/ACA

Albumin

0 0 1

1 0

4 4 6 -

-1 -4 -

-3

1 1

-1 -

-1 -1

1 1 1

AIkaBne phosphatase.

0 4

3

1 0

2 2

-

-3 -3 -6 -30 40 -15 -52

-1

-15 -5 -9

-10 -12 44

0

-

-1 4 -6 -1 -6 -1 -9

-261

ACA

*‘*

a-Amy&se, Pbadebasf

2

4 -

0 7 3 -

2 1 5 -

1 0 8 6 -

-1

-3 -3

3 3 2 0 1 3 2 3 4 *** ***

Cholesterol. SMACIACA

2

2 0 0

1 0

0 -

-

-1

0 2 5 -

2 -

-1 -1 -1

2 -1 31 *** -1 -

-1

-2

-1 -1

Creatinine, SMAC/ACA

1 -

1 -

-3

2

-

2 2 3

1 3 2 0 -

-

1

3 2 2 2 3 1 1 1 3 1 0 ***

GIucose, ** SMACIACA

0 *** ***

-

6 -6 -5 -

-1 -1 -1 -3 -3 -1

-2

-7 -1 -1 -3 -3 -1 -1 -2 -9 -5 4 -5 -3 -5 -2 -1

AA-I/ACA

kOEl,

0 1 1 1 0

2 1

-

-6 -

2 1

2 2 1 -

5 -

-1

-1

1

6 *il

-1 -

-18

LDW, LKBlACA

0 0 0 0 0 1 0 5 2 2

0 -

8 11 1 -

-1 -1 -1

0 1

0 -1 -

-1 -1

11 -1 1

-1

Protein, SMAClACA

0 -

-

0 1 1

-2 -

0 2

1 0 4 3 -

1 -

0 0 1 0 0 0 1 0 1 1 4 ***

Urea, SMACtACA

For each constituent, tbe divergency of the control-material results from the patient-sera regression line is expressed as standard deviations. A negative sign means that the control-material point lies below the patientsera ragrcssion line. no sign means that the control-material point lies above this Bne. (See also Figs. 2 and 3.)

INTERMETHOD

TABLE IV

0

0 -

-1 4 -2 -

-1 3

28

Greatestouthers 3 patientsera .-3

28

10

Totalmnnber of control materials tested

Totalnumber ofnoncommutable control matarials

13

45

-3 2

4 3 4 -1 2 -1 1 -1 0 1 1 1 0 1

34

55

-1 2

4 7 -2 0 0 0 4 -2 -2 0 0 -1 -13 -1 -12 -17 % -7 -12 -20 -21 -14 -12 -12

5 8 2 2 2 2 11

-

1

2 -

33

47

-2 2

7

46

-2 1

-2 1 0 1 I 1 1 1 2 1 4 2 3 2

0 -1 -1 -2 *** -2 *** I*** 9***

2 2 3 12 10 12 12 6 5 5 4 5 4 5 4 5 6 8 6 -

0 q*** -

-

4 7 2

0 -

13

47

-1 2

-2 0 4 0 -1 2 2 1 -10 -1 1 3 -2 3 2 2 1 3 -2 3 1 0 4 -

-1

28

45

-2 2

-10 % -7 % -12 -7 -12 -13

-2 -1 -11 -15 -13

-3 *** -

-1

-10 -18 %

-

14

42

-2 2

5 2 2 2 4 1 3 3 1 -1 -

4 4 0 1 4 1 3 -3 5 2 -1

-1

5

52

-1 4

13 0 0 1 0 1 1 1 0 2 0 1 1 1 1 1 0 0 0 1 0 0 -1

-1 -1

15

45

-3 1

3 -11 -11 -11 -9 -21 -3 -20 -21 16 20 2 1 2 5 1 1 1 0 1 1

1

-

* See Table II. **For %lucose. the S.D.ofthepatientseraregressionline wasverysmaU.andthereforethe differencebetweenthe assayresultsoftheindividuafcontrolmatarfals fslessthanthedivergencyfrom the patient-seraregressionlinemightsuggest. *** Controlserumcontainingthiseonstituentinanamountlying >10% outsfdetherange of thepatient-seratested(routinemethodvalues).

3

-2

5 5 6 3 -

-

-1 1 4 -

-

-

-

0 0 1

34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 58 57 58 59

E w

344 TABLE

V

INFLUENCE Intermethod a-amylase

regression Control

OF CALIBRATION

vanability of control materials in relation to that of patient sera for the determination of before and after recalibration of the DuPont ACA. The divergencies from the patient-sera lines is expressed as standard deviations (see Table IV).

material

No. *

Divergency Before

1 2 3 4 6 7 15 17 21

-261 0 -1 -4 -1 -6

from patient-sera

recalibration

regression

After recalibration -308 -1 -1 -5 -1 -8

1 -1 -8

line

0 -1 -10

* See Table II.

from Table IV, most of the control materials only performed well in the glucose, protein, and creatinine assays. For all of the other constituents tested, a large number of control sera differed considerably from the patient sera as to inter-method analytical variability. In Table IV the divergencies, expressed as standard deviations, between the regression lines and the most extreme outliers of the population of patient-sera values on either side of the lines, are given as well. For albumin, creatinine and protein, scattering of the control-material points with a divergency 23 X SD. is restricted to one side of the patient-sera regression line. In the other cases the scattering occurs on both sides of this line Influence 0 f recalibration As a result of this investigation, it became clear that in the determination of or-amylase there was a systematic difference between the results of our routine method and those of the ACA, which reflected some degree of drifting. The functional regression line found for patient sera in the first instance was Y = 1.23X - 0.89, with R = 0.985 and S.D. = 23 U/liter. Before this was noted, some of the control materials had already been assayed. The results shown in Table III were obtained for patient sera after recalibration of the ACA. The control sera were then re-assayed for a-amylase as well, which showed that the change in the ACA values was proportional to the observed change in the patient-sera values both before and after recalibration (see Table V). Thus, the divergency from the patient-sera regression line of each individual control material before recalibration remained the same after recalibration of the ACA. Discussion The data presented here indicate that when assayed for various serum constituents with two analytical methods for the determination of each consti-

345

tuent, most of the commercially available control materials under study show an intermethod analytical variability that is not compatible with the corresponding variability of patient sera. The same phenomenon in enzyme reference materials has been called a lack of commutability by Fasce et al. [ 71. We suggest that the concept of commutability be extended to the intermethod variability of serum constituents of quality control materials in general, in the following sense. When two different analytical methods are used to determine a serum constituent in a quality control material, and the measured values (when represented in a functional regression plot of the results of these determinations in a range of fresh patient sera) show a divergency from the regression line obtained with the patient-sera results amounting to <3 X S.D. of that line, this control material is said to be commutable with patient sera in the determination of the serum constituent under consideration. The criterion of 3 X S.D. is provisional, We adopted it because a randomly chosen element of a population has a probability of 0.3% of having a divergency of 3 X SD. from the mean of a population *. In Table IV the number of control materials that did not satisfy this criterion is given for each of the serum constituents studied. Under our experimental conditions, only 6 out of the 59 control materials tested showed commutability with patient sera for all of the constituents they were tested for. Only 2 out of these 6 sera, however, were tested for all ten constituents. The control-material data presented here are the averages of at least two, but usually three or more replicate determinations, samples from different vials being used for each replicate. The close agreement between the replicate results is an indication that the between-vial variability observed by Jansen et al. [6] played no role in our investigation (data not shown). Our results show a difference in analytical behaviour between control sera of human and animal origin only for albumin; here 9 of the 10 control materials showing poor commutability were of animal origin. For such purposes as intra- and interlaboratory quality control and the calibration of analytical methods, calibration sera of human origin and of exactly known composition are in theory the materials of choice. In practice, however, our results indicate that, because of the lack of commutability most of them exhibit at present, these materials should not be used unrestrictedly for these purposes. If, for example, a control material like No. 25 (see Table IV) had been used in an interlaboratory quality control trial for e-amylase, without taking the methods of analysis and calibration used by eadh participant into account, the result of the one method might have been judged as “wrong”, whereas the result obtained with the other method might seem “right”. For patient sera, however, both of these methods would give identical results. This confirms the view of one group of authors [7,9] that when control materials are used in an interlaboratory quality control scheme, it is essential for the evaluation of the results to take into account not only the analytical methods and apparatus used by each participant, but also the method used for calibra* For SmallPoPuIations, ability

IeveI depends

this probability may on the number of patient

be somewhat sera assayed

larger. In this investigation. the actual probin each case. and varies between 1.0 and 0.3%.

346

tion. For example, use of the average of all the results as a basis for the interpretation of the participants’ values, cannot yield information about the accuracy of these values and may lead to serious misinterpretation when they are evaluated. A poor performance in such an interlaboratory quality control trial does not necessarily mean that patient sera are badly analyzed as well. Difficulties that may be encountered when quality control materials are used for calibration purposes can be illustrated by the results we obtained in the analysis of both patient sera and control materials for a-amylase before and after recalibration of the DuPont ACA (see Table V). If the ACA had been recalibrated such that both our methods gave the same results for a given control serum, they might still give strongly diverging results for another control serum, or patient sera. The new patient sera results might even no longer be compatible with the existing normal range. Therefore, we advocate the use of fresh patient sera for the calibration of routine analytical methods, until suitable control materials are developed. We suggest that the assay method that is routinely used for a given serum constituent be calibrated against a reference method, with the use of patient sera containing that constituent in an amount ranging from (be)low normal to high pathological. With the routine method, calibrated in this way, the control materials in use can be assayed in replicate and their average value and S.D. used for monitoring the intra-method, day-to-day precision. However, these values should also be considered cautiously, because a possible lack of commutability might confuse the interpretation of these results as well. The use of patient sera in the calibration of routine methods, as described here, minimizes the risk that patient sera values represent artefactual deviations from the normal range due to the use of a non-commutable control material in the calibration procedure. Furthermore, the use of patient sera and similar reference methods in the calibration procedure would facilitate the interlaboratory comparison of patient-sera results. The question remains, however, whether these results represent the true values. The accuracy of the patient sera results obtained in this way depends ultimately on the reliability of the reference method used and therefore represents the best approximation of the true values that can be attained in the present state of art. In the present study, only a limited number of serum constituents and assay methods were investigated. Thus, the data presented in this report do not indicate which of the control materials tested are to be considered “good” or “reliable”, and which are not. Nevertheless, our results indicate that patient sera and most of the commercially available control materials should be regarded and used as two different materials. Acknowledgements The authors wish to express their gratitude to the Department of Medical Statistics of the University of Leiden and in particular to Drs. J. Tijssen for providing the statistical methods and to Mr. J. Hagedooren for performing the computations. Furthermore, we would like to thank Dr. F.A. de Wolff for critical reading of the manuscript. We also are very grateful to Dr. W. van der Slik for his stimulating interest in the investigation. Finally we like to thank Miss H.G. der Kinderen for handling of the manuscript.

347

References 1 Biittner. J., Borth. R.. BoutweB. J.H.. Broughton, P.M.G. and Bowyer, R.C. (1977) Provisional Recommendation on Quality Control in Clinical Chemistry. Part 3. Calibration and Control Materials, Clin. Chim. Acta 75, Fll-F20 (following p. 180) 2 Biittner, J., Borth, R., Boutwell. J.H., Broughton. P.M.G. and Bowyer. R.C. (1978) Provisional Recommendation on Quality Control in Clinical Chemistry. Part 5. External Quality Control, Chn. Chim. Acta 83. F189-F202 (following p. 188) 3 Jansen. A.P.. Van Kampen, E.J.. Leijnse, B.. Meiiers, C.A.M. and Van Munster. P.J.J. (1977) Clin. Chim. Acta 74.191-201 4 Dobrow. D.A. and Amador, E. (1970) Am. J. Clin. Pathol. 53.60-67 5 Attwood. EC. and Zochowski, G. (1976) Clin. Biochem. 9. 260-264 6 Jansen. A.P., Van Kampen, E.J.. Meilers. C.A.M.. Van Munster, P.J.J. and Boerma, G.J.M. (1978) Clin. Chim. Acta 84.246-258 7 Fasce. C.F., Jr., Rej. R., Copeland, W.H. and Vanderhnde, R.E. (1973) Clin. Chem. 19, 5-9 8 Biittner. H., Frei. J.. Gabl. F. and Roth, M. (1973) in Quality Control in Clinical Chemistry (Anido, G.A. et al., eds.), pp. 246-267, H. Hubert Publishers, Bern/Stuttgart/Vienna 9 Rej. R., Fasce, C.F., Jr. and Vanderhnde, R.E. (1976) Clin. Chem. 21, 1141-1158 10 Jung. K., Griitzmann. F.D., Eager, E. and Fechner. C. (1977) Clin. Chhn. Acta 79. 515-526 11 Wroblewski, F. and LaDue. J.S. (1955) J.S. Proc. Sot. Exp. Biol. Med. N.Y. 90. 210-213 12 Hiinteler, J.L.A., Van der Slik. W. and Persijn. J.-P. (1972) Clin. Chim. Acta 37,391-397 13 Ceska, M.. Birath, K. and Brown, B. (1969) Clhr. Chhn. Acta 26,437444 14 Kaplan, A. and Savory, J. (1965) Clin. Chem. 11.937-942 15 Armitage, P. (1971) in Statistical Methods in Medical Research, pp. 275-278. Blackwell Scientific Publications, Oxford/Edinburgh