Technical Briefs Automated Selection of Statistical Quality-Control Procedures to Assure Meeting Clinical or Analytical Quality Requirements, James O. Westgard1,2* and Bernard Stein2 [1 Dept. of Pathol. and Lab. Med., Univ. of Wisconsin Med. School, Madison, WI 53792 (*address for correspondence; fax 608-263-0910), and 2 Westgard Quality Corp., Ogunquit, ME 03907; e-mail www.westgard.com] Efforts to totally automate laboratory testing processes must address the issue of how to assure the quality of the final test result. A recent survey by Tetrault and Steindel [1] reported that laboratories are using the same qualitycontrol (QC) procedures today that they used 10 years ago. Actually, more than half of the laboratories indicated they are still using control limits set as the mean 6 2s (the 12s rule), a practice that dates to the 1950s and ’60s [2, 3], when statistical QC was first used with manual methods and the first generation of automated Technicon AutoAnalyzer systems. Other laboratories indicated they are using variations of the multirule type of QC procedure introduced in the early 1980s [4]. Tetrault and Steindel [1] recommend that “the best set of control rules will vary from method to method and cannot be determined through simple algorithms or formulas. The laboratorian has to balance true errordetection capabilities against the probabilities of falsely rejecting a good run.” The information needed about the error-detection and false-rejection characteristics of stable QC procedures became available in the clinical chemistry literature almost 20 years ago [5– 8] and was extended by Cembrowski et al. [9 –12] to consider patient data algorithms. This information was incorporated into laboratory QC texts [13, 14] and clinical chemistry texts [15, 16] in the 1990s and continues to be expanded and improved by Parvin [17–20] and by Smith and Kroft [21]. Guidelines and strategies for selecting QC procedures were described in 1986 [13], and some detailed applications have been published to demonstrate the selection and design of QC procedures [22–24]. QC selection grids were introduced in 1990 [25] to provide a simple table “look up” approach for selecting QC procedures on the basis of the size of systematic error that must be detected and on an assay’s expected stability or frequency of errors. More quantitative quality-planning models were introduced in 1991 [26, 27], and a new planning tool—the OPSpecs chart3—was developed [28, 29] to show the relation between allowable precision and accuracy as well as what QC is necessary to assure detection of critical-sized errors that would otherwise cause method performance to exceed a defined analytical or clinical quality requirement. For applications involving analytical quality requirements and commonly used QC procedures, a compilation of OPSpecs charts is available to support manual applications for the full range of CLIA proficiency testing criteria for acceptability [30]. For applications involving clinical quality requirements and a wider variety of QC
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procedures, a Windows3-based PC program (QC Validator3, Version 1.1; Westgard Quality Corp., Ogunquit, ME) was developed to prepare power function graphs, critical error graphs, and OPSpecs charts [31]. Quality-planning models describe the mathematical relationships between a quality requirement and the factors that can cause variation in the final test result. Some of these factors are analytical, such as the stable imprecision (smeas, %) and inaccuracy of the method and the sensitivity of the QC procedure to detect unstable random and systematic errors (DSEcrit). Others are preanalytical, such as the within-subject biological variation (swsub), which describes the changes in concentration about the subject’s true homeostatic set point. We have now developed an automated process to select statistical control rules and numbers of control measurements (N) that will assure the quality required by clinical decision interval (Dint) criteria or analytical total error (TEa) criteria. The quality-planning models used in the automatic process are essentially the same as those described earlier [26, 27], except that the effect of replicate measurements on method performance and QC design are more completely and directly considered by entering the number of replicate samples analyzed. Here, we describe the automatic process and demonstrate that it provides results comparable with those in earlier studies documented in this journal. The computer program used, QC Validator Version 2.0, runs under MicroSoft Windows 3.1 or Windows 95. It requires an IBM or IBM-compatible computer with a 386 or higher processor, 1 MB hard disk space, 2 MB RAM memory, a VGA interface and monitor, a printer supported by Windows, and TrueType fonts. The program includes an installation utility, a detailed HELP facility, and a manual that provides tutorials, a reference guide to program function and operation, and a review of the technical background for the program. The computer program allows the user to enter information about the characteristics of a method’s performance (e.g., smeas, inaccuracy, expected frequency of errors, swsub) and the quality required (clinically important change or Dint, TEa). The user then initiates automatic selection on the basis of the number of control materials to be analyzed (i.e., 1, 2, or 3), and the program constructs a chart of operating specifications (OPSpecs chart) that displays the selected control rules and N. The automatic QC selection process is based on user-editable criteria for the types of control rules that can be implemented by the laboratory, the total numbers of control measurements that are practical, the maximum percentage of false rejections that can be tolerated, and the minimum percentage of error detection that is acceptable for detection of medically important DSEcrit or random errors. The table of candidate QC procedures contains the power curves and OPSpecs calculation parameters for 94
3 QC Validator and OPSpecs are registered trademarks of Westgard Quality Corp. Windows is a registered trademark of Microsoft Corp.
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different QC procedures, including: constant-limit single rules 12s, 12.5s, 13s, and 13.5s, with N values from 1 to 8; multirules such as 13s/22s/R4s/41s/10x# , with N values of 2 and 4 applied over runs from 1 to 5; multirules such as 13s/2 of 32s/31s/12x# , with N values of 3 and 6 applied over runs from 1 to 4; multirules such as 13s/22s/R4s/41s/8x# , with N values of 8 applied over 1 run; variable-limit single rules such as 10.05 and 10.01, with N values from 2 to 8; mean and range rules such as x# 0.05/R0.05, x# 0.01/R0.01, and x# 0.002/R0.002, with N values from 2 to 8; and extended limit rules such as 14s, 15s, and 16s, with N values from 1 to 8. The program includes an editor utility that allows users to add power curves and OPSpecs calculation parameters for other rules and N values. OPSpecs charts were prepared for the examples illustrated earlier by spreadsheet calculations of allowable imprecision and allowable inaccuracy for both the clinical model [26] and the analytical model [27]. Fig. 1 (top) shows the OPSpecs chart for a cholesterol example, for which Dint 5 20% and swsub 5 6.5%. The x-intercepts for maximum allowable imprecision range from 2.4% to 3.5%, which agrees with the range of 2.4% to 3.5% observed previously (Fig. 2a in [26]). For individual QC procedures, the largest difference was observed for the 13s rule with N 5 4, for which the current intercept is 2.8% (vs the previous estimate of 2.95%). Performing duplicate cholesterol tests and using the average of the two measured concentrations for diagnostic classification provides a maximum allowable imprecision of 3.3% to 4.7%, compared with the earlier estimate of 3.2% to 4.9% (Fig. 2b in [26]. For a cholesterol example in which TEa is 10%, Fig. 1 (bottom) shows the estimated allowable imprecision (xintercepts) to be 1.9% to 2.6%, the same as the 1.9% to 2.6% documented earlier (Fig. 4 in [27]). In the same example, when error detection is optimized for random error rather than systematic error, the allowable imprecision is estimated as 1.2% to 2.3%, very similar to earlier estimates of 1.2% to 2.4% (Fig. 6 in [27]). The differences between the program output and the earlier spreadsheet calculations are the result of the different calculation parameters for the sizes of systematic error that can be detected with the specified probability. These parameters were estimated as the x-values that correspond to the 0.90 intersection with the power curves for the different control rules and N values, which in the spreadsheet calculation were constructed on a point-topoint basis and then later fitted with smoothed curves to improve the estimates. The automatic QC selection process was tested by comparing the QC procedures selected by the program that uses the OPSpecs methodology with those selected in an earlier study in which critical error graphs were used manually to select control rules and N values for 18 different analytes on a multitest chemistry analyzer [23]. In the earlier study, the design strategy fixed N at 2 per run, considered only single-rule QC procedures, and
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varied the control rules to match the error detection capability to the test method performance. Table 1 shows the test, total error requirement, observed imprecision, calculated ‚SEcrit, and the QC procedure selected manually from critical error graphs or selected automatically from OPSpecs charts. For the automatic selection process, the frequency of errors parameter was set to ,2%, to permit use of 50% Analytical Quality Assurance (AQA) charts in those situations where 90% AQA could not be achieved. For 14 tests, both the manual and automatic approaches selected the 13.5s rule with N 5 2; for another test (albumin), both approaches selected the 12.5s rule with N 5 2. For two tests (chloride and total CO2), the 12.5s rule with N 5 2 had been selected manually, whereas the
Fig. 1. OPSpecs charts for (top) a clinical decision interval quality requirement of 20% and (bottom) a TEa quality requirement of 10%. Both panels: The operating limits (top to bottom) correspond to the following QC procedures: 13s/22s/R4s/41s with N 5 4; 12.5s with N 5 4; 13s with N 5 4; 12.5s with N 5 2; 13s/22s/R4s with N 5 2; and 13s with N 5 2. The operating point (bottom panel) represents the National Cholesterol Education Program’s recommended 3% specifications for imprecision and inaccuracy.
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automatic selection process identified a multirule procedure with N 5 4. In these latter two cases, if the automatic QC selection criteria were changed to restrict N to 2, then the automated process would select a multirule procedure with N 5 2; if the selection logic for 50% AQA charts was changed to prefer single rules over multirules, then a 12.5s rule with N 5 2 would be selected. That is, the automatic QC process can be modified to implement the same preferences used earlier in the process of manual selection from critical error graphs. For one test (calcium), method performance relative to the quality desired was so marginal that the assay required a special effort to make duplicate measurements, which was necessary to improve method performance and process control. The effect of making duplicate measurements could be considered directly with the automatic selection process, which then identified a 12.5s rule with N 5 4 as necessary to provide 90% detection of medically important errors. Therefore, although the QC designs and outcomes of the automatic and manual selections were similar, one thing was not: The time required to assess the QC designs for these 18 tests and to document the selection with printouts of OPSpecs charts was ;30 min with the automatic QC selection process, whereas the earlier study took several months. In the future, one can envision an automatic QC selection process integrated into laboratory instrument software, on-line QC monitors, data management workstations, laboratory information systems, and automated laboratory process control software, thereby permitting laboratory scientists to focus on defining the quality needed for a test, rather than worrying about the control
rules and numbers of control measurements that should be used. When coupled with on-line data acquisition and appropriate software to estimate method performance characteristics, the information needed for QC design will itself be automatically available. When linked to software for on-line QC monitoring, the selected rules can be implemented automatically. Thus, an integrated system for analytical quality management is possible when an automatic QC selection process is linked with method performance data and on-line QC monitoring, allowing the management of analytical quality with an appropriate QC design based on current information on method performance. Such an integrated quality-management system provides the opportunity for a dynamic QC system that can automatically adjust to changes in method performance, e.g., changing to more-sensitive control rules and higher N values if performance deteriorates and relaxing the rules and reducing N if performance improves. Changes in method stability can be factored into the dynamic QC process and can also be used to adjust run length, as suggested by recent design work on the application of “average of normals” patient data algorithms to measure process stability and determine when to requalify the testing process [32]. Thus, technology for the next-generation system for analytical quality management can be envisioned now that automatic QC selection is a reality.
Westgard Quality Corp. supported the development of this software. Robert Kennedy performed the coding, and Sten Westgard developed the HELP facility and program
Table 1. QC procedures selected by automated QC selection process using OPSpecs charts compared with those selected manually by using critical error graphs. Control rules and N selected Test
TEa, %
Smeas, %
DSEcrit
Manually
Automatically
Sodium Potassium Chloride Total CO2 Glucose Urea N Creatinine Calcium Phosphorus Uric acid Cholesterol Total protein Albumin Total bilirubin GGT ALP AST LD
3.08 10.0 4.0 10.0 8.0 10.0 30.0 5.0 10.0 10.0 10.0 12.0 10.0 20.0 10.0 10.0 20.0 20.0
0.52 1.17 1.04 2.50 1.20 1.33 3.00 1.68 1.28 1.10 1.35 1.84 2.13 2.20 1.17 1.17 3.0 3.0
4.27 6.90 2.20 2.35 5.02 5.87 8.35 1.33 6.16 7.44 5.76 4.87 3.04 7.44 6.90 6.90 5.02 5.02
13.5s, N 5 2 13.5s, N 5 2 12.5s, N 5 2 12.5s, N 5 2 13.5s, N 5 2 13.5s, N 5 2 13.5s, N 5 2 Special case 13.5s, N 5 2 13.5s, N 5 2 13.5s, N 5 2 13.5s, N 5 2 12.5s, N 5 2 13.5s, N 5 2 13.5s, N 5 2 13.5s, N 5 2 13.5s, N 5 2 13.5s, N 5 2
Same Same MR, N 5 4 MR, N 5 4 Same Same Same MR, N 5 4 Same Same Same Same Same Same Same Same Same Same
MR, multirule; GGT, g-glutamyltransferase; ALP, alkaline phosphatase; AST, aspartate aminotransferase; LD, lactate dehydrogenase.
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documentation. Additional information about the program is available at www.westgard.com. References 1. Tetrault GA, Steindel SJ. Qprobe 94 – 08. Daily quality control exception practices. Chicago: College of American Pathologists, 1994. 2. Levey S, Jennings ER. The use of control charts in the clinical laboratory. Am J Clin Pathol 1950;20:1059 – 66. 3. Henry RJ, Seagalov M. The running of standards in clinical chemistry and the use of the control chart. J Clin Pathol 1952;5:305–11. 4. Westgard JO, Barry PL, Hunt MR, Groth T. A multi-rule Shewhart chart for quality control in clinical chemistry. Clin Chem 1981;27:493–501. 5. Westgard JO, Groth T, Aronsson T, Falk H, de Verdier C-H. Performance characteristics of rules for internal quality control; probabilities for false rejection and error detection. Clin Chem 1977;23:1857– 67. 6. Westgard JO, Falk H, Groth T. Influence of a between-run component of variation, choice of control limits, and shape of error distribution on the performance characteristics of rules for internal quality control. Clin Chem 1979;25:394 – 400. 7. Westgard JO, Groth T. Power functions for statistical control rules. Clin Chem 1979;25:863–9. 8. Westgard JO, Groth T. Design and evaluation of statistical control procedures: applications of a computer “quality control simulator” program. Clin Chem 1981;27:1536 – 45. 9. Cembrowski GS, Westgard JO, Kurtyzc DFI. Use of anion gap for the quality control of electrolyte analyzers. Am J Clin Pathol 1983;79:688 –96. 10. Cembrowski GS, Chandler EP, Westgard JO. Assessment of “average of normals” quality control procedures and guidelines for implementation. Am J Clin Pathol 1984;81:492–9. 11. Cembrowski GS, Westgard JO. Quality control of multichannel hematology analyzers: evaluation of Bull’s algorithm. Am J Clin Pathol 1985;83:337– 45. 12. Lunetsky ES, Cembrowski GS. Performance characteristics of Bull’s multirule algorithm for the quality control of multichannel hematology analyzers. Am J Clin Pathol 1987;88:634 – 8. 13. Westgard JO, Barry PL. Cost-effective quality control: managing the quality and productivity of analytical processes. Washington, DC: AACC Press, 1986:240pp. 14. Cembrowski GS, Carey RN. Laboratory quality management. Chicago: ASCP Press, 1989:264pp. 15. Westgard JO, Klee GG. Quality assurance. Chapter 17 in: Burtis CA, Ashwood ER, eds. Tietz textbook of clinical chemistry, 2nd ed. Philadelphia: WB Saunders, 1994:548 –92. 16. Cembrowski GS, Sullivan AM. Quality control and statistics. Chapter 4 in: Bishop ML, Duben-Engelkirk JL, Fody EP, eds. Clinical chemistry: principles, procedures, correlation, 3rd ed. Philadelphia: Lippincott, 1996:61–98. 17. Parvin CA. Estimating the performance characteristics of quality-control procedures when error persists until detection. Clin Chem 1991;37: 1720 – 4. 18. Parvin CA. Comparing the power of quality-control rules to detect persistent systematic error. Clin Chem 1992;38:358 – 63. 19. Parvin CA. Comparing the power of quality-control rules to detect persistent increases in random error. Clin Chem 1992;38:364 –9. 20. Parvin CA. New insight into the comparative power of quality-control rules that use control observations within a single analytical run. Clin Chem 1993;39:440 –7. 21. Smith FA, Kroft SH. Exponentially adjusted moving mean procedure for quality control: an optimized patient sample control procedure. Am J Clin Pathol 1996;105:44 –51. 22. Linnet K. Choosing quality-control systems to detect maximum clinically allowable analytical errors. Clin Chem 1989;35:284 – 8. 23. Koch DD, Oryall JJ, Quam EF, Feldbruegge DH, Dowd DE, Barry PL, Westgard JO. Selection of medically useful quality-control procedures for individual tests done in a multitest analytical system. Clin Chem 1990;36:230 –3. 24. Westgard JO, Oryall JJ, Koch DD. Predicting effects of QC practices on the cost-effective operation of a multitest analytical system. Clin Chem 1990; 36:1760 – 4. 25. Westgard JO, Quam EF, Barry PL. Quality control selection grids (QCSGs) for planning QC procedures. J Clin Lab Sci 1990;3:271– 8. 26. Westgard JO, Hyltoft Petersen P, Wiebe DA. Laboratory process specifications for assuring quality in the US National Cholesterol Education Program. Clin Chem 1991;37:656 – 61. 27. Westgard JO, Wiebe DA. Cholesterol operational process specifications for assuring the quality required by CLIA proficiency testing. Clin Chem 1991; 37:1938 – 44. 28. Westgard JO. Assuring analytical quality through process planning and quality control. Arch Pathol Lab Med 1992;116:765–9. 29. Westgard JO. Charts of operational process specifications (“OPSpecs
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charts”) for assessing the precision, accuracy, and quality control needed to satisfy proficiency testing criteria. Clin Chem 1992;38:1226 –33. 30. Westgard JO. OPSpecs manual, expanded ed. Operating specifications for precision, accuracy, and quality control. Ogunquit, ME: Westgard QC, 1996:240pp. 31. Westgard JO. A program for evaluating QC procedures. Med Lab Observ 1994;26(2):55– 60. 32. Westgard JO, Smith FA, Mountain PJ, Boss S. Design and assessment of average of normals (AON) patient data algorithms to maximize run lengths for automatic process control. Clin Chem 1996;42:1683– 8.
Rapid Screening for a1-Antitrypsin Z and S Mutations, Christopher W.K. Lam, Chi-Pui Pang,* Priscilla M.K. Poon, Chang-Hong Yin, and Geetha Bharathi (Dept. of Chem. Pathol., Chinese Univ. of Hong Kong, Prince of Wales Hosp., Shatin, N.T., Hong Kong; *author for correspondence: fax 852 26365090, e-mail
[email protected])
a1-Antitrypsin (A1AT) is a serine protease inhibitor required for the prevention of proteolytic tissue damage, principally in the lung, by neutrophil elastase released by inflammatory cells [1]. While severe A1AT deficiency is the major factor leading to emphysema and related pulmonary diseases, it is also associated with neonatal hepatitis and cirrhosis [1, 2]. A1AT deficiency is an autosomal codominant disorder with a prevalence of about 1:3000 in Caucasians [3]. The A1AT gene has 7 exons spanning ;12 kb. The most common gene defect resulting in A1AT deficiency is that of a protease inhibitor (PI)-system Z mutation Glu342 to Lys, which is a single base substitution of G to A in exon 5 [4, 5]. The S mutation, a Glu264 to Val change, is caused by an A to T substitution in exon 3 [6]. Individuals with SS are unaffected, SZ may be symptomatic, and ZZ results in the most severe clinical symptoms. In Caucasians the prevalence of the S allele ranges from 5% to 10% and Z allele 2% to 5% depending on geographical location [7, 8]. Although the frequencies are unknown in the Chinese, geographical variability of the A1AT alleles is evident by phenotypic analysis of the PI variants [9]. We have established a rapid screening procedure involving multiplex PCR to detect the Z and S mutations in Chinese in Hong Kong who came from southern China. EDTA–whole-blood specimens were obtained from local Chinese in Hong Kong who attended the Prince of Wales Hospital for routine checkup or for treatment of diabetes mellitus. Genomic DNA was extracted from the blood specimens by the salting-out method [10]. Our procedure for mutation analysis was modified from the PCR-mediated site-directed mutagenesis method of Tazelaar et al. [11] with their primers for the Z and the S mutations, labeled as primers ZF and ZR and primers SF and SR respectively. Each PCR mixture, in a final volume of 25 mL, contained 0.2 mmol/L deoxynucleoside triphosphates (Boehringer Mannheim, Mannheim, Germany), 1.5 mmol/L magnesium chloride, 0.1 g/L gelatin, 2.5 pmol each of primers ZF, ZR, SF, and SR (synthesized by Gibco BRL, Gaithersburg, MD), 0.2 mg of DNA, 0.5 U of Taq polymerase (Gibco BRL), and 13 PCR buffer from Gibco
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BRL. After an initial denaturation at 94 °C for 5 min, a 35-cycle PCR program was carried out on a Perkin-Elmer (Norwalk, CT) thermal cycler: 94 °C for 1 min, 55 °C for 1 min, and 72 °C for 2 min; final extension at 72 °C for 10 min. After PCR, the restriction digestion mixture was prepared: 10 mL of PCR product, 1 U of Taq I restriction endoclease (Gibco BRL) [12], and buffer to a final volume of 15 mL. Restriction digestion was completed after 2 h of incubation at 65 °C. The digested PCR products were analyzed by 3% agarose electrophoresis at constant voltage of 200 V for 1 h. Control samples of known genotype were electrophoresed in each gel along with a size calibration mixture. For fast screening, 0.2 mg of DNA from five individuals were added to a PCR mixture for amplification and subsequent Taq I restriction digestion. When an abnormal result appeared, the individual DNA specimens were analyzed separately to identify the DNA sample carrying the mutant allele. To validate this protocol, all the DNA specimens in this study were analyzed individually and in groups of five. Identical results were obtained. We obtained DNA specimens from 2005 unrelated Chinese subjects free of primary lung and liver diseases. Two individuals were found to be heterozygous for Z, i.e., MZ, and another two heterozygous for S, i.e., MS (Fig. 1). No SS, SZ, or ZZ were found. There were therefore two Z and two S alleles from a total of 4010 alleles, giving a frequency of 0.05% for both the Z and S mutations and 0.1% for the MZ and MS genotypes in the Chinese population. Combined with simultaneous phenotyping observation that showed absence of other mutations, our study suggests that 99.8% of the Chinese are of the MM genotype.
Fig. 1. Taq I endonuclease restriction analysis of A1AT alleles after amplification with Z and S primers in the same reaction mixture. Lanes (a) to (c) were PCR products of 5 DNA templates amplified simultaneously: (a) only M alleles were detected, (b) a Z allele was detected, and (c) an S allele was detected. Lanes (d) to (h) were PCR products of single DNA templates amplified individually: (d) an MS control, (e) an MM homozygote, (f) an MM control specimen, (g) an SS control, and (h) a ZZ control. (M) is a DNA marker of pBR HaeIII digest.
Such a dominance of the MM genotype in Chinese is unexpected, although people of Asian origin are known to have fewer Z mutations than Caucasians. While northern Europeans have a higher prevalence of the Z genotypes than southern Europeans, the Z and S mutations range from 1% to 4% and from 5% to 10% respectively in most Caucasian populations [7, 13]. In the Chinese, even the MZ and MS heterozygotes are less prevalent, at 0.5%, similar to the SS or ZZ homozygotes, at 0.25% and 0.3% respectively, of the British population [13]. To ascertain the association between the Z mutation and A1AT phenotypes in the Chinese population, we are analyzing the genotypes and phenotypes of normal Chinese subjects and of patients with emphysema. Meanwhile, we have shown our protocol of a double PCR with 5 DNA templates to be rapid, reliable, and economical for screening a large number of specimens directly for the Z and S mutations. There is, however, a limitation in this approach of batch analysis. If a single DNA sample in the batch of five samples failed to amplify by PCR, it would not be recognized. If this sample happened to be from a patient with a mutation, it would have been missed. In our individual analysis of 2005 samples, we did not have any sample that failed to amplify, showing the PCR protocol to be robust.
We thank Kathy Piesse of the Royal Prince Alfred Hospital, Sydney, Australia and N.A. Kalsheker of the University of Nottingham, UK, for their supply of specimens of confirmed M, S, and Z genotypes. References 1. Cox DW. a1-Antitrypsin deficiency. In: Scriver CR, Beaudet AL, Sly WS, Valle D, eds. The metabolic and molecular bases of inherited disease, 7th ed. New York: McGraw-Hill, 1995:4125–58. 2. Blank CA, Brantly M. Clinical features and molecular characteristics of a1-antitrypsin deficiency. Ann Allergy 1994;72:105–20. 3. Sveger T. Liver disease in alpha1-antitrypsin deficiency detected by screening of 200,000 infants. N Engl J Med 1976;294:1316 –21. 4. Long GL, Chandra T, Woo SLC, Davie EW, Kurachi K. Complete sequence of the cDNA for human a1-antitrypsin and the gene for the S variant. Biochem 1984;23:4828 –37. 5. Lomas DA, Evans DL, Stone SR, Chang WSW, Carrell RW. Effect of the Z mutation on the physical and inhibitory properties of a1-antitrypsin. Biochem 1993;32:500 – 8. 6. Jeppsson J-O. Amino acid substitution Glu–Lys in alpha-1-antitrypsin PiZ. FEBS Lett 1976;65:195–7. 7. Hjalmarsson K. Distribution of alpha-1-antitrypsin phenotypes in Sweden. Hum Hered 1988;38:27–30. 8. Dykes DD, Miller SA, Polesky HF. Distribution of alpha1-antitrypsin variants in a US white population. Hum Hered 1984;34:308 –10. 9. Ying QL, Zhang ML, Liang CC, Liu XP, Huang YW, Wang RX, et al. Geographical variability of alpha-1-antitrypsin alleles in China: a study on six Chinese populations. Hum Genet 1985;69:184 –7. 10. Miller SA, Dykes DD, Polesky HF. A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res 1988;16: 1215. 11. Tazelaar JP, Friedman KJ, Kline RS, Guthrie ML, Farber RA. Detection of a1-antitrypsin Z and S mutations by polymerase chain reaction-mediated site-directed mutagenesis. Clin Chem 1992;38:1486 – 8. 12. Dry PJ. Rapid detection of alpha-1-antitrypsin deficiency by analysis of a PCR-induced TaqI restriction site. Hum Genet 1991;87:742– 4. 13. Hutchison DCS. The epidemiology of a1-antitrypsin deficiency. Lung 1990;168(Suppl):535– 42.
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Quantitative Immunological Detection of Total Estrogen Receptor (Cytosolic and Nuclear) in Term Decidua of Preeclampsia: a Preliminary Study, Sanaa Eissa,1* Mohamed M. Mostafa,2 Alaa A.E.A. El-Gendy,3 and Ibrahim A. Senna3 [1 Oncology Diagnostic Unit, Biochem. Dept. (*address for correspondence: fax 202-285-9928), 2 Dept. of Gyn., Ain Shams Faculty of Med., Abbassia, Cairo, Egypt, and 3 Fayoum General Hospital, Egypt] Although progesterone and estrogens are essential for maintaining human pregnancy after implantation, few reports have investigated the localization of their specific receptors in different uterine cell types throughout pregnancy [1, 2]. Wu et al. detected estrogen receptors (ER) in low concentrations in decidua in early pregnancy but not in term decidua [2]. ER concentrations in term decidua of pregnancies with complications (in particular, preeclampsia) have not been investigated. Classical biochemical assay methods estimate ER either in cytosol fraction or in the high-salt extracted nuclei [3–5]. These assays need two subcellular fractionation steps and two ER assays in two fractions: cytosol and nuclear extract. To measure total ER (cytosolic and nuclear) in one fraction using one single assay in decidual samples, we modified the assay method by using a homogenization buffer containing KCl. The effective KCl concentration for maximum ER extraction without inhibition of the ER enzyme immunoassay (EIA) ranged from 0.4 – 0.8 mol/L KCl. All steps of sample preparation in our laboratory were carried out at 4 °C, all reagents were purchased from Sigma Chemical Co. (St. Louis, MO), and all women included in the study gave informed consent. We used
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scrapped decidual samples from placental bed from women with uncomplicated pregnancies who underwent cesarean section for obstetrical causes (n 5 10) and from women with preeclampsia (n 5 20): mild (n 5 4), moderate (n 5 6), and severe (n 5 10). Tissues were immediately washed in ice-cold saline and homogenized at 1 g/10 mL in ice-cold homogenization buffer (10 mmol/L Tris buffer, pH 7.5, containing 10 mmol/L K2EDTA, 100 mL/L glycerol, 5 mmol/L benzamidine, 10 mmol/L 2-mercaptoethanol, 0.39 mmol/L phenylmethylsulfonyl fluoride, and 5 mg/L aprotinin) with Ultraturax T-25 homogenizer for five bursts of 1 min each, separated by a 1-min pause. The homogenate was filtered and divided into two parts. The first part was centrifuged in a Beckman CS-6R centrifuge (Brea, CA) at 800g for 15 min at 4 °C to obtain the crude nuclear pellet. The supernatant fluid was recentrifuged at 100 000g for 1 h with a Beckman L7 ultracentrifuge to obtain the cytosol. The crude nuclear pellet was dissolved in 10 mmol/L ice-cold phosphate buffered saline (pH 7.2) by sonication for three 30-s bursts and recentrifuged at 800g for 15 min at 4 °C. The washed nuclear pellet was incubated with 5 volumes of ice-cold high-salt extraction buffer (homogenization buffer containing 0.4 mol/L KCl) on ice for 30 min with vortexmixing every 10 min. Then it was ultracentrifuged in a Beckman L7 ultracentrifuge at 100 000g for 30 min at 4 °C. The nuclear extract (supernatant) was obtained. The second part of the homogenate was incubated with 0.4 mol/L KCl on ice for 30 min with vortex-mixing every 10 min, then ultracentrifuged in a Beckman L7 ultracentrifuge at 100 000g for 30 min at 4 °C. The supernatant was isolated. After quantifying the protein concentration in the cytosol, nuclear extract, and tissue extract by using
Fig. 1. (A) Total ER in term decidua in normal pregnancy and mild, moderate, and severe preeclampsia; (B) cytosolic and nuclear ER in term decidua in severe preeclampsia. Dotted line represents the lower detection limit for immunological detection of ER.
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Bradford’s method [6] with bovine serum albumin as the calibrator, we applied the samples directly to the ELISA plate for assay of ER with EIA kit from Abbott Laboratories (Chicago, IL) [7]. ER concentrations estimated by our modified method strongly correlated to those calculated as a sum of ER concentration in cytosol plus those in nuclear extract (y 5 20.98 1 1.128x, r 5 0.91, P 5 0.005). Cytosolic, nuclear, and total ER were not detectable (lower detection limit, 1 fmol/mg protein) in term decidua from women with uncomplicated pregnancies or from women with mild or moderate preeclampsia, but were detected in term decidua from women with severe preeclampsia (Fig. 1) (cytosolic ER, 1.4 –9 fmol/mg protein, mean 4.98 fmol/mg protein; nuclear ER, 1.5–11 fmol/mg protein, mean 4.68 fmol/mg protein; total ER 2.6 –19.2 fmol/mg protein, mean 9.1 fmol/mg protein). This difference was statistically significant by Student’s t-test (P 5 ,0.01). Although our results suggest a significant association between ER detection in term decidua and severe preeclampsia, the cause– effect relationship cannot be determined in this study. Establishing a cause– effect relationship may improve understanding of the biological relationship and may have potential clinical implications through development of diagnostic or therapeutic markers for these lesions. We recommend the routine use of this modified assay for quantification of total ER in the clinical laboratory. We thank A. Khalifa for his unlimited support. References 1. Perrot-Applanat M, Deng M, Fernandez H, Lelaidier C, Meduri G, Bouchard P. Immunohistochemical localization of estradiol and progesterone receptors in human uterus throughout pregnancy: expression in endometrial blood vessels. J Clin Endocrinol Metab 1994;78:216 –24. 2. Wu WX, Brooks J, Millar MR, Ledger WL, Glasier AF, McNeilly AS. Immunolocalization of estrogen and progesterone receptors in the human decidua in relation to prolactin production. Hum Reprod 1993;8:1129 –35. 3. Kokko E, Janne O, Kauppila A, Vihko R. Effects of tamoxifen, medroxy progesterone acetate and their combination on human endometrial estrogen and progestin receptor concentrations. Am J Obstet Gynecol 1982;143: 382–5. 4. Clarck JH, Peck EJ Jr. Nuclear retention of receptor– estrogen complex and nuclear receptor sites. Nature 1976;260:635–9. 5. Thorpe SM, Lykkesfelt AE, Vinterby A, Lonsdorfer M. Quantitative immunological detection of estrogen receptors in nuclear pellets from human breast cancer biopsies. Cancer Res 1985;46(Suppl):4251. 6. Bradford MM. A rapid and sensitive method for quantitation of microgram quantities of protein utilizing the principle of protein dye binding. Anal Bioch 1976;72:248 –51. 7. Greene GL, Nolan C, Engler JP, Jensen EV. Monoclonal antibodies to human estrogen receptor. Proc Natl Acad Sci U S A 1980;77:5115–9.
Speciation of Arsenic in Serum, Urine, and Dialysate of Patients on Continuous Ambulatory Peritoneal Dialysis, Xinrong Zhang,1 Rita Cornelis,1* Jurgen De Kimpe,1 Louis Mees,1 and Norbert Lameire2 (1Lab. for Anal. Chem., Inst. for Nuclear Sci., Univ. of Gent, Proeftuinstraat 86, Gent, Belgium; 2Renal Div., Dept. of Med., Univ. Hosp., De
Pintelaan 185, B-9000 Gent, Belgium; *author for correspondence: fax 32 (0)9 2646699, e-mail Cornelis@ inwchem.rug.ac.be) Renal replacement therapy is currently achieved by hemodialysis (HD), hemofiltration, hemodiafiltration (HDF), continuous ambulatory peritoneal dialysis (CAPD), or renal transplantation. The concentration of trace elements in serum of patients could be influenced by these treatments. A significant increase of As concentrations in serum of patients on HD treatment has been reported [1, 2]. As concentrations higher than the reference value were also observed in serum of patients on HDF [3]. Although there are several studies describing the status of trace elements in serum, plasma, and dialysate in CAPD patients [4, 5], no data on As serum concentrations of CAPD patients are available. The aim of this work is to determine total As concentrations and to speciate As species in serum, urine, and dialysate of CAPD patients. Fourteen CAPD patients were studied. Serum, urine, and dialysate samples were collected from the University Hospital. Patients gave their informed consent before blood sampling. To decrease the influence of As intake from the diet, patients were requested to refrain from ingesting seafood during the 3 days before blood and urine collection. The reagents and apparatus for the separation and measurement of As species and for the measurement of total As have been described [6]. Briefly, two types of HPLC columns were used for the separation of anionic and cationic As species: an anion exchange column (Supelcosil LC-SAX, 250 3 4.6 mm; Supelco, Bellefonte, PA) and a cation exchange column [Dionex (Sunnyvale, CA) Ionpac® CS 10, 250 3 4 mm]. A PerkinElmer (Norwalk, CT) 3030 atomic absorption spectrometer was used throughout for the detection of As signals. Analysis of serum creatinine was performed according to the Jaffe method [7]. The accuracy of the total As measurements was tested by simultaneously analyzing a Certified Freeze-Dried Reference Serum (CRM) of the University of Gent, Belgium. The accuracy of the As speciation measurements was tested by analyzing a BCR candidate Reference Material CRM 526 tuna tissue, as no serum reference material is yet available for the As speciation study. No significant differences were established between the analytical results and the certified values. The precision (CV) of the method for 10 replicate analyses of aqueous solution of As species at a concentration of 10.0 mg/L was always better than 5% for each form of As. The 3-day run-to-run precision of measurement of 10.0 mg/L As species added to serum was better than 10%. The mean total As concentration in the serum of 14 CAPD patients is 4.67 6 5.41 mg/L, significantly higher (P ,0.001) than the reference values of 0.96 6 1.52 mg/L in serum of healthy subjects previously obtained in our laboratory [8], indicative of an accumulation of As in serum of CAPD patients. The main As species in the serum of these patients are dimethylarsinic acid (DMA) and arsenobetaine (AsB), respectively carrying 15.2%
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(0.71 6 1.05 mg/L) and 76.2% (3.56 6 4.27 mg/L) of total As. No significant differences were observed between CAPD patients and previously reported nondialyzed uremic patients (0.82 6 1.05 mg/L DMA and 3.55 6 4.58 mg/L AsB) [6]. The DMA concentrations of CAPD patients are, however, significantly lower than those of HD patients (1.93 6 1.51 mg/L, 29.8% of total As, P ,0.001) [6], although no significant difference of AsB concentration was observed between these two groups (3.56 6 4.27 vs 3.47 6 2.89 mg/L, P 5 0.5658). Fig. 1(top) shows the comparison of As species concentration in the serum of the three groups of patients (uremic nondialyzed, CAPD, and HD). The AsB data imply that the main source of As accumulation in serum of CAPD patients comes from the diet, as AsB is particularly present in fish and seafood. Significantly lower DMA concentration in CAPD patients than in HD patients indicates that the CAPD treatment is probably more efficient in removing the toxic inorganic As species from the blood and better avoids the in vivo methylation in the body since this treatment is a continuous procedure compared with the intermittent procedure of HD. The mean concentrations of DMA, AsB, and total As in urine of 10 CAPD patients with residual urine production are 2.24 6 1.80, 9.09 6 8.50, and 12.28 6 10.66 mg/L, respectively. These concentrations are significantly higher
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than the serum values of these patients (P 5 0.0191, 0.009, and 0.040, respectively). However, considering the relatively small amount of urine produced by these patients (mean 758 mL/24 h) vs the overall blood volume (estimated mean 5000 mL/per patient, mean hematocrit 34.1%) and the volume of drained dialysate (mean 8680 mL/24 h), the absolute urinary excretion of As is very low. The mean amounts 6 SD in the total urine are: DMA 1.34 6 0.98 mg, AsB 4.62 6 2.57 mg, and total As 6.30 6 3.37 mg, and represent only 10% of total As distributed in the four compartments (serum 23%, packed cells 14%, dialysate 53%). Calculation of the ratio of DMA/AsB showed no significant difference between urine and serum (P 5 0.1456) or dialysate (P 5 0.0845). This suggests nonselectivity of renal removal of DMA, a more toxic As species than AsB. A very low As concentration was found in fresh dialysate (0.04 mg/L). The mean concentrations are, however, increased to 3.98 6 4.91 (total As), 0.59 6 0.87 (DMA), and 3.06 6 3.96 (AsB) mg/L in 24-h dialysate. No significant differences in As concentrations were found between serum and drained dialysate after 24-h dialysis (P 5 0.4482, 0.5984, and 0.5770 for total As, DMA, and AsB, respectively), indicating a nonselective accumulation or removal of different As species. These results can be easily understood because CAPD means a slow equilibrium
Fig. 1. Comparison of (top) mean total As and As species concentrations in serum of healthy subjects (HS) (n 5 23), uremic nondialyzed (ND) (n 5 19), CAPD (PD) (n 5 14), and hemodialysis (HD) (n 5 18) patients, and (bottom) mean total As and As species concentrations in serum (SE) (n 5 14), dialysate (DL) (n 5 14), and urine (UR) (n 5 10) of CAPD patients.
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dialysis. The low-molecular-mass species of AsB and DMA are easily transferable across the peritoneal membrane, and equilibrium in concentrations is readily achieved. The As species in dialysate are significantly lower than those in urine (P ,0.005 for each As species) because of the concentrating capacity of the kidney. The analysis of total As and As species on each of the four exchanges of CAPD dialysate showed no significant differences in concentrations on 1 day for the same patient. Considering a mean volume of 8680 6 912 mL of drained dialysate per day, the mean amount of total As is 35.1 6 45.7 mg per patient/24 h. Fig. 1 (bottom) compares the concentrations of total As and the As species in the three different fluids (serum, dialysate, and urine). Neither inorganic As species of As(III) and As(V) nor methylmalonic acid and AsC could be detected in serum, urine, and dialysate of the CAPD patients, with one exception. Patient 5 showed measurable concentrations of As(III) and As(V) in urine, but not in her 24-h dialysate. The possibility that the urine was contaminated with inorganic As species during collection cannot be excluded, although a very clean collector was used for the urine, and the patient was instructed about the collection. As this result was only observed in one specimen, more patients and urine samples are needed to confirm this finding. By dividing the subjects into the groups according to the serum creatinine concentrations, we found that the group with significantly higher serum creatinine concentration has a significantly higher As concentration (P ,0.05). The creatinine concentrations for the groups of healthy subjects and CAPD and HD patients were respectively 80 6 25, 646 6 244, and 914 6 173 mmol/L, whereas the As concentrations in the three groups were respectively 0.96 6 1.52, 4.67 6 5.41, and 6.47 6 4.28 mg/L. A similar conclusion was obtained previously when we studied the accumulation of As in the serum of uremic patients on conservative treatment and of patients on hemodialysis treatment [6, 9].
We thank M.C. Lambert for the assistance on the collection of the samples. References 1. De Kimpe J, Cornelis R, Mees L, Van Lierde S, Vanholder R. More than tenfold increase of As in serum and packed cells of chronical hemodialysis patients. Am J Nephrol 1993;13:429 –34. 2. Astrug A, Kuleva V, Kiriakov Z, Tomov A, Djingova R. Trace elements in blood and plasma of patients with chronic renal failure treated with maintenance haemodialysis. Trace Elem Med 1984;1:65–70. 3. Van Renterghem D, Cornelis R, Vanholder R. Behaviour of 12 trace elements in serum of uraemic patients on hemodiafiltration, J Trace Elem Electrolytes Health Dis 1992;6:169 –74. 4. Wallaeys B, Cornelis R, Mees L, Lameire N. Trace elements in serum, packed cells, and dialysate of CAPD patients. Kidney Int 1986;30:599 – 604. 5. Cornelis R, Ringoir S, Mees L, Lameire N, Wallaeys B, Hoste J. Trace element patterns in blood of patients with renal failure. In: McHowell J, Gawthorne JM, White CL, eds. Trace element metabolism in man and animals. Canberra: Australian Academy of Science, 1981:530 –3. 6. Zhang X, Cornelis R, De Kimpe J, Mees L, Vanderbiesen V, De Cubber A, Vanholder R. Accumulation of arsenic species in serum of patients with chronic renal disease. Clin Chem 1996;42:1231–7.
7. Jaffe M. Ueber den Niederschlag welchen Pikrinsa¨ure in normalen Harn erzeugt und ueber eine neue Reaktion des Kreatinins. Z Physiol Chem 1886;10:391– 400. 8. Versieck J, Vanballenberghe L. Determination of arsenic and cadmium in human blood serum and packed cells. Trace Elem Man Anim 1985;5: 650 –5. 9. Zhang X, Cornelis R, De Kimpe J, Mees L, Vanderbiesen V, Vanholder R. Total arsenic determination in serum and packed cells of patients with chronic renal insufficiency. Fresenius J Anal Chem 1995;353:143–7.
Lipemia Interference with a Rate-Blanked Creatinine Method, Glen L. Hortin* and Katherine Goolsby (Dept. of Pathol., Univ. of Alabama at Birmingham, 618 S. 18th St., Birmingham, AL 35233; *author for correspondence: fax 205-975-4468, e-mail
[email protected]) The reaction of picric acid (2,4,6-trinitrophenol) with creatinine under strongly alkaline conditions, originally described by Jaffe .100 years ago [1], remains the most common means of measuring creatinine in clinical samples. However, picric acid reacts with many compounds besides creatinine to yield colored products [1–5]. Ketones, glucose, proteins, cephalosporins, and other compounds react to yield positive interferences; also, decreases in the color of bilirubin and hemoglobin under the reaction conditions yield negative interferences [2–7]. To counter these interferences, analysts have tried many variations of the Jaffe reaction, including modifications of sample preparation, addition of oxidizers to remove interferents, solvent extraction of interferents, pH adjustment of the reaction, continuous-flow dialysis, and kinetic measurement of the reaction [2–5]. In kinetic methods, the most common approach to decreasing interferences, the timing of the absorbance readings can be selected to minimize the effects of components that react faster or more slowly than creatinine. For most samples, these modifications of the alkaline–picric acid methods have improved the specificity of measurement. Results have become closer to the “true” creatinine values determined with reference methods [2–5]—improvements in measurement that have led to a downward adjustment of creatinine reference intervals by ;3 mg/L [8, 9] (to express creatinine in mmol/L, multiply mg/L by 8.84). Despite improvements, substantial problems of interference with creatinine measurements remain [2–5]. Recently, a rate-blanking method has been introduced that compensates for absorbance changes of bilirubin and hemoglobin under the strongly alkaline reaction conditions [6, 7]. Although this modification decreases the interference from bilirubin and hemoglobin, we find that it introduces a new interference from lipemic samples. Introduction of a new rate-blanked compensated creatinine method from Boehringer-Mannheim Corp. (Indianapolis, IN) was considered advantageous in our laboratory because the method decreases negative interference from chromogens such as bilirubin and hemoglobin [6, 7]. However, we noted unexpectedly low creatinine values for several patients after introduction of the method. In our clinic laboratory, occasional samples—all lipemic—
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yielded negative values, and other samples yielded creatinine values substantially below what was clinically expected from the patient’s previous values, body mass, and clinical status. Use of some of these measured serum creatinine values to calculate creatinine clearance yielded clearance values of 200 mL/min per 1.73 m2 body surface area or more, which are inconsistent with usual reference values [2, 10]. Nonetheless, the manufacturer claims the method is not subject to significant interference from lipemia, as tested by adding a synthetic lipid emulsion to samples to give a final triglyceride concentration of as much as 10 g/L [7]. At this concentration, lipid emulsions showed a small negative interference (;1 mg/L decrease in creatinine) for creatinine concentrations near the upper limit of normal (;13 mg/L). Any effect ,10% was considered not significant in the evaluation study [7]. To investigate the problem of interference by lipemic samples, we compared creatinine values obtained with three different methods—the rate-blanked compensated method from Boehringer-Mannheim, a compensated kinetic method from Boehringer-Mannheim, and an enzymatic method on the Vitros 700 analyzer from Johnson and Johnson (Rochester, NY)—for 17 lipemic samples received in our clinic laboratory during 1 week. The two Jaffe reactions were performed simultaneously with the same lot of reagents on different channels of the same Hitachi 747 analyzer. Lipemic samples were identified by the lipemic index reported by the instrument (an index of changes in absorbance measurements attributable to light scattering by lipoprotein particles of a specimen diluted in isotonic saline). Because the lipemic index depends on lipoprotein size as well as quantity, it does not correlate exactly with triglyceride concentrations. We also determined triglycerides in some samples as another measure of lipemia, using the Hitachi 747 and a coupled enzymatic method from Boehringer-Mannheim without blanking for glycerol. As listed in Table 1, the rate-blanked compensated method (Jaffe 1) provided values that averaged 5 mg/L lower than another compensated method (Jaffe 2) and 4 mg/L lower than the enzymatic method. For some samples, the difference between the rate-blanked method and the other comparative methods was as much as 10 mg/L. However, there was good agreement between the compensated method and the enzymatic method for the lipemic samples. The magnitude of interference by lipemia in the rateblanked creatinine method did not correlate closely with the lipemic index or triglyceride concentration. One of the problems with this interference is that it is difficult to identify a cutoff point below which interference from lipemia does not occur. We routinely analyze by an alternative method samples with a lipemic index .65; ;0.5–1% of clinic samples have a lipemic index above this value, so several samples per day require alternative analysis. The frequency of lipemic samples from our clinic is likely to be high relative to that for inpatient settings, because many of our samples are from nonfasting patients. This interference has a large potential clinical impact on the measurement of creatinine in the critical
range of 5–15 mg/L, where highly accurate measurements of serum creatinine are needed for calculating creatinine clearance, monitoring renal transplant patients, and detecting early stages of kidney dysfunction from diabetes and other etiologies [2, 10]. Differences between the two Jaffe methods for lipemic samples were not related to differences in calibration. The same calibration material was used, and method comparison studies showed close agreement for nonlipemic samples; analysis of 17 samples with triglyceride ,4 g/L (lipemic index range 5– 63, mean 20) during the same week as analysis of lipemic samples showed no difference .1 mg/L for comparative analyses by the two methods. The interference of lipemic samples in the rate-blanked compensated method appeared to relate to nonlinear changes in measured absorbance by lipoproteins during the analysis. After addition of the first reagent containing NaOH, there was a curvilinear increase of absorbance— rapid at first during the period used to rate-blank the absorbance changes in the sample, but slowing before addition of the second reagent (containing picric acid). Thus, the blanking for baseline absorbance changes was excessive. For the rate-blanking to be accurate, the baseline absorbance changes (without picric acid) would need to be linear throughout the entire reaction. Identifying the problem as involving the rate-blanking step explains why a simpler compensated method using the same reagents does not have similar lipemia interference. Ways to avoid the lipemia interference include using Jaffe methods without rate blanking, using enzymatic methods, or storing samples to allow separation of lipoproteins. Interferences from lipemia are difficult to study, given
Table 1. Comparison of creatinine concentrations in lipemic samples measured by three methods. Concn., mg/L
Mean
Jaffe 1
Jaffe 2
Enzymatic
24 3 11 11 8 14 10 2 13 9 13 6 9 11 18 20 5 9.3
5 11 16 17 12 15 15 14 18 11 17 11 14 15 22 20 11 14.4
6 12 16 16 11 15 13 10 16 11 16 9 11 13 22 20 10 13.4
Lipemic indexa
Triglycerides, g/L
634 400 215 213 139 139 131 120 111 110 104 90 84 81 67 63 63
44.3 .17.0 16.2 7.8 4.9
6.7 5.5
3.1 2.0 14.4
a Lipemic samples were identified by a lipemic index measured with the Hitachi 747 analyzer.
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the lack of a readily available source of lipoproteins for adding to samples. Effects of lipemia are often estimated by using synthetic lipid emulsions, which may or may not accurately replicate the physicochemical properties of actual lipoproteins during analyses. With regard to interference from lipoproteins in a creatinine assay, we found that the effects of lipemia disappeared when samples were stored overnight, even when samples were vigorously vortex-mixed to try to disperse the lipoproteins in the samples. Use of stored samples, as commonly done in many method-evaluation studies, would not detect the interference found in freshly drawn samples. This example illustrates the need to use actual lipemic samples freshly drawn from human subjects when investigating interference from lipemia.
References ¨ ber den Niederschlag welchen Pikrinsa¨ure in normalen Harn 1. Jaffe M. U erzeugt und uber eine neue Reaction des Kreatinins. Z Physiol Chem 1886;10:391– 400. 2. Narayanan S, Appleton HD. Creatinine: a review. Clin Chem 1980;26:1119 – 26. 3. Spencer K. Analytical reviews in clinical biochemistry: the estimation of creatinine. Ann Clin Biochem 1986;23:1–25. 4. Bowers LD, Wong ET. Kinetic serum creatinine assays. II. A critical evaluation and review: the role of various factors in determining specificity. Clin Chem 1980;26:555– 61. 5. Weber JA, van Zanten AP. Interferences in current methods for measurement of creatinine. Clin Chem 1991;37:695–700. 6. Hoffman RJ, Feld RD. A modified procedure for creatinine (CRT) interference which eliminates bilirubin (BILT) interference on the Hitachi 737 [Abstract]. Clin Chem 1988;34:1313. 7. Foster-Swanson A, Swartzentruber M, Nolen PA, Crawford K, Sine HE. Multicenter evaluation of the Boehringer-Mannheim compensated, rateblanked/Jaffe application on BM/Hitachi systems. Adv Clin Diagnostics (Boehringer-Mannheim Corp.) 1993;3–12. 8. Foster-Swanson A, Swartzentruber M, Roberts P, Feld R, Johnson M, Wong S, et al. Reference interval studies of the rate-blanked creatinine/Jaffe method on BM/Hitachi systems in six US laboratories [Abstract]. Clin Chem 1994;40:1057. 9. Painter PC, Cope JY, Smith JL. Reference intervals. In: Burtis CA, Ashwood ER, eds. Tietz textbook of clinical chemistry, 2nd ed. Philadelphia: WB Saunders, 1994:2184 –5.
10. Levey AS, Perrone RD, Madias NE. Serum creatinine and renal function. Annu Rev Med 1988;39:465–90.
Editor’s Note: Kenneth A. Slickers and Harrison E. Sine (Boehringer-Mannheim Corp., Lab Diagnostics, 9115 Hague Rd., P.O. Box 50446, Indianapolis, IN 46250-0446) comment: Hortin and Goolsby have demonstrated not only that the Jaffe creatinine assay remains imperfect (despite all efforts to confer specificity) but also that the interferograph model for assessing interferences has yet another shortcoming. We have previously recognized that the age of the hemolysate can influence hemoglobin interference and that the type of bilirubin can influence the icterus interference. Now, apparently, in addition to the concentration and type of lipids in the sample, even the age of the lipids that give rise to lipemia may also be important in predicting interferences based on turbidity measurements. Clearly, as valuable as the serum indexes are, they are still only indicators of possible interferences, and not quantitative estimates. Studies are currently under development to determine what, if any, improvement can be made in the current Jaffe creatinine assay that will preserve the improved performance vis-a`-vis bilirubin that the rate-blanking introduced, while minimizing this newly recognized influence of endogenous lipemia. We would like to determine more specifically which components of the sample lipids are responsible for the behavior. But we recognize that it is also possible, based on the poor correlation of the bias to the sample lipemia, that the actual interference is due to some other sample constituent, and that the lipemia is simply a marker for this interfering substance. Upon completion of these studies, operating parameters and (or) assay limitation statements will be appropriately amended.
Correction In the Beckman Conference paper by J.B. Silkworth and J.F. Brown, Jr., entitled “Evaluating the impact of exposure to environmental contaminants on human health,” 1996;42:1345–9, insert the underlined words: p. 1345, line 8 of the abstract: “ . . . are also produced, often to greater extents, by naturally occurring constituents . . . .” p. 1345, line 7 of the article: “ . . . (DDT), might have on wildlife and even on human health. The press then popularized the issue and may have been more effective at promoting fear than understanding.” p. 1346, first column, line 9: “ . . . specific cytoplasmic receptor protein known as the aromatic hydrocarbon receptor.” p. 1348, first column, line 11: “ . . . effects produced in humans by the synthetic chemicals of this type are no different from those produced— generally to a much greater extent— by long-accepted natural components of the human environment.” On page 1345, second column, end of first paragraph, replace “ . . . lowest observable effect concentration.” with “ . . . lowest observable effect level.” The final sentence on page 1345 should read: “Some of these congeners are of environmental concern because they tend to bioaccumulate and to produce toxicological effects in heavily dosed test animals [7,9 ], including immunotoxicity, birth defects . . . .”
