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Chronic Kidney Disease |
* Division of Nephrology and Institute of Nephrology, and Department of Clinical Laboratory, The First Hospital, Peking University, Beijing, Department of Nephrology, The First Hospital, ZheJang Medical College, ZheJiang, Department of Nephrology, ShenZhen Hospital, Peking University, ShenZhen, Department of Nephrology, The First Hospital, Sun Yat-sen University, GuangZhou, || Department of Nephrology, The Third Hospital, and ¶ Department of Nephrology, The Fourth Hospital, HeBei Medical University, ShiJia Zhuang, ** Department of Nephrology, HuaXi Hospital, SiChuan University, ChengDu, Department of Nephrology, The First Hospital, China Medical University, ShenYang, and Department of Nephrology, TongRen Hospital, Capital Medical University, Beijing, China
Address correspondence to: Dr. Li Zuo, Division of Nephrology and Institute of Nephrology, The First Hospital, Peking University, No. 8 Xishiku Street, Xicheng District, Beijing, 100034, PR China. Phone: +86-10-66551122 ext. 2388, +86-10-66551072; Fax: +86-10-66551055, +86-10-66551072; E-mail: zuoli{at}bjmu.edu.cn
Received for publication April 19, 2006. Accepted for publication August 2, 2006.
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Abstract |
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 Top Abstract IntroductionMaterials and MethodsResultsDiscussionConclusionAppendixReferences |
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Introduction |
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Creatinine-based estimating equations overcame some of these limitations and offered a rapid method for GFR estimation. In the Modification of Diet in Renal Disease (MDRD) Study, using renal clearance of 125I-iothalamate as a reference GFR (rGFR), Levey et al. (3) published a series of creatinine-based GFR estimating equations (MDRD equations). The abbreviated MDRD equation, which includes only four variables—Pcr, gender, age, and ethnicity (4)—has been the most widely used in clinical practice, becoming a powerful screening tool for early detection of CKD. It provided an acceptable level of accuracy (at least 70% of estimated GFR [eGFR] within a 30% deviation from the rGFR) in advanced stages of CKD (5) and was recommended by Kidney Disease Outcome Quality Initiative (K/DOQI) clinical practice guidelines (5).
Race is an important determinant of GFR estimation. For example, when the MDRD equations are applied to black individuals, a coefficient should be used (3). In our previous study (6), the performance of MDRD equation 7 and the abbreviated MDRD equation was tested in a group of Chinese patients with CKD. The results showed that both equations underestimated rGFR in near-normal renal function and overestimated rGFR in advanced renal failure. We concluded that careful modification of these equations was necessary to improve their performance when used to identify Chinese patients with CKD.
In our study, an attempt was made to improve the performance of the original MDRD equations by modifying the original MDRD equation 7 and abbreviated MDRD equation. The diagnostic performance of the modified equations was compared with the original ones in various stages of CKD.
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Materials and Methods |
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The nine participating renal institutes used the same data collecting methods and the same data collecting forms. The collected data included gender, age, body height, body weight, BP, and rGFR. Fasting plasma was taken from selected patients for analysis of creatinine, urea nitrogen, and albumin in a single laboratory at the First Hospital, Peking University.
GFR Measurement
Unlike Pcr, 99mTc-DTPA plasma clearance was measured in the nine participating renal institutes. Efforts had been made to make the interinstitute variance as small as possible, including staff training, 99mTc-DTPA drug selection (radiochemical purity >95% and percentage of 99mTc-DTPA bound to plasma protein <5%). The identical operational procedures were followed by all nine participating centers, including patients’ preparation, intravenous injection, plasma sampling time points and procedure, and radioactivity measurement (6).
rGFR was measured by the dual plasma sampling method (7,8), standardized by body surface area (BSA) (9), and resulted in the rGFR: rGFR (ml/min per 1.73 m2) = [Dln (P1/P2)/(T2-T1)] exp {[(T1lnP2) – (T2lnP1)]/(T2 – T1)} x 0.93 x 1.73/BSA, where D is dosage of drug injected, T1 is time of first blood sampling (approximately 2 h), P1 is plasma activity at T1, T2 is time of second blood sampling (approximately 4 h), and P2 is plasma activity at T2. The units of measurement were counts per minute per milliliter for D, P1, and P2 and minutes for T1 and T2.
Pcr Assay and Calibration
Pcr levels were measured in a single laboratory on a Hitachi 7600 analyzer using the Jaffe’s kinetic method, which was described elsewhere (6). To ensure that our Pcr values were calibrated equally to the MDRD study, we randomly selected 57 fresh-frozen plasma samples (range 0.72 to 12.64 mg/dl [64 to 1118 µmol/L] of Jaffe’s kinetic method Pcr values measured in our laboratory) from our specimens and analyzed them in both our laboratory and the Cleveland Clinic Laboratory. The Pcr value that was measured by our laboratory can be calibrated to the Pcr value that was measured by the Cleveland Clinic Laboratory, which used a CX3 analyzer (Beckman Coulter Inc., Fullerton, CA), using a linear regression equation: CX3 Pcr (mg/dl) = –15.91 + 1.32 x Hitachi Pcr (mg/dl) (R2 = 0.999; P < 0.001).
Other Analyses
Plasma urea was measured by the urease method. The normal reference range was 3.20 to 7.10 mmol/L [8.96 to 19.88 mg/dl] blood urea nitrogen (BUN). Plasma albumin was measured using the bromcresol green method. The normal reference range was 3.5 to 5.5 g/dl (35 to 55 g/L).
Estimation of GFR from Original MDRD Equations
Calibrated CX3 Pcr was put into the MDRD equation 7 and abbreviated MDRD equation to estimate GFR (7GFR and aGFR, respectively):
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Modification of Original MDRD Equations
A total of 720 participants were included, and 36 outliers were deleted. The remaining 684 patients were used for further analysis. From these patients, 454 were randomly selected and used for the training model, and the remaining 230 patients were used to test the performance of the modified equations.
We assumed that the performances of MDRD equations could be improved in Chinese patients with CKD by adding a racial factor, so 7GFR and aGFR were calculated on the basis of data from the 454 training samples; using 7GFR and aGFR as dependent variables, respectively, two linear regression models were established to predict rGFR from 7GFR or aGFR. It was decided that if the intercepts of the two models were not significantly different from zero, then the models should be simplified by forcing the intercepts to be zero.
In the former two models, the Pcr value that was calibrated to the MDRD laboratory was used to estimate 7GFR and aGFR, so when the two modified equations are used, Pcr value that is calibrated to the MDRD laboratory should be used. This was inconvenient in clinical practice in China. In the above concern, we reconstructed another two regression models, using an approach similar to that used in the development of the original MDRD equations. In these two models, log transformation was applied before the linear regression, and linearity and equal variance test were satisfactory. In the concern that retransforming back to the usual scale might induce bias, the predicted eGFR was adjusted using the smearing method (10). The smearing coefficients for these two models were calculated to be 1.05.
eGFR was compared with rGFR using Bland-Altman analysis of the validation set. The difference between eGFR and rGFR was defined as eGFR minus rGFR; the absolute difference between eGFR and rGFR was defined as the absolute value of difference. The regression of the difference between eGFR and rGFR against the average of the two methods was measured. The bias for eGFR was expressed as the area between the regression line and a common distance along the zero difference line. Ninety-five percent limits of agreement then were constructed around this linear regression line. The precision was expressed as the width between the 95% limits of agreement. Accuracy was measured as the percentage of eGFR that did not deviate >15, 30, and 50% from the rGFR.
Statistical Analyses
Quantitative variables of patient’s age, height, weight, BSA, body mass index, Pcr, plasma urea, plasma albumin, and rGFR were described as mean ± SD or as median (Table 1). The accuracy of the equations was compared in certain stages of CKD with 
2 test. Because of skewed distribution, Spearman correlation and linear regression were used to describe the relationship between eGFR and rGFR. The Wilcoxon signed ranks test was used to compare the difference and absolute difference in a certain stage of CKD. The results were considered to be significant at P < 0.05. Medcalc for Windows, version 8.0 (Medcalc Software, Mariekerke, Belgium) was used for data analysis.
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Results |
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Modification of MDRD Equations
In the first two linear regression, the intercepts of the modified MDRD equation 7 (–0.383; 95% confidence interval [CI] –3.104 to 2.337) and of the modified abbreviated MDRD equation (0.311; 95% CI –2.526 to 3.149) were not significantly different from 0 (P = 0.78 and P = 0.83, respectively). By forcing the two intercepts to be zero, the form of two models was reduced and got the following equations (n = 454, R2 = 0.95 and 0.94 respectively):
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Development of New Equations
Calibrated CX3 Pcr was needed in equations 3 and 4, which were not convenient for clinical application in Chinese, so we tried to reconstruct another two regression models, using Pcr values measured with the Jaffe’s kinetic method on a Hitachi 7600 analyzer. The first model used the same variables as MDRD equation 7, and the second used the same variables as the abbreviated MDRD equation. The two resulted in equations 5 and 6 after adjustment using the smearing method, presented in the Appendix (n = 454; R2 = 0.86 for both).
Diagnostic Performance of the Equations
First, the overall diagnostic performance was compared among equations 1 through 6. Linear regressions were made using eGFR against rGFR. The six intercepts were much similar, but the slopes of equations 3 through 6 were significantly closer to the identical line compared with the slopes of equations 1 and 2). On the Bland-Altman plot, compared with equations 1 and 2, the biases of equations 3 through 6 were much less, and precision of equations 3 through 6 were slightly higher (Table 2, Figure 1). The differences between eGFR resulted from equations 3 through 6, and rGFR were significantly less than the differences that resulted from the other two. Equations 3 and 5 showed fewer absolute differences than equation 1; so did equations 4 and 6 than equation 2. The 15% accuracy of equations 3 through 6 was significantly higher compared with equations 1 and 2, 30% accuracy of equations 4 through 6 was significantly higher than equations 1 and 2; there also was some improvement in the 30% accuracy of equation 3 but without statistically significant. The 50% accuracy was comparable for the six equations. There was no significant difference among equations 3 through 6 in 15 to 50% accuracy (Table 2).
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2 test, P < 0.05; Table 3). In CKD stages 3 through 5, there was no significant difference in the percentages of misclassification among the six equations (2 test, P > 0.05).
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The final equations 9 and 10, based on the values of Pcr measured with a Hitachi 7600 analyzer from our laboratory after smearing adjustment, also are described in the Appendix (n = 684 for both; R2 = 0.86).
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Discussion |
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In our study, all the modified MDRD equations showed lower bias and higher accuracy than the original MDRD equations in each stage of CKD when applied to Chinese patients; particularly in patients with near-normal kidney function, cases of CKD stage 2 that were misdiagnosed as CKD stage 3 by modified equations were less by approximately 40% than those of original MDRD equations. This will help nephrologists to figure out a relatively correct prevalence of CKD and ensure that clinicians make a proper clinical action plan for patients with CKD and avoid unnecessary clinical intervention.
Because there were no significant performance difference among equations 3 through 6 and because the final equations 7 through 10 were based on all patients, which were assumed to be more accurate than equations 3 through 6, we recommend that equations 7 through 10 be used. Because equations 8 and 10 require only one laboratory variable, Pcr, and the GFR estimating process is simplified without decreasing accuracy, for easier application, especially in population screening, equations 8 and 10 are recommended.
There are several methods to measure Pcr in clinical laboratories. Jaffe’s kinetic method on a Hitachi analyzer is the most widely used method in Chinese clinical laboratories. For better practicability, the Jaffe’s kinetic method on a Hitachi analyzer was used our study. For patients with Pcr value as measured on a Hitachi analyzer using the Jaffe’s kinetic method, equation 10 could be used; for patients with Pcr as measured on Beckman analyzers using the Jaffe’s kinetic method, equation 8 could be used.
Recently, some studies (16,17) emphasized the importance of calibration of Pcr. Use of Pcr in MDRD equations requires that the Pcr value be calibrated to the Cleveland Clinic Laboratory value. Failure to do so can introduce a systemic bias in the eGFR, so we think that it is important to calibrate Pcr to the Cleveland Clinic Laboratory value in equation 8; for equation 10, Pcr calibration could be performed in the laboratory at the First Hospital, Beijing University.
There are several reasons for why the modified equations outperformed the original equations. First, there were racial differences, and addition of the Chinese racial factor certainly allowed performance improvement. Furthermore, the rGFR method that was used in our study—plasma clearance of 99mTc-DTPA—was different from that used in the MDRD study—renal clearance of 125I-iothalamate. These two methods may differ from each other compared with inulin clearance (18–20). Therefore, GFR estimation equations that are derived from different rGFR might differ from each other, even in the same group of patients.
Several limitations in our study should be noted. First, according to Levey et al. (16), the Pcr-based equations were derived from the results of multiple regression analysis, their performance best fitted around the observed mean. The original MDRD equations were developed in patients with average GFR of 39.8 ml/min per 1.73 m2; the eGFR would underestimate rGFR in individuals with a higher range of GFR and overestimate rGFR in a group with advanced kidney failure. Although great improvement was achieved, equations 3 through 6 still underestimate GFR when GFR is nearly normal. We modified MDRD equations on the basis of the original MDRD equations and used the same variables and a similar method so that it would not inevitably inherit the same shortcomings of the original equations.
Second, in the modified MDRD equations, Pcr still was the important GFR-predicting variable, so the main, unavoidable pitfall of Pcr-based GFR estimation equations will contribute to the inaccuracy of each equation. It is a fundamentally different relationship between Pcr and GFR in populations with different levels of GFR (16); therefore, different levels of Pcr were not necessarily reflecting the true variation of GFR (21). In near-normal GFR levels, there was no significant decrease of Pcr with the increment of true GFR. However, in advanced kidney failure, with the prominent increment of Pcr, only a slight GFR decrease was detected. Some other potential GFR-predicting variables, such as plasma cystatin C, might be included to improve the performance of GFR-estimating equations, especially in early stages of CKD (22).
Third, because the percentage of patients with CKD that was caused by hypertension and/or diabetes was relatively small in our studied population, the modified equations’ performance in patients with hypertension and/or diabetes needs to be examined further.
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Conclusion |
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Appendix |
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Acknowledgments |
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We are grateful to the Cleveland Clinic Laboratory for calibration of Pcr; without their kind help, this work could not have been accomplished. We express our thanks to the Department of Mathematics, Peking University, for helpful statistical advice. We acknowledge Fresenius Medical Care of China and PUMC Pharmaceutical Co. Ltd. for generous sponsorship.
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