CLINICAL
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TRANSLATIONAL RESEARCH
Comparison of Cystatin C and Creatinine-Based Equations for GFR Estimation After Living Kidney Donation Naim Issa,1 Aleksandra Kukla,1 Scott Jackson,1 Samy M. Riad,2 Meredith C. Foster,3 Arthur J. Matas,4 John H. Eckfeldt,5 and Hassan N. Ibrahim1,6 Background. The performance of glomerular filtration rate (GFR) equations incorporating both cystatin C (CysC) and serum creatinine (Creat) in living kidney donors has not been studied before. Methods. From a population of 3,698 living kidney donors, 257 donors were randomly selected to undergo GFR measurement (mGFR) by the plasma disappearance of iohexol. GFR was estimated with the Modification of Diet in Renal Disease (MDRD) equation and the Chronic Kidney Disease Epidemiology Collaboration study eGFR(CKDEPI-Creat) in 257 donors and the two newly developed equations using CysC with and without Creat, eGFR(CKDEPI-CysC) and eGFR(CKD-EPI-Creat+CysC), in 215 donors. Results. Mean mGFR was 71.8T11.8 mL/min/1.73 m2. The eGFR(MDRD) exhibited least and only negative bias and the three other models were comparable in terms of bias. The eGFR(CKD-EPI-Creat+CysC) equation was most precise; r2=0.64. Both eGFR(MDRD) and eGFR(CKD-EPI-Creat+CysC) had high percentage (94.4% and 92.6%, respectively) of estimates falling within 30% of mGFR versus estimates by eGFR(CKD-EPI-Creat) and eGFR(CKDEPI-CysC) equations (87.2% and 85.1%, respectively). The eGFR(MDRD) was by far most accurate in identifying those with mGFR less than 60 mL/min/1.73 m2 whereas the CKD-EPI models were extremely accurate in classifying those with mGFR greater than or equal to 60 mL/min/1.73 m2. Conclusions. eGFR(CKD-EPI-Creat+CysC) equation provides comparable accuracy to the eGFR(MDRD) in overall estimation of mGFR, but with higher precision. However, eGFR(CKD-EPI-Creat+CysC) clearly misses many of those with a post-donation GFR less than 60 mL/min/1.73 m2 and therefore eGFR(MDRD) is preferable in detecting donors with GFR less than 60 mL/min/1.73 m2. Keywords: Glomerular filtration rate, Chronic Kidney Disease Epidemiology Collaboration, Modification of diet in renal disease, Cystatin C, Living kidney donor, Kidney transplantation. (Transplantation 2014;98: 871Y877)
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idney donors enjoy a favorable renal outcome, and the risk of end-stage renal disease in carefully screened kidney donors seems to be similar to the general population
(1Y3). Serum creatinine (Creat)Ybased GFR estimation equations may not accurately estimate true GFR in the setting of uninephrectomy in healthy donors (4Y6). We have previously
N.I. and A.K. contributed equally to this work and are considered first authors. The authors declare no conflicts of interest. 1 Division of Renal Diseases and Hypertension, Department of Medicine, University of Minnesota, Minneapolis, MN. 2 Division of Nephrology, University of Alabama, Birmingham, AL. 3 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD. 4 Division of Transplant Surgery, Department of Surgery, University of Minnesota, Minneapolis, MN. 5 Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN. 6 Address correspondence to: Hassan N. Ibrahim, M.D., M.S., Division of Renal Diseases and Hypertension, University of Minnesota, 717 Delaware Street SE, Suite 353, Mail Code 1932, Minneapolis, MN 55414. E-mail: [email protected] N.I. participated in the research design, data analysis, and writing and final approval of the article. A.K. participated in the research design, data analysis, and writing and final approval of the article. S.J. participated in data analysis, and reviewed and contributed to the final approval of
the article. S.M.R. contributed to writing, reviewing, and final approval of the article. M.C.F. participated in data analysis, and reviewed and contributed to the final approval of the article. A.J.M. contributed to writing, reviewing, and final approval of the article. J.H.E. contributed to assays analysis and standardizations, research design, and reviewed and contributed to the final approval of the article. H.N.I. participated in the research design, data analysis, and writing and final approval of the article. Parts of these analyses were presented in a poster form at the 2013 American Transplant Congress in Seattle, WA, USA. Supplemental digital content (SDC) is available for this article. Direct URL citations appear in the printed text, and links to the digital files are provided in the HTML text of this article on the journal’s Web site (www.transplantjournal.com). Received 2 December 2013. Revision requested 22 December 2013. Accepted 23 February 2014. Copyright * 2014 by Lippincott Williams & Wilkins ISSN: 0041-1337/14/9808-871 DOI: 10.1097/TP.0000000000000129
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reported on the performance of these equations in a large cohort of living kidney donors who underwent formal iohexol glomerular filtration rate (mGFR) measurement and found that the re-expressed Modification of Diet in Renal Disease (MDRD) equation could be a reasonable substitute, albeit still suboptimal and is clearly inferior to formal GFR measurements (7, 8). Cystatin C (CysC) has emerged as an alternate and perhaps more reliable filtration marker for estimating GFR, and the recently introduced combined Creat-CysC equation performs better than equations based on either of these markers alone (9Y12). Herein, we address the performance of those new models of GFR estimation in kidney donors compared to old models.
RESULTS Donor Characteristics Donor characteristics are shown in Table 1. Sixty-one percent of donors were female, 99.2% were Caucasian, 24.5% reported a diagnosis of hypertension, and 3.1% reported a diagnosis and treatment for diabetes mellitus with oral hypoglycemic agents or insulin, or both. Mean Creat and CysC at the time of GFR measurement were 1.0T0.2 mg/dL and 1.0T0.1 mg/L, respectively. Eighty-four percent of donors had an mGFR greater than 60 mL/min/1.73 m2, 16% had an mGFR between 30 and 60 mL/min/1.73 m2 and none had an mGFR of less than 30 mL/min/1.73 m2. Moreover, based on first void urinary albumin-to-creatinine ratio and standard definitions, 87.3% of donors were normoalbuminuric, 11.5% were microalbuminuric, and only 1.2% were macroalbuminuric. Importantly, none of the donors had both mGFR less than 45 mL/min/1.73 m2 and albuminuria.
TABLE 1.
Characteristics of the study population
No. donors Age at donation, yr Age at iohexol GFR measurement, yr Time from donation, yr Female White BMI at GFR measurement, kg/m2 Hypertensiona Diabetes mellitusa Serum creatinine at donation, mg/dL Serum creatinine at GFR measurement, mg/dL Serum cystatin C at GFR measurement, mg/Lb GFR, mL/min/1.73 m2 Iohexol (mGFR) eGFR (MDRD) eGFR (CKD-EPI-Creat) eGFR (CKD-EPI-CysC)b eGFR (CKD-EPI-Creat+CysC)b
257 41.0T11.0 52.9T9.8 11.9T9.0 61.1% 99.2% 28.1T4.9 24.5% 3.1% 0.9T0.1 1.0T0.2 1.0T0.1
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Comparison of Estimated GFR by Four Equations With mGFR The mean mGFR in the 257 donors was 71.9T 11.9 mL/min/1.73 m2, compared to 71.6T14.9 mL/min/ 1.73 m2 by eGFR(MDRD), 78.4T16.3 mL/min/1.73 m2 by eGFR(CKD-EPI-Creat), 81.5T14.2 mL/min/1.73 m2 by eGFR(CKD-EPI-CysC), and 79.5T13.6 mL/min/1.73 m2 by eGFR(CKD-EPI-Creat+CysC) (Table 1). Indices of bias (by both mean difference and mean percent difference), precision, and accuracy (by percentage of estimates falling within 10% and 30% of mGFR) of these equations in relation to mGFR are depicted in Table 2, whereas Figure 1 represents the graphical relationships between eGFR and mGFR for each equation using Bland-Altman plots that depict the distribution of errors in estimation of mGFR with eGFR when a given eGFR value is observed; also shown are Deming regression parameters and histograms and density curves of the biases. eGFR(MDRD) The eGFR(MDRD) very closely estimates mGFR with a bias of only j0.3T11.7 mL/min/1.73 m2 (P=0.64) and a relative bias of 12.9T10.6% (Table 2). The interquartile range was 15.7, and the Deming r2 and root mean square error (RMSE) was 0.41 and 8.31. The eGFR(MDRD) estimates fell within 10% and 30% of mGFR in 45.1% and 94.2% of the cases, respectively (Table 2). eGFR(CKD-EPI-Creat) The eGFR(CKD-EPI-Creat) on average overestimated mGFR with a bias of 6.5T12.1 mL/min/1.73 m2 (PG0.01) and a relative bias of 15.6T9.8%. The interquartile range was 17.7, and the Deming R2 and RMSE was 0.45 and 8.08. The eGFR(CKD-EPI-Creat) was slightly less accurate than the GFR(MDRD) with estimates falling within 10% and 30% of mGFR in 39.3% and 87.2% of the cases, respectively (Table 2). eGFR(CKD-EPI-CysC) The eGFR(CKD-EPI-CysC) exhibited the highest bias and overestimating mGFR by 9.8T10.4 mL/min/1.73 m2 (PG0.01) with a relative bias of 16.8T13.1% (Table 2, Fig. 1C). The interquartile range was 13.5, and the Deming R2 and RMSE was 0.48 and 7.53. eGFR(CKD-EPI-CysC) was also least numerically accurate with estimates falling within 10% and 30% of mGFR in 36.7% and 85.1% of the cases, respectively (Table 2).
71.9T11.9 71.6T14.9 78.4T16.3 81.5T14.2 79.5T13.6
eGFR(CKD-EPI-Creat+CysC) The eGFR(CKD-EPI-Creat+CysC) overestimated mGFR with an absolute bias of 7.9T9.4 mL/min/1.73 m2 (PG0.01) and a relative bias of 13.6T10.5% (Table 2, Fig. 1C). The interquartile range was 9.4, and the Deming R2 and RMSE was 0.64 and 5.95. The eGFR(CKD-EPI-Creat+CysC) was less accurate than eGFR(MDRD) with estimates falling within 10% and 30%of mGFR in 42.3% and 92.6% of the cases, respectively (Table 2). However, by far it had the best precision of mGFR with a R2 of 0.64.
Self-reported and defined as requiring treatment by study participants. n=215. BMI, body mass index; GFR, glomerular filtration rate; MDRD, Modification of Diet in Renal Disease; Creat, creatinine; CysC, serum cystatin C; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration.
Effects of Body Mass Index on the eGFR Models Performance We then sought to analyze the performance of the various models in donors with obesity (body mass index
a b
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Issa et al.
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TABLE 2. Overall performance of estimating GFR models, and stratified by BMI (G30 vs. Q30 kg/m2) and age at GFR measurement (G50 vs. Q50 years) n=257a
Bias (SD) (P value)
eGFR method eGFR (MDRD) eGFR (CKD-EPI-Creat) eGFR (CKD-EPI-CysC) eGFR (CKD-EPI-Creat+CysC) BMI G30 (n=179) eGFR (MDRD) eGFR (CKD-EPI-Creat) eGFR (CKD-EPI-CysC) eGFR (CKD-EPI-Creat+CysC) BMI Q30 (n=78) eGFR (MDRD) eGFR (CKD-EPI-Creat) eGFR (CKD-EPI-CysC) eGFR (CKD-EPI-Creat+CysC) Age G50 (n=105) eGFR (MDRD) eGFR (CKD-EPI-Creat) eGFR (CKD-EPI-CysC) eGFR (CKD-EPI-Creat+CysC) Age Q50 (n=152) eGFR (MDRD) eGFR (CKD-EPI-Creat) eGFR (CKD-EPI-CysC) eGFR (CKD-EPI-Creat+CysC)
j0.3 6.5 9.8 7.9
IQR
Relative bias (%)
R2b
RMSE
Within 10% of mGFR (%)
Within 30% of mGFR (%)
(11.7) (0.64) (12.1) (G0.01) (10.4) (G0.01) (8.3) (G0.01)
15.7 17.7 13.5 9.4
12.9 15.6 16.8 13.6
(10.6) (9.8) (13.1) (10.5)
0.41 0.45 0.48 0.64
8.3 8.1 7.5 6.0
116 101 79 91
(45.1) (39.3) (36.7) (42.3)
242 (94.2) 224 (87.2) 183 (85.1) 199 (92.6)
j1.4 (11.5) (0.11) 5.7 (11.9) (G0.01) 11.0 (9.5) (G0.01) 8.3 (7.7) (G0.01)
15.8 17.2 12.5 9.8
12.8 14.6 17.3 13.2
(9.5) (12.1) (12.8) (10.2)
0.40 0.44 0.52 0.65
9.3 9.0 8.2 7.0
80 79 55 65
(44.7) (44.1) (36.7) (43.3)
171 (95.5) 156 (87.2) 126 (84.0) 138 (92.0)
(11.8) (0.13) (12.5) (G0.01) (11.8) (G0.01) (9.4) (G0.01)
14.7 18.0 15.6 7.2
13.3 17.9 15.8 14.6
(12.8) (14.7) (13.8) (11.3)
0.47 0.49 0.42 0.61
8.5 8.3 9.2 7.6
36 22 24 26
(46.2) (28.2) (36.9) (40.0)
j1.3 (12.4) (0.29) 8.0 (13.0) (G0.01) 11.8 (11.1) (G0.01) 9.3 (8.1) (G0.01)
17.2 20.2 15.3 11.0
13.0 16.5 17.6 13.8
(10.9) (13.8) (15.1) (11.8)
0.35 0.36 0.33 0.57
9.1 9.1 9.2 7.3
47 38 37 40
(44.8) (36.2) (40.7) (44.0)
100 (95.2) 91 (86.7) 73 (80.2) 83 (91.2)
13.7 15.5 12.3 10.2
12.9 14.9 16.3 13.4
(10.4) (12.4) (11.5) (9.6)
0.37 0.38 0.42 0.55
8.3 8.2 7.9 7.0
69 63 42 51
(45.4) (41.5) (33.9) (41.1)
142 (93.4) 133 (87.5) 110 (88.7) 116 (93.6)
2.0 8.4 7.0 6.9
0.3 5.5 8.4 6.8
(11.1) (0.73) (11.4) (G0.01) (9.7) (G0.01) (8.2) (G0.01)
71 68 57 61
(91.0) (87.2) (87.7) (93.9)
a
n=215 for cystatin CYbased formulas. R2 and RMSE from Deming regression. IQR, interquartile range; RMSE, root mean square error. b
[BMI] Q30 kg/m2) compared to non-obese donors, as the obese subgroup may be particularly prone to inaccurate estimation of renal function. Our cohort comprised 78 living donors with a current BMI greater than or equal to 30 kg/m2. There was a significant difference in bias between the two BMI groups for the eGFR(MDRD) (P=0.03); however, one bias was negative and the other was positive so not probably clinically useful (Table 2). The bias of the eGFR(CKDEPI-CysC) is significantly higher in those with BMI less than 30 kg/m2 (P=0.01). However, there was no significant difference for eGFR(CKD-EPI-Creat) (P=0.10) or eGFR (CKD-EPI-Creat+CysC) (P=0.25) between the two BMI groups (Table 2). Effects of Donor Age on the eGFR Models’ Performance Additionally, we stratified results based on age at time of GFR measurement (G50 vs. Q50 years), which comprised 105 and 152 donors, respectively (Table 2). Bias in donors younger than 50 years was significantly higher than those 50 years or older for the eGFR(CKD-EPI-CysC) and eGFR (CKD-EPI-Creat+CysC) formulas (P=0.02 in both cases), but the difference was trivial and probably not clinically significant. For eGFR(MDRD) and eGFR(CKD-EPI-Creat), there
was no significant difference in bias (P=0.28 and P=0.10, respectively). Importantly, other than the noted small difference in bias, relative accuracy and precision were comparable for all models in both age groups. Correctly Categorizing mGFR (G60 mL/min/1.73 m2 vs. Q60 mL/min/1.73 m2) Given that structural kidney damage is not required to establish the diagnosis of CKD stage 3, and the fact that an estimated GFR level of less than 60 mL/min/1.73 m2 is not infrequently encountered in living kidney donors, we sought to examine the performance of the four equations in their ability to predict and correctly categorize mGFR when it is less than 60 mL/min/1.73 m2 or greater than or equal to 60 mL/min/1.73 m2 (Fig. 2, Table 3). The eGFR (MDRD) and eGFR(CKD-EPI-Creat) equations performed best for donors with lower mGFR (G60 mL/min/1.73 m2) with 67.6% and 43.2% of estimated GFR correctly categorized in the less than 60 mL/min/1.73 m2, respectively. On the other hand, the eGFR(CKD-EPI-CysC) and eGFR(CKDEPI-Creat+CysC) equations performed best for donors with higher mGFR (Q60 mL/min/1.73 m2) with 98.3% and 97.2% of estimated GFR correctly categorized as greater than or equal to 60 mL/min/1.73 m2 range, respectively. Kappa
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FIGURE 1. Bland-Altman plots showing the distribution of errors in estimation of mGFR with eGFR when a given eGFR value is observed; Deming regression plots with equivalence line, and histograms and density curves of the distribution of the difference (bias) between eGFR and mGFR for (A) eGFR (MDRD), (B) eGFR (CKD-EPI-Creat), (C) eGFR (CKD-EPI-CysC), and (D) eGFR (CKD-EPI-Creat+CysC).
statistics ranged from 0.33 to 0.41, indicating slight to moderate agreement (Table 3).
DISCUSSION Our results indicate that eGFR(MDRD) and eGFR(CKDEPI-Creat+CysC) equations exhibit less bias and better
prediction of mGFR than eGFR(CKD-EPI-Creat) and eGFR (CKD-EPI-CysC) equations, and the combined equation had the highest precision. Importantly, eGFR(MDRD) was the best model in detecting those with true GFR less than 60 mL/min/1.73 m2. In clinical practice, it is not uncommon for living kidney donors to have a slightly higher serum Creat. The application of creatinine-based GFR formulas to healthy
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FIGURE 2. Graphs representing the percentage agreement of the four different GFR equations, eGFR(MDRD), eGFR(CKD-EPI-Creat), eGFR(CKD-EPI-CysC), and eGFR(CKDEPI-Creat+CysC), with mGFR (Q or G60 mL/min/1.73 m2).
living kidney donors before and after donation generated a lot of controversies, and those creatinine-based GFR estimates are relatively imprecise (5Y7, 13Y20). The analytical accuracy of CysC testing has recently been greatly improved by the calibration of CysC laboratory assays to an international reference material (21, 22), and therefore utilizing both serum Creat and CysC should be more accurate and precise than equations using either component alone (12) as shown in our present study. In this study, we built upon our previously published observations examining the performance of those newly developed formulas in living kidney donors. The results of our study add to current knowledge, as GFR assessment was obtained at different times post-donation (range 3Y45 years). We observed higher precision with the eGFR(CKD-EPICreat+CysC). One could argue that a more precise estimating equation represents mainly the strength of the linear association between estimated and measured GFR, and it does not provide clinically applicable information on the overall performance of the equation. On the other hand, bias and accuracy provide the clinician with better tools to assess the performance of estimating equations. We also observed a higher accuracy with the eGFR(CKD-EPI-Creat+CysC) and eGFR(MDRD) equation compared to eGFR(CKD-EPICreat) and eGFR(CKD-EPI-CysC). An estimating equation with combined higher accuracy and precision would be ideal for use in the setting of living kidney donation when measured GFR is generally preserved. Although it was not surprising that the eGFR(MDRD) performed best when mGFR was less than 60 mL/min/1.73 m2, as it was derived from populations with CKD, the GFR(CKD-EPI-Creat+CysC) equation was as accurate but more precise than the eGFR(MDRD) at higher levels of mGFR, as our cohort of living donors had a mean mGFR of 71.8 mL/min/1.73 m2, similar to the study by Inker et al. (12). We acknowledge several limitations. This is a retrospective single center study. However, unlike previously published studies, it includes a large cohort of living kidney donors in whom iohexol GFR was obtained. Nonetheless, the cross-sectional nature of this study is a major shortcoming. It remains therefore unknown if the performance and correlation of the various GFR assessment methodologies change for a particular donor over time. In that regard, the
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wide span of post-donation periods herein may impede interpretation of the results as different GFR estimating models may perform differently at different time points post-donation. In addition, the study population is 99.2% Caucasian, so rendering the generalization to a larger pool of live donors derived from the diverse ethnic groups would be rather difficult. Our findings need to be validated in donors of other racial and ethnic groups such as African Americans and Hispanics. Although we selected donors randomly for formal GFR measurement, there were significant differences in this population when compared to the donor population as a whole: the donors with measured GFR were older and had a shorter period of elapsed time since donation. We also recognize the relative limitations of using a single measurement of GFR by iohexol and of serum creatinine and cystatin C that may affect reproducibility of the estimating equations. There are also inherent limitations of the biostatistical methods used herein (such as bias, accuracy, and precision) in quantitatively testing the performance of GFR estimating equations especially for the precision that mainly describes how close the values of repeated measurements are. However, in the absence of better alternatives, those analyses have been widely recognized as standard statistical methodology for studies validating new measuring tools against a reference technique (23). Our analysis is novel as it is the first study comparing the newly developed equations incorporating both CysC and Creat (CKD-EPI 2012 cystatin C and creatinine-cystatin C equations) to the widely used MDRD equation and the CKDEPI 2009 creatinine equation in a cohort of living donors who donated 3 to 45 years ago. Nonetheless, it has been shown that serum Cystatin C can be influenced by factors other than renal function, such as C-reactive protein levels and cigarette smoking. Because many of our living donors are several years post-donation, the confounding of their current health status, tobacco smoking, and C-reactive protein level may have also affected outcomes. Another limitation is that only 215 of 257 donors had available CysC levels. What Should the Clinician Do? Taken together, eGFR(CKD-EPI-Creat+CysC) equation provides comparable accuracy to the eGFR(MDRD) in overall estimation of measured GFR but with higher precision. However, we do not recommend the widespread use of the eGFR(CKD-EPI-Creat+CysC) equation alone post-living kidney donation, as donors with low post-donation GFR (although a minority) may go unrecognized. A reasonable TABLE 3. Correct categorization at mGFRa threshold of 60 mL/min/1.73 m2 % Matched with mGFR Formula/GFR category eGFR(MDRD) eGFR(CKD-EPI-Creat) eGFR(CKD-EPI-CysC) eGFR(CKD-EPI-Creat+CysC) a
mGFR G60 mGFR Q60 Kappa (specificity) (sensitivity) statistic 67.6 43.2 25.7 28.6
mGFR=measured GFR (mL/min/1.73 m2).
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82.7 94.1 98.3 97.2
0.39 0.41 0.33 0.34
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approach in this population would be that one would start with estimating GFR with the MDRD study equation, and if the estimate is less than 60 mL/min/1.73 m2, one would not use other estimating models. If the MDRD estimate, however, is greater than 60 ml/min/1.73 m2, the eGFR (CKD-EPI-Creat+CysC) should be used to provide a more precise estimate.
MATERIALS AND METHODS Between 1963 and 2007, 3,698 living donors donated a kidney at the University of Minnesota. Utilizing the Social Security Death Master File, we found that 3,404 are alive, 268 have died, and 26 of the donors were foreign nationals and were thus untraceable. Alive and traceable kidney donors were stratified by gender and years from donation in 3-year intervals. A random start was used to generate random numbers using a SAS macro to select a similar portion of donors (5%Y10%) from each stratum to undergo GFR measurements. In total, 257 donors of 2,199 who returned health status updates had laboratory testing and were randomly selected for iohexol GFR measurement (mGFR). The 257 donors were similar to donors without GFR measurement on current age and gender distribution. The study was approved by the University of Minnesota Institutional Review Board. GFR was measured using the plasma disappearance of iohexol, as previously described (18, 24, 25). The plasma disappearance method was chosen because of its excellent correlation with inulin clearance and does not require timed urine collection which reduces the variability in GFR measurements (25, 26). The plasma iohexol disappearance profile was analyzed by the one-compartment model and corrected with the BrochnerMortenson formula with all data points fitted by nonlinear regression. This correction produces a correlation with inulin GFR that is virtually identical to the two-compartment model. The coefficient of variation of the iohexol GFR method was consistently less than 10%. Serum Creat and CysC were obtained the morning of the iohexol GFR measurement and after an 8- to 12-hr fast.
Standardization of Serum Creatinine and Cystatin C Serum Creat was measured using a Roche Modular P and Roche enzymatic creatinine reagents, which are traceable to the Isotope Dilution Mass Spectrometry (IDMS) method of the National Institute of Standards and Technology as described in detail in our previous publications (7, 27). Likewise, we used the standardized serum Cystatin C traceable to the International Federation of Clinical Chemistry Working Group for standardization of Serum Cystatin C and the Institute for Reference Materials and Measurements certified reference materials (21, 22). CysC was measured using the PENIA cystatin C kit on a ProSpec nephelometer (Siemens) from blood specimens stored at j70-C. A freeze-thaw study on CysC showed very good correlation between frozen and fresh serum (once frozen as y-axis and fresh as x-axis ordinary least-squares [OLS] regression was y=1.015, x=j0.0213). Multiple freeze-thaws were not investigated. Furthermore, it was recently advocated to use the re-expressed CKD-EPI-Cystatin C equations to reduce variability in estimated GFR by CKD-EPI Cystatin C (11). These CysC values were in alignment with the ERM DA-471/IFCC international CysC reference material, which is needed for use in the most recent CKD-EPI equations published in 2012 (12). Serum CysC levels were available in 215 donors. We evaluated the performance of four GFR estimating models (Table S1, SDC, http://links.lww.com/TP/A973): 1. The IDMS-traceable re-expressed MDRD study equation; eGFR(MDRD); 2. CKD-EPI-Creatinine; eGFR(CKD-EPI-Creat) 3. CKD-EPI-Cystatin C; eGFR(CKD-EPI-CysC) 4. CKD-EPI-Creatinine and Cystatin C; eGFR(CKD-EPI-Creat+CysC)
Statistical Analysis We assessed the performance of these four equations against mGFR in several ways: Bias: the average prediction error=3 (eGFRjmGFR)/n; relative
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bias (percent deviation from the mGFR) was also calculated. Precision: the value of r2 and RMSE from the regression of mGFR on eGFR and the interquartile range of differences. Relative accuracy: the percent of estimates falling within 10% and 30% of mGFR. Bland-Altman plots were created for all equations (23). Dashed lines shown represent T2 standard deviations. Deming regression plots with slope, intercept, R2, and RMSE are also presented. Deming regression is utilized instead of OLS regression method because there is considerable error in both the estimated and also measured GFR. Finally, a density plot reflecting the distribution of the residuals (i.e., biases) is presented for each estimating equation. Statistical analysis was performed in SAS (ver. 9.3; SAS Institute Inc., Cary, NC, USA) and Stata v12.1 was used for Deming regression analysis.
ACKNOWLEDGMENTS The authors would like to thank all living kidney donors and the living kidney donors’ coordinators at the University of Minnesota, Minneapolis, MN. REFERENCES 1.
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Budisavljevic MN, Nietert PJ, Zhai Y, et al. Long-term effects of kidney donation on renal function and blood pressure in African Americans. Clin J Am Soc Nephrol 2011; 6: 1474. Cherikh WS, Young CJ, Kramer BF, et al. Ethnic and gender related differences in the risk of end-stage renal disease after living kidney donation. Am J Transplant 2011; 11: 1650. Ibrahim HN, Foley R, Tan L, et al. Long-term consequences of kidney donation. N Engl J Med 2009; 360: 459. Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009; 150: 604. Tent H, Rook M, Stevens LA, et al. Renal function equations before and after living kidney donation: a within-individual comparison of performance at different levels of renal function. Clin J Am Soc Nephrol 2010; 5: 1960. Tan JC, Ho B, Busque S, et al. Imprecision of creatinine-based GFR estimates in uninephric kidney donors. Clin J Am Soc Nephrol 2010; 5: 497. Sebasky M, Kukla A, Leister E, et al. Appraisal of GFR-estimating equations following kidney donation. Am J Kidney Dis 2009; 53: 1050. Levey AS, Coresh J, Greene T, et al. Expressing the Modification of Diet in Renal Disease Study equation for estimating glomerular filtration rate with standardized serum creatinine values. Clin Chem 2007; 53: 766. Tangri N, Stevens LA, Schmid CH, et al. Changes in dietary protein intake has no effect on serum cystatin C levels independent of the glomerular filtration rate. Kidney Int 2011; 79: 471. Stevens LA, Schmid CH, Greene T, et al. Factors other than glomerular filtration rate affect serum cystatin C levels. Kidney Int 2009; 75: 652. Inker LA, Eckfeldt J, Levey AS, et al. Expressing the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) cystatin C equations for estimating GFR with standardized serum cystatin C values. Am J Kidney Dis 2011; 58: 682. Inker LA, Schmid CH, Tighiouart H, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med 2012; 367: 20. Rule AD, Gussak HM, Pond GR, et al. Measured and estimated GFR in healthy potential kidney donors. Am J Kidney Dis 2004; 43: 112. Issa N, Stephany B, Fatica R, et al. Donor factors influencing graft outcomes in live donor kidney transplantation. Transplantation 2007; 83: 593. Lujan PR, Chiurchiu C, Douthat W, et al. CKD-EPI instead of MDRD for candidates to kidney donation. Transplantation 2012; 94: 637. Velosa JA, Offord KP, Schroeder DR. Effect of age, sex, and glomerular filtration rate on renal function outcome of living kidney donors. Transplantation 1995; 60: 1618. Poggio ED, Braun WE, Davis C. The science of Stewardship: due diligence for kidney donors and kidney function in living kidney donationVevaluation, determinants, and implications for outcomes. Clin J Am Soc Nephrol 2009; 4: 1677.
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Ibrahim HN, Rogers T, Tello A, et al. The performance of three serum creatinine-based formulas in estimating GFR in former kidney donors. Am J Transplant 2006; 6: 1479. Barri YM, Parker T 3rd, Daoud Y, et al. Definition of chronic kidney disease after uninephrectomy in living donors: what are the implications? Transplantation 2010; 90: 575. Issa N, Meyer KH, Arrigain S, et al. Evaluation of creatinine-based estimates of glomerular filtration rate in a large cohort of living kidney donors. Transplantation 2008; 86: 223. Grubb A, Blirup-Jensen S, Lindstrom V, et al. First certified reference material for cystatin C in human serum ERM-DA471/IFCC. Clin Chem Lab Med 2010; 48: 1619. Blirup-Jensen S, Grubb A, Lindstrom V, et al. Standardization of Cystatin C: development of primary and secondary reference preparations. Scand J Clin Lab Invest Suppl 2008; 241: 67.
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TRANSLATIONAL RESEARCH
Comparison of Cystatin C and Creatinine-Based Equations for GFR Estimation After Living Kidney Donation Naim Issa,1 Aleksandra Kukla,1 Scott Jackson,1 Samy M. Riad,2 Meredith C. Foster,3 Arthur J. Matas,4 John H. Eckfeldt,5 and Hassan N. Ibrahim1,6 Background. The performance of glomerular filtration rate (GFR) equations incorporating both cystatin C (CysC) and serum creatinine (Creat) in living kidney donors has not been studied before. Methods. From a population of 3,698 living kidney donors, 257 donors were randomly selected to undergo GFR measurement (mGFR) by the plasma disappearance of iohexol. GFR was estimated with the Modification of Diet in Renal Disease (MDRD) equation and the Chronic Kidney Disease Epidemiology Collaboration study eGFR(CKDEPI-Creat) in 257 donors and the two newly developed equations using CysC with and without Creat, eGFR(CKDEPI-CysC) and eGFR(CKD-EPI-Creat+CysC), in 215 donors. Results. Mean mGFR was 71.8T11.8 mL/min/1.73 m2. The eGFR(MDRD) exhibited least and only negative bias and the three other models were comparable in terms of bias. The eGFR(CKD-EPI-Creat+CysC) equation was most precise; r2=0.64. Both eGFR(MDRD) and eGFR(CKD-EPI-Creat+CysC) had high percentage (94.4% and 92.6%, respectively) of estimates falling within 30% of mGFR versus estimates by eGFR(CKD-EPI-Creat) and eGFR(CKDEPI-CysC) equations (87.2% and 85.1%, respectively). The eGFR(MDRD) was by far most accurate in identifying those with mGFR less than 60 mL/min/1.73 m2 whereas the CKD-EPI models were extremely accurate in classifying those with mGFR greater than or equal to 60 mL/min/1.73 m2. Conclusions. eGFR(CKD-EPI-Creat+CysC) equation provides comparable accuracy to the eGFR(MDRD) in overall estimation of mGFR, but with higher precision. However, eGFR(CKD-EPI-Creat+CysC) clearly misses many of those with a post-donation GFR less than 60 mL/min/1.73 m2 and therefore eGFR(MDRD) is preferable in detecting donors with GFR less than 60 mL/min/1.73 m2. Keywords: Glomerular filtration rate, Chronic Kidney Disease Epidemiology Collaboration, Modification of diet in renal disease, Cystatin C, Living kidney donor, Kidney transplantation. (Transplantation 2014;98: 871Y877)
K
idney donors enjoy a favorable renal outcome, and the risk of end-stage renal disease in carefully screened kidney donors seems to be similar to the general population
(1Y3). Serum creatinine (Creat)Ybased GFR estimation equations may not accurately estimate true GFR in the setting of uninephrectomy in healthy donors (4Y6). We have previously
N.I. and A.K. contributed equally to this work and are considered first authors. The authors declare no conflicts of interest. 1 Division of Renal Diseases and Hypertension, Department of Medicine, University of Minnesota, Minneapolis, MN. 2 Division of Nephrology, University of Alabama, Birmingham, AL. 3 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD. 4 Division of Transplant Surgery, Department of Surgery, University of Minnesota, Minneapolis, MN. 5 Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN. 6 Address correspondence to: Hassan N. Ibrahim, M.D., M.S., Division of Renal Diseases and Hypertension, University of Minnesota, 717 Delaware Street SE, Suite 353, Mail Code 1932, Minneapolis, MN 55414. E-mail: [email protected] N.I. participated in the research design, data analysis, and writing and final approval of the article. A.K. participated in the research design, data analysis, and writing and final approval of the article. S.J. participated in data analysis, and reviewed and contributed to the final approval of
the article. S.M.R. contributed to writing, reviewing, and final approval of the article. M.C.F. participated in data analysis, and reviewed and contributed to the final approval of the article. A.J.M. contributed to writing, reviewing, and final approval of the article. J.H.E. contributed to assays analysis and standardizations, research design, and reviewed and contributed to the final approval of the article. H.N.I. participated in the research design, data analysis, and writing and final approval of the article. Parts of these analyses were presented in a poster form at the 2013 American Transplant Congress in Seattle, WA, USA. Supplemental digital content (SDC) is available for this article. Direct URL citations appear in the printed text, and links to the digital files are provided in the HTML text of this article on the journal’s Web site (www.transplantjournal.com). Received 2 December 2013. Revision requested 22 December 2013. Accepted 23 February 2014. Copyright * 2014 by Lippincott Williams & Wilkins ISSN: 0041-1337/14/9808-871 DOI: 10.1097/TP.0000000000000129
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reported on the performance of these equations in a large cohort of living kidney donors who underwent formal iohexol glomerular filtration rate (mGFR) measurement and found that the re-expressed Modification of Diet in Renal Disease (MDRD) equation could be a reasonable substitute, albeit still suboptimal and is clearly inferior to formal GFR measurements (7, 8). Cystatin C (CysC) has emerged as an alternate and perhaps more reliable filtration marker for estimating GFR, and the recently introduced combined Creat-CysC equation performs better than equations based on either of these markers alone (9Y12). Herein, we address the performance of those new models of GFR estimation in kidney donors compared to old models.
RESULTS Donor Characteristics Donor characteristics are shown in Table 1. Sixty-one percent of donors were female, 99.2% were Caucasian, 24.5% reported a diagnosis of hypertension, and 3.1% reported a diagnosis and treatment for diabetes mellitus with oral hypoglycemic agents or insulin, or both. Mean Creat and CysC at the time of GFR measurement were 1.0T0.2 mg/dL and 1.0T0.1 mg/L, respectively. Eighty-four percent of donors had an mGFR greater than 60 mL/min/1.73 m2, 16% had an mGFR between 30 and 60 mL/min/1.73 m2 and none had an mGFR of less than 30 mL/min/1.73 m2. Moreover, based on first void urinary albumin-to-creatinine ratio and standard definitions, 87.3% of donors were normoalbuminuric, 11.5% were microalbuminuric, and only 1.2% were macroalbuminuric. Importantly, none of the donors had both mGFR less than 45 mL/min/1.73 m2 and albuminuria.
TABLE 1.
Characteristics of the study population
No. donors Age at donation, yr Age at iohexol GFR measurement, yr Time from donation, yr Female White BMI at GFR measurement, kg/m2 Hypertensiona Diabetes mellitusa Serum creatinine at donation, mg/dL Serum creatinine at GFR measurement, mg/dL Serum cystatin C at GFR measurement, mg/Lb GFR, mL/min/1.73 m2 Iohexol (mGFR) eGFR (MDRD) eGFR (CKD-EPI-Creat) eGFR (CKD-EPI-CysC)b eGFR (CKD-EPI-Creat+CysC)b
257 41.0T11.0 52.9T9.8 11.9T9.0 61.1% 99.2% 28.1T4.9 24.5% 3.1% 0.9T0.1 1.0T0.2 1.0T0.1
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Comparison of Estimated GFR by Four Equations With mGFR The mean mGFR in the 257 donors was 71.9T 11.9 mL/min/1.73 m2, compared to 71.6T14.9 mL/min/ 1.73 m2 by eGFR(MDRD), 78.4T16.3 mL/min/1.73 m2 by eGFR(CKD-EPI-Creat), 81.5T14.2 mL/min/1.73 m2 by eGFR(CKD-EPI-CysC), and 79.5T13.6 mL/min/1.73 m2 by eGFR(CKD-EPI-Creat+CysC) (Table 1). Indices of bias (by both mean difference and mean percent difference), precision, and accuracy (by percentage of estimates falling within 10% and 30% of mGFR) of these equations in relation to mGFR are depicted in Table 2, whereas Figure 1 represents the graphical relationships between eGFR and mGFR for each equation using Bland-Altman plots that depict the distribution of errors in estimation of mGFR with eGFR when a given eGFR value is observed; also shown are Deming regression parameters and histograms and density curves of the biases. eGFR(MDRD) The eGFR(MDRD) very closely estimates mGFR with a bias of only j0.3T11.7 mL/min/1.73 m2 (P=0.64) and a relative bias of 12.9T10.6% (Table 2). The interquartile range was 15.7, and the Deming r2 and root mean square error (RMSE) was 0.41 and 8.31. The eGFR(MDRD) estimates fell within 10% and 30% of mGFR in 45.1% and 94.2% of the cases, respectively (Table 2). eGFR(CKD-EPI-Creat) The eGFR(CKD-EPI-Creat) on average overestimated mGFR with a bias of 6.5T12.1 mL/min/1.73 m2 (PG0.01) and a relative bias of 15.6T9.8%. The interquartile range was 17.7, and the Deming R2 and RMSE was 0.45 and 8.08. The eGFR(CKD-EPI-Creat) was slightly less accurate than the GFR(MDRD) with estimates falling within 10% and 30% of mGFR in 39.3% and 87.2% of the cases, respectively (Table 2). eGFR(CKD-EPI-CysC) The eGFR(CKD-EPI-CysC) exhibited the highest bias and overestimating mGFR by 9.8T10.4 mL/min/1.73 m2 (PG0.01) with a relative bias of 16.8T13.1% (Table 2, Fig. 1C). The interquartile range was 13.5, and the Deming R2 and RMSE was 0.48 and 7.53. eGFR(CKD-EPI-CysC) was also least numerically accurate with estimates falling within 10% and 30% of mGFR in 36.7% and 85.1% of the cases, respectively (Table 2).
71.9T11.9 71.6T14.9 78.4T16.3 81.5T14.2 79.5T13.6
eGFR(CKD-EPI-Creat+CysC) The eGFR(CKD-EPI-Creat+CysC) overestimated mGFR with an absolute bias of 7.9T9.4 mL/min/1.73 m2 (PG0.01) and a relative bias of 13.6T10.5% (Table 2, Fig. 1C). The interquartile range was 9.4, and the Deming R2 and RMSE was 0.64 and 5.95. The eGFR(CKD-EPI-Creat+CysC) was less accurate than eGFR(MDRD) with estimates falling within 10% and 30%of mGFR in 42.3% and 92.6% of the cases, respectively (Table 2). However, by far it had the best precision of mGFR with a R2 of 0.64.
Self-reported and defined as requiring treatment by study participants. n=215. BMI, body mass index; GFR, glomerular filtration rate; MDRD, Modification of Diet in Renal Disease; Creat, creatinine; CysC, serum cystatin C; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration.
Effects of Body Mass Index on the eGFR Models Performance We then sought to analyze the performance of the various models in donors with obesity (body mass index
a b
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TABLE 2. Overall performance of estimating GFR models, and stratified by BMI (G30 vs. Q30 kg/m2) and age at GFR measurement (G50 vs. Q50 years) n=257a
Bias (SD) (P value)
eGFR method eGFR (MDRD) eGFR (CKD-EPI-Creat) eGFR (CKD-EPI-CysC) eGFR (CKD-EPI-Creat+CysC) BMI G30 (n=179) eGFR (MDRD) eGFR (CKD-EPI-Creat) eGFR (CKD-EPI-CysC) eGFR (CKD-EPI-Creat+CysC) BMI Q30 (n=78) eGFR (MDRD) eGFR (CKD-EPI-Creat) eGFR (CKD-EPI-CysC) eGFR (CKD-EPI-Creat+CysC) Age G50 (n=105) eGFR (MDRD) eGFR (CKD-EPI-Creat) eGFR (CKD-EPI-CysC) eGFR (CKD-EPI-Creat+CysC) Age Q50 (n=152) eGFR (MDRD) eGFR (CKD-EPI-Creat) eGFR (CKD-EPI-CysC) eGFR (CKD-EPI-Creat+CysC)
j0.3 6.5 9.8 7.9
IQR
Relative bias (%)
R2b
RMSE
Within 10% of mGFR (%)
Within 30% of mGFR (%)
(11.7) (0.64) (12.1) (G0.01) (10.4) (G0.01) (8.3) (G0.01)
15.7 17.7 13.5 9.4
12.9 15.6 16.8 13.6
(10.6) (9.8) (13.1) (10.5)
0.41 0.45 0.48 0.64
8.3 8.1 7.5 6.0
116 101 79 91
(45.1) (39.3) (36.7) (42.3)
242 (94.2) 224 (87.2) 183 (85.1) 199 (92.6)
j1.4 (11.5) (0.11) 5.7 (11.9) (G0.01) 11.0 (9.5) (G0.01) 8.3 (7.7) (G0.01)
15.8 17.2 12.5 9.8
12.8 14.6 17.3 13.2
(9.5) (12.1) (12.8) (10.2)
0.40 0.44 0.52 0.65
9.3 9.0 8.2 7.0
80 79 55 65
(44.7) (44.1) (36.7) (43.3)
171 (95.5) 156 (87.2) 126 (84.0) 138 (92.0)
(11.8) (0.13) (12.5) (G0.01) (11.8) (G0.01) (9.4) (G0.01)
14.7 18.0 15.6 7.2
13.3 17.9 15.8 14.6
(12.8) (14.7) (13.8) (11.3)
0.47 0.49 0.42 0.61
8.5 8.3 9.2 7.6
36 22 24 26
(46.2) (28.2) (36.9) (40.0)
j1.3 (12.4) (0.29) 8.0 (13.0) (G0.01) 11.8 (11.1) (G0.01) 9.3 (8.1) (G0.01)
17.2 20.2 15.3 11.0
13.0 16.5 17.6 13.8
(10.9) (13.8) (15.1) (11.8)
0.35 0.36 0.33 0.57
9.1 9.1 9.2 7.3
47 38 37 40
(44.8) (36.2) (40.7) (44.0)
100 (95.2) 91 (86.7) 73 (80.2) 83 (91.2)
13.7 15.5 12.3 10.2
12.9 14.9 16.3 13.4
(10.4) (12.4) (11.5) (9.6)
0.37 0.38 0.42 0.55
8.3 8.2 7.9 7.0
69 63 42 51
(45.4) (41.5) (33.9) (41.1)
142 (93.4) 133 (87.5) 110 (88.7) 116 (93.6)
2.0 8.4 7.0 6.9
0.3 5.5 8.4 6.8
(11.1) (0.73) (11.4) (G0.01) (9.7) (G0.01) (8.2) (G0.01)
71 68 57 61
(91.0) (87.2) (87.7) (93.9)
a
n=215 for cystatin CYbased formulas. R2 and RMSE from Deming regression. IQR, interquartile range; RMSE, root mean square error. b
[BMI] Q30 kg/m2) compared to non-obese donors, as the obese subgroup may be particularly prone to inaccurate estimation of renal function. Our cohort comprised 78 living donors with a current BMI greater than or equal to 30 kg/m2. There was a significant difference in bias between the two BMI groups for the eGFR(MDRD) (P=0.03); however, one bias was negative and the other was positive so not probably clinically useful (Table 2). The bias of the eGFR(CKDEPI-CysC) is significantly higher in those with BMI less than 30 kg/m2 (P=0.01). However, there was no significant difference for eGFR(CKD-EPI-Creat) (P=0.10) or eGFR (CKD-EPI-Creat+CysC) (P=0.25) between the two BMI groups (Table 2). Effects of Donor Age on the eGFR Models’ Performance Additionally, we stratified results based on age at time of GFR measurement (G50 vs. Q50 years), which comprised 105 and 152 donors, respectively (Table 2). Bias in donors younger than 50 years was significantly higher than those 50 years or older for the eGFR(CKD-EPI-CysC) and eGFR (CKD-EPI-Creat+CysC) formulas (P=0.02 in both cases), but the difference was trivial and probably not clinically significant. For eGFR(MDRD) and eGFR(CKD-EPI-Creat), there
was no significant difference in bias (P=0.28 and P=0.10, respectively). Importantly, other than the noted small difference in bias, relative accuracy and precision were comparable for all models in both age groups. Correctly Categorizing mGFR (G60 mL/min/1.73 m2 vs. Q60 mL/min/1.73 m2) Given that structural kidney damage is not required to establish the diagnosis of CKD stage 3, and the fact that an estimated GFR level of less than 60 mL/min/1.73 m2 is not infrequently encountered in living kidney donors, we sought to examine the performance of the four equations in their ability to predict and correctly categorize mGFR when it is less than 60 mL/min/1.73 m2 or greater than or equal to 60 mL/min/1.73 m2 (Fig. 2, Table 3). The eGFR (MDRD) and eGFR(CKD-EPI-Creat) equations performed best for donors with lower mGFR (G60 mL/min/1.73 m2) with 67.6% and 43.2% of estimated GFR correctly categorized in the less than 60 mL/min/1.73 m2, respectively. On the other hand, the eGFR(CKD-EPI-CysC) and eGFR(CKDEPI-Creat+CysC) equations performed best for donors with higher mGFR (Q60 mL/min/1.73 m2) with 98.3% and 97.2% of estimated GFR correctly categorized as greater than or equal to 60 mL/min/1.73 m2 range, respectively. Kappa
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FIGURE 1. Bland-Altman plots showing the distribution of errors in estimation of mGFR with eGFR when a given eGFR value is observed; Deming regression plots with equivalence line, and histograms and density curves of the distribution of the difference (bias) between eGFR and mGFR for (A) eGFR (MDRD), (B) eGFR (CKD-EPI-Creat), (C) eGFR (CKD-EPI-CysC), and (D) eGFR (CKD-EPI-Creat+CysC).
statistics ranged from 0.33 to 0.41, indicating slight to moderate agreement (Table 3).
DISCUSSION Our results indicate that eGFR(MDRD) and eGFR(CKDEPI-Creat+CysC) equations exhibit less bias and better
prediction of mGFR than eGFR(CKD-EPI-Creat) and eGFR (CKD-EPI-CysC) equations, and the combined equation had the highest precision. Importantly, eGFR(MDRD) was the best model in detecting those with true GFR less than 60 mL/min/1.73 m2. In clinical practice, it is not uncommon for living kidney donors to have a slightly higher serum Creat. The application of creatinine-based GFR formulas to healthy
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FIGURE 2. Graphs representing the percentage agreement of the four different GFR equations, eGFR(MDRD), eGFR(CKD-EPI-Creat), eGFR(CKD-EPI-CysC), and eGFR(CKDEPI-Creat+CysC), with mGFR (Q or G60 mL/min/1.73 m2).
living kidney donors before and after donation generated a lot of controversies, and those creatinine-based GFR estimates are relatively imprecise (5Y7, 13Y20). The analytical accuracy of CysC testing has recently been greatly improved by the calibration of CysC laboratory assays to an international reference material (21, 22), and therefore utilizing both serum Creat and CysC should be more accurate and precise than equations using either component alone (12) as shown in our present study. In this study, we built upon our previously published observations examining the performance of those newly developed formulas in living kidney donors. The results of our study add to current knowledge, as GFR assessment was obtained at different times post-donation (range 3Y45 years). We observed higher precision with the eGFR(CKD-EPICreat+CysC). One could argue that a more precise estimating equation represents mainly the strength of the linear association between estimated and measured GFR, and it does not provide clinically applicable information on the overall performance of the equation. On the other hand, bias and accuracy provide the clinician with better tools to assess the performance of estimating equations. We also observed a higher accuracy with the eGFR(CKD-EPI-Creat+CysC) and eGFR(MDRD) equation compared to eGFR(CKD-EPICreat) and eGFR(CKD-EPI-CysC). An estimating equation with combined higher accuracy and precision would be ideal for use in the setting of living kidney donation when measured GFR is generally preserved. Although it was not surprising that the eGFR(MDRD) performed best when mGFR was less than 60 mL/min/1.73 m2, as it was derived from populations with CKD, the GFR(CKD-EPI-Creat+CysC) equation was as accurate but more precise than the eGFR(MDRD) at higher levels of mGFR, as our cohort of living donors had a mean mGFR of 71.8 mL/min/1.73 m2, similar to the study by Inker et al. (12). We acknowledge several limitations. This is a retrospective single center study. However, unlike previously published studies, it includes a large cohort of living kidney donors in whom iohexol GFR was obtained. Nonetheless, the cross-sectional nature of this study is a major shortcoming. It remains therefore unknown if the performance and correlation of the various GFR assessment methodologies change for a particular donor over time. In that regard, the
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wide span of post-donation periods herein may impede interpretation of the results as different GFR estimating models may perform differently at different time points post-donation. In addition, the study population is 99.2% Caucasian, so rendering the generalization to a larger pool of live donors derived from the diverse ethnic groups would be rather difficult. Our findings need to be validated in donors of other racial and ethnic groups such as African Americans and Hispanics. Although we selected donors randomly for formal GFR measurement, there were significant differences in this population when compared to the donor population as a whole: the donors with measured GFR were older and had a shorter period of elapsed time since donation. We also recognize the relative limitations of using a single measurement of GFR by iohexol and of serum creatinine and cystatin C that may affect reproducibility of the estimating equations. There are also inherent limitations of the biostatistical methods used herein (such as bias, accuracy, and precision) in quantitatively testing the performance of GFR estimating equations especially for the precision that mainly describes how close the values of repeated measurements are. However, in the absence of better alternatives, those analyses have been widely recognized as standard statistical methodology for studies validating new measuring tools against a reference technique (23). Our analysis is novel as it is the first study comparing the newly developed equations incorporating both CysC and Creat (CKD-EPI 2012 cystatin C and creatinine-cystatin C equations) to the widely used MDRD equation and the CKDEPI 2009 creatinine equation in a cohort of living donors who donated 3 to 45 years ago. Nonetheless, it has been shown that serum Cystatin C can be influenced by factors other than renal function, such as C-reactive protein levels and cigarette smoking. Because many of our living donors are several years post-donation, the confounding of their current health status, tobacco smoking, and C-reactive protein level may have also affected outcomes. Another limitation is that only 215 of 257 donors had available CysC levels. What Should the Clinician Do? Taken together, eGFR(CKD-EPI-Creat+CysC) equation provides comparable accuracy to the eGFR(MDRD) in overall estimation of measured GFR but with higher precision. However, we do not recommend the widespread use of the eGFR(CKD-EPI-Creat+CysC) equation alone post-living kidney donation, as donors with low post-donation GFR (although a minority) may go unrecognized. A reasonable TABLE 3. Correct categorization at mGFRa threshold of 60 mL/min/1.73 m2 % Matched with mGFR Formula/GFR category eGFR(MDRD) eGFR(CKD-EPI-Creat) eGFR(CKD-EPI-CysC) eGFR(CKD-EPI-Creat+CysC) a
mGFR G60 mGFR Q60 Kappa (specificity) (sensitivity) statistic 67.6 43.2 25.7 28.6
mGFR=measured GFR (mL/min/1.73 m2).
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82.7 94.1 98.3 97.2
0.39 0.41 0.33 0.34
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approach in this population would be that one would start with estimating GFR with the MDRD study equation, and if the estimate is less than 60 mL/min/1.73 m2, one would not use other estimating models. If the MDRD estimate, however, is greater than 60 ml/min/1.73 m2, the eGFR (CKD-EPI-Creat+CysC) should be used to provide a more precise estimate.
MATERIALS AND METHODS Between 1963 and 2007, 3,698 living donors donated a kidney at the University of Minnesota. Utilizing the Social Security Death Master File, we found that 3,404 are alive, 268 have died, and 26 of the donors were foreign nationals and were thus untraceable. Alive and traceable kidney donors were stratified by gender and years from donation in 3-year intervals. A random start was used to generate random numbers using a SAS macro to select a similar portion of donors (5%Y10%) from each stratum to undergo GFR measurements. In total, 257 donors of 2,199 who returned health status updates had laboratory testing and were randomly selected for iohexol GFR measurement (mGFR). The 257 donors were similar to donors without GFR measurement on current age and gender distribution. The study was approved by the University of Minnesota Institutional Review Board. GFR was measured using the plasma disappearance of iohexol, as previously described (18, 24, 25). The plasma disappearance method was chosen because of its excellent correlation with inulin clearance and does not require timed urine collection which reduces the variability in GFR measurements (25, 26). The plasma iohexol disappearance profile was analyzed by the one-compartment model and corrected with the BrochnerMortenson formula with all data points fitted by nonlinear regression. This correction produces a correlation with inulin GFR that is virtually identical to the two-compartment model. The coefficient of variation of the iohexol GFR method was consistently less than 10%. Serum Creat and CysC were obtained the morning of the iohexol GFR measurement and after an 8- to 12-hr fast.
Standardization of Serum Creatinine and Cystatin C Serum Creat was measured using a Roche Modular P and Roche enzymatic creatinine reagents, which are traceable to the Isotope Dilution Mass Spectrometry (IDMS) method of the National Institute of Standards and Technology as described in detail in our previous publications (7, 27). Likewise, we used the standardized serum Cystatin C traceable to the International Federation of Clinical Chemistry Working Group for standardization of Serum Cystatin C and the Institute for Reference Materials and Measurements certified reference materials (21, 22). CysC was measured using the PENIA cystatin C kit on a ProSpec nephelometer (Siemens) from blood specimens stored at j70-C. A freeze-thaw study on CysC showed very good correlation between frozen and fresh serum (once frozen as y-axis and fresh as x-axis ordinary least-squares [OLS] regression was y=1.015, x=j0.0213). Multiple freeze-thaws were not investigated. Furthermore, it was recently advocated to use the re-expressed CKD-EPI-Cystatin C equations to reduce variability in estimated GFR by CKD-EPI Cystatin C (11). These CysC values were in alignment with the ERM DA-471/IFCC international CysC reference material, which is needed for use in the most recent CKD-EPI equations published in 2012 (12). Serum CysC levels were available in 215 donors. We evaluated the performance of four GFR estimating models (Table S1, SDC, http://links.lww.com/TP/A973): 1. The IDMS-traceable re-expressed MDRD study equation; eGFR(MDRD); 2. CKD-EPI-Creatinine; eGFR(CKD-EPI-Creat) 3. CKD-EPI-Cystatin C; eGFR(CKD-EPI-CysC) 4. CKD-EPI-Creatinine and Cystatin C; eGFR(CKD-EPI-Creat+CysC)
Statistical Analysis We assessed the performance of these four equations against mGFR in several ways: Bias: the average prediction error=3 (eGFRjmGFR)/n; relative
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bias (percent deviation from the mGFR) was also calculated. Precision: the value of r2 and RMSE from the regression of mGFR on eGFR and the interquartile range of differences. Relative accuracy: the percent of estimates falling within 10% and 30% of mGFR. Bland-Altman plots were created for all equations (23). Dashed lines shown represent T2 standard deviations. Deming regression plots with slope, intercept, R2, and RMSE are also presented. Deming regression is utilized instead of OLS regression method because there is considerable error in both the estimated and also measured GFR. Finally, a density plot reflecting the distribution of the residuals (i.e., biases) is presented for each estimating equation. Statistical analysis was performed in SAS (ver. 9.3; SAS Institute Inc., Cary, NC, USA) and Stata v12.1 was used for Deming regression analysis.
ACKNOWLEDGMENTS The authors would like to thank all living kidney donors and the living kidney donors’ coordinators at the University of Minnesota, Minneapolis, MN. REFERENCES 1.
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