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NDT Advance Access published online on April 30, 2007
Nephrology Dialysis Transplantation, doi:10.1093/ndt/gfm243
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© The Author [2007]. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Comparison between creatinine and cystatin C-based GFR equations in renal transplantation

Ahmed Zahran1, Mabood Qureshi2 and Ahmed Shoker1

1Department of Medicine, Division of Nephrology and 2Department of Pathology, Royal University Hospital, University of Saskatchewan, Canada

Correspondence and offprint requests to: Ahmed Shoker, Division of Nephrology, St. Paul's Hospital, 1702 – 20th St. West, Saskatoon, SK S7M 0Z9, Canada. Email: shoker{at}sask.usask.ca



   Abstract
Top
 Abstract
Introduction
 Materials and methods
 Results
 Discussion
 References
 
Background. Estimation of glomerular filtration rate (GFR) from serum creatinine (Scr) or cystatin C (Cys C) exhibit variable performances.

Methods. We compared the performances of 14 Scr and 9 Cys C estimated GFR equations using inulin clearance (Clin) as the reference test in 103 stable renal transplant populations. Bias, precision, receiving operation characteristics (ROC), accuracy within 30% ranges from the reference method and agreements of each test were compared.

Results. Mean Clin was 46.4 ± 20.9 ml/min/1.73 m2. Scr and Cys C levels correlated well with each other (r = 0.83, P < 0.0001) and with Clin (r = –0.57 and –0.53, P < 0.001, respectively). ROC analysis demonstrated no superiority of Cys C over Scr. Gats equation achieved the highest accuracy of 70% in patients with GFR ≥60 ml/min/1.73 m2. In patients with GFR ≥60 ml/min/1.73 m2, the Nankivell equation demonstrated the highest accuracy of 73.91%. Cys C-based equations were not depicted to be thoroughly accurate. Bias, precision and agreement were otherwise similar in all GFR tests.

Conclusion. Scr-based equations did not appear to be inferior to Cys C-based equations as a means to estimate GFR in renal transplant patients.

Keywords: cystatin C; GFR; renal transplantation; serum creatinine



   Introduction
Top
 Abstract
 Introduction
Materials and methods
 Results
 Discussion
 References
 
The present approach adopted to assess the renal function in humans is often limited to measurements of proxies for glomerular filtration rate (GFR) such as serum creatinine (Scr), creatinine clearance (Clcr) and estimates of GFR derived from Scr-based equations [1]. The limitations of Scr and Clcr for estimation of GFR are well known. Scr concentration is affected by several factors that are independent of changes in GFR such as age, race, muscle mass, gender, medication use and catabolic state [2]. Various Scr-based equations have been developed in an attempt to improve the estimation of GFR from Scr [3–16]. These equations, however, have not been shown to be accurate in renal transplant recipients, and their suitability in clinical trials has been called into question [17]. In addition, the methods used to measure Scr interfere with the accuracy of the GFR estimation formulae [18]. To circumvent the problems attached to the measurement of GFR based on Scr, several investigators studied the feasibility of cystatin C (Cys C) as a marker of GFR [19–21]. Recently, several prediction equations have been derived from both paediatric and adult patients to estimate GFR from the Cys C concentration [22–29]. However, only three studies tested the performance of GFR equations based on Cys C or Scr levels in renal transplant patients [30–32]. In these studies, the reference method was an isotope GFR scan and they reached discrepant results. Two studies compared the performance of Scr and Cys C concentration in renal transplant patients using inulin clearance (Clin) as a reference method. In these studies, no comparison of the current equations was performed [20,33]. Accordingly, the objective of this study was to compare the performance of GFR estimates from the Cys C and Scr concentrations using old and recent equations in an independent sample of adult renal transplant recipients. Bias, precision, accuracy within 30% range from the reference GFR and agreement of the prediction equations were compared with Clin.



   Materials and methods
Top
 Abstract
 Introduction
 Materials and methods
Results
 Discussion
 References
 
Study population
The protocol for this study followed the ethical standards of this institution. Adult renal transplant recipients who were followed at the Saskatchewan Transplant Program and were at least 1-month post-transplantation and had stable renal function were eligible to participate. Consecutive patients, who met these criteria and had undergone all laboratory testing completed between August 2005 and July 2006, were included in this analysis. However, patients were excluded for the following reasons: unable or unwilling to provide informed consent, pregnant or breastfeeding women, acute rejection or a change in Scr of >10% within the preceding 4 weeks or showed evidence of congestive heart failure or chronic liver disease associated with ascites.

Laboratory assessment
The inulin clearance (Inutest)
GFR was measured by the Clin (Inutest), in which sinistrin, an inulin analogue, is used as a substitute for inulin because it is more water soluble and easy to handle. The procedure started at 8.30 a.m. after an overnight fasting. Two I.V. lines were established, one for injection and the other for sampling. Hydration was achieved with loading with oral water (10 ml/kg) to produce good urine volume of at least 2 ml/min. A blank urine sample was voided and a blood sample was drawn, followed by injection of an intravenous loading dose of Inutest® 25% (Fresenius, Linz, Austria) over 1 minute. The dose was calculated from the necessary plasma concentration (250 mg/l) and the sinistrin volume of distribution as recommended by the company as follows:


Formula

The sinistrin volume of distribution approximately corresponds to the extracellular volume and amounts to ~18% of the body weight.

The bolus dose was followed immediately by a maintenance dose to achieve sinistrin plasma concentration of approximately 250 mg/l as follows:


Formula

This dose was infused over 160 min in 250 ml normal saline by IVAC pump.

After an equilibration period of 90 min, patients were asked to empty their bladder spontaneously and then two consecutive clearance periods of 30 min each were analysed. During each period, urine volume was collected, measured and sampled. Blood samples were drawn at the beginning and end of each period (the sample at the end of the first period was considered the beginning sample for the second period). Clin was measured as the mean of the two clearance periods with the formula UV/P, where U and P are sinistrin concentration in urine and plasma and V is urine flow rate ml/min. Clin was expressed in 1.73 m2. Body surface area was calculated by Du Bois formula [34].

Inulin concentrations were determined by Heyrovsky method [35]. In the original method, each sample was read over approximately 15 s in a cuvette by a spectrophotometer. We found that the absorbance of the reaction increased in a time dependant fashion (data not shown) Therefore, 0.25 ml triplicate samples were measured in 96 round U bottom microplate wells (Evergreen Scientific). Samples were measured on a UV at 520 nm using microplate reader (ELx 808TM Absorbance microplate reader, BIO-TEK). With this modification, sinistrin concentration of 0.02–0.5 mg/ml and optic density remained linear with an r2 of >0.98. It should be noted that all measured samples had concentrations well within this range. Elevated blood sugar is known to affect the in vitro concentration of sinistrin. In our preliminary analysis, we confirmed that blood sugar up to 12 mmol/l does not affect our assay, and therefore, we included diabetic transplant patients who demonstrated a blood sugar level of <12 mmol/l prior to the procedure. Our modified method has an intra-assay CV of <6% and an inter-assay CV of consistently <8%. The 103 Clin retained for this analysis were all performed on patients who maintained a urine flow rate of at least 2 ml/min and established a stable sinistrin concentration that did not vary by >10% in the three plasma samples.

Measurement of cystatin C concentrations
Cys C was measured by Enzyme Linked Immunosorbent Assay (ELISA), (Biovender Laboratory Medicine, Inc.) using the microplate reader (ELx 808TM Absorbance microplate reader, BIO-TEK) at wave length 450 nm. The intra-assay CV was 9.6% at 589 ng/ml (0.589 mg/l) and 5% at 2862 ng/ml (2.862 mg/l). The inter-assay CV was 6.2% at 600 ng/ml (0.6 mg/l) and 4.8% at 2905 ng/ml (2.905 mg/l). This ELISA method has an r of 0.97 (r2 0.94) when compared with the DADE B latex assisted turbidimetry method and r of 0.96 (r2 0.92) with DAKO ELISA method as studied by the manufacture (personal communication).

Measurement of serum creatinine concentrations
Serum creatinine was measured with an enzymatic assay (SYNCHRON LX 20 Systems, Bachman, Coulter, Inc., Fullerton, CA, USA) with a normal range of 60–110 µmol/l. The intra-assay and inter-assay CV were <3%. This method has a small bias of ≤ +6 umol/l as compared with the isotope dilution mass spectrometry (IDMS), however, this difference is not considered significant in estimation of GFR [36].

GFR estimates
Fourteen Scr-based equations [3–16] and nine Cys C-based equations [22–29] (Table l) were studied. We included the modified MDRD equation which is suggested for use to estimate GFR [13] when Scr is measured by IDMS method. Continuous data were presented as mean ± SD and discrete variables as frequency (%). A P value of <0.05 was considered as statistically significant.


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  		Table 1. Serum creatinine and cystatin C-based prediction equations

 
Statistical evaluation of the predictive formulas
We used the statistical package of social signs (SPSS, version 14) and Excel to perform the analysis. The evaluation of the prediction equations was performed by calculating the bias, precision, agreement and accuracy as recommended in the National Kidney Foundation guidelines on chronic kidney disease [37]. In addition, beta errors between all equations and Clin were calculated. Bias was defined as the mean difference between the measured and estimated GFR. Precision was defined as SD of the difference between the measured and estimated GFR. Accuracy was defined as the percentage of GFR estimates lying within 30% of measured GFR. In addition, the limits of agreement between the estimated and measured GFR were presented in tabulated and graphic forms using Bland and Altman analysis (Figures 2 and 3) [38]. Moreover, we performed receiving operation characteristics (ROC) analysis to substantiate our results. ROC analysis was performed to quantitate the accuracy of Scr and Cys C to detect reduced GFR using a cut-off value of 30 and 60 ml/min/1.73 m2. We used MedCalc software to find if there is a significant difference between area under the curve (AUC) for Scr and Cys C.


Figure 2
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  		Fig. 2. Bland and Altman analysis of GFR estimates. In this graphical method the difference between predicted and measured GFR is plotted against inulin clearance. The mean difference (m) is indicated by centre line, limits of agreement are indicated by the upper (m + 2SD) and lower (m – 2SD) lines. Gats and Clin are plotted in (A), MDRD 2 and Clin are plotted in (B).

 

Figure 3
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  		Fig. 3. Bland and Altman analysis of GFR estimates. In this graphical method the difference between predicted and measured GFR is plotted against inulin clearance. The mean difference (m) is indicated by centre line, limits of agreement are indicated by the upper (m + 2SD) and lower (m – 2SD) lines. Nankivell and Clin are plotted in (A) and Hull and Clin are plotted in (B).

 


   Results
Top
 Abstract
 Introduction
 Materials and methods
 Results
Discussion
 References
 
Of the 120 patients approached, a total of 110 stable renal transplantations agreed to participate in this study. Seven patients were excluded. Two patients developed an allergy requiring discontinuation of the test, two patients produced a urine flow rate of <2 ml/min and three patients did not achieve a steady state as recognized from a significant difference in sinistrin plasma concentrations of >10% and 72% of patients had received a kidney from a cadaveric donor. Causes of end-stage renal disease were non-diabetic glomerular diseases in 61%, diabetic glomerular disease in 24% and congenital and unknown in 15%. Mean urine flow rate during Clin procedure was 6.6 ml/min. (medium 6.2; range 2–15). The baseline characteristics of the cohort and subgroups with GFR above and below 60 ml/min/1.73 m2 are presented in Tables 2–4GoGo. The majority of the patients included in the study were males. The patients had a wide range of renal function that encompassed all five stages of kidney disease, outcomes, quality initiative and chronic kidney disease classification system [39]. All patients were on prednisolone, calcineurin inhibitors in addition to a mycophenolate derivative (CellCept or Myfortic) or rapamune. All patients were on Septra one tablet every other day. Tables 2–5GoGoGo show the statistical descriptive results. The average Clin was 46.4 ± 20.7 ml/min/1.73 m2, with a range of 12.3–121.6 ml/min/1.73 m2 and coefficient of variation of 44.6%. The mean, median and range of estimated GFR with the different prediction equations are shown in Table 5.


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  		Table 2. Patient characteristics

 

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  		Table 3. Patient characteristics with GFR >60 ml/min/1.73 m2

 

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  		Table 4. Patient characteristics with GFR <60 ml/min/1.73 m2

 

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  		Table 5. Mean, median and range of measured and estimated GFR

 
Performance of Scr and Cys C
Pearson's correlation coefficients (r) between normalized Clin and Scr or Cys C showed a significant, negative correlation between normalized Clin with Scr and Cys C with r value –0.57 (r2 = 0.32) and –0.53 (r2 = 0.28), respectively. Of more importance was the observation that Scr and Cys C levels correlated with an r of 0.83 (r2 = 0.69) and P < 0.0001, which suggest a similar performance of both molecules to predict GFR in this population. Figure 1A and B show that the area under the ROC curves (95% confidence interval) at cut-off value of 30 ml/min/1.73 m2 for Scr and Cys C were 0.842 (0.736–0.947) and 0.841 (0.735–0.948), respectively. At 60 ml/min/1.73 m2 these values were 0.803 (0.736–0.905) and 0.718 (0.598–0.839), respectively. Again, there was no significant difference between Cys C and Scr at cut-off values 30 ml/min/1.73 m2 and 60 ml/min/1.73 m2 to detect changes in GFR (P = 0.995 and 0.080, respectively).


Figure 1
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  		Fig. 1. Non-parametric ROC plots for the diagnostic accuracy of serum creatinine concentration (solid line) and cystatin C (dotted line) in reduced GFR (<30 ml/min/1.73 m2). (A) The area under the curve for serum creatinine = 0.842, SE = 0.054, P = 0.000 and for cystatin C = 0.841, SE = 0.054, P = 0.001. (B) The area under the curve for serum creatinine = 0.803, SE = 0.052, P = 0.000 and for cystatin C = 0.718, SE = 0.061, P = 0.001.

 
Performance of the derived mathematical equations in total populations
The Gats equation was the most accurate. It had the least bias (–0.24) and the highest percentage of values that fell within 30% of the Clin (66.02%). The MDRD 2 equation had the second best accuracy in the general population with a reasonable bias of –1.68 ml/min and accuracy of 65.05%. Among the Cys C-based equations, the Hoek had the best accuracy of 55.45% and the Filler equation had the least bias of 1.44 ml/min. Each GFR test correlated significantly (P = 0.0001) with Clin. Analysis of r2 pointed out that only 41% of the interindividual variability for GFR prediction by Edwards’ formula was explained by true difference in Clin, while it was 26% for the Hoek equation. For all equations, <25% of variance was explained by the estimation formulae. Limits of agreement between the predicted and measured tests showed lack of reliable agreement as presented in Table 6.


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  		Table 6. Limits of agreement of glomerular filtration rate prediction equations in total population

 
Performance of the derived mathematical equations in patients with GFR above and below 60 ml/min/1.73 m2
Table 7 shows the performance of the GFR equations in this subgroup. In patients with GFR above 60 ml/min/1.73 m2, the Nankivell and Hull equations were the most accurate GFR estimates, while C–G equations demonstrated the least bias among all GFR estimates. Analysis of accuracy showed that the performance of Cys C-based equations were approximately 20% lower than that of the creatinine based equations in patients with GFR below 60 ml/min/1.73 m2. The Gats equation followed by MDRD 2 showed the highest accuracy. The MDRD 2 showed the least bias followed by Jellife 1 and Gats equations. Among the Cys C-based equations, Macisac followed by Hoek had the highest accuracy. Bias was the least in the Larsson equation (DakoCytomation).


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  		Table 7. Precision, bias and accuracy of prediction equations compared with inulin clearance of GFR above and below 60 ml/min/1.73 m2

 


   Discussion
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
References
 
In a recent international survey, opinion on GFR estimation was evaluated. The majority used either the Cockcroft–Gault formula or the MDRD 2 equation [39]. Therefore, these formulae as well as other tests of GFR equations were included to insure complete comparison between the old and recently available equations. The comparison of these equations was extended to patients with GFR above and below 60 ml/min/1.73 m2 in accordance with chronic kidney disease classifications cut-off limit.

The correlation coefficients for both Scr and Cys C with Clin nearly showed similar r values. In addition, the r value between Cys C and Scr was highly significant, similar to that reported by Poge et al. [40]. ROC analysis in this study showed that Scr is not inferior to Cys C as a surrogate marker for Clin. These results are at variance to those reported by Plebani et al. [33] who concluded the superiority of Cys C in a small group of 12 transplant patients. Our results are, however, in agreement with Daniel et al. [20] who performed 103 Clin in a larger cohort of 60 transplant patients. Our results are also in agreement with those of Poge et al. [40] and Christensson et al. [19] who found no difference in the ROC for Cys C (0.094) and Scr (0.09). In total, these results suggest that there is no real difference in the diagnostic accuracy.

Consistent with other investigators, we noticed that patients with higher GFRs had a lower r value [41,42] albeit with a higher r value than ours. Our results are in agreement with the general notion that a decrease in GFR can occur below normal values by two thirds or more before an appreciable increase in serum creatinine occurs [43,44].

The accuracy of the Scr and Cys C-based equations varied from one study to another [17,30–32,45] even in the recent literature in which isotope GFR was used. In this analysis, the Gates equation showed the highest accuracy in the general population and in patients with a GFR of <60 ml/min/1.73 m2. The MDRD 2 and Jelliffe 1 were second preferred. The accuracy of the Nankivell equation was particularly superior in patients with a GFR above 60 ml/min/1.73 m2 a fact that was not identified previously in a similar work of Mariat et al. [17] who did not perform subgroup analysis. The Hull equation had a similar superior performance but with a slightly inferior bias to Nankivell.

Consistent with previous work [17,30,45] assessment of agreement indicated that none of the equations demonstrated an acceptable agreement with Clin, since they concealed a discrepancy of 23–93 ml/min/1.73m2. It should be noted, however, that the agreement ranges concluded in this analysis are close to those concluded by Mariat et al. [17].

The findings of this study are clinically relevant given the previous research that showed conflicting results on the relative performances of Scr and Cys C in kidney transplant recipients. Similar to Scr, there are several reports which suggest that factors other than GFR may indeed affect Cys C concentration [46–51]. Therefore, Cys C may indeed suffer from limitations unseen previously, which questions its appeal as a replacement marker for Scr to estimate GFR.

Comparison of Cys C and Scr in transplant populations gave conflicting results [19–22,33,52–59]. Three studies were conducted with the intent to compare the performance of Scr and Cys C-based equations in transplant patients. In one of the studies performed on 29 patients using 125I-Iothalamate as the reference method, the authors concluded equal performances between the simplified MDRD and Larsson equation with an overall accuracy of 66% and 69%, respectively, within a 30% range from the reference method. The respective biases were 1.7 and 4.7 [30]. In the second transplant study, 117 transplant patients were included using 99mTc-DTPA as the reference method. In this study White et al. [31] showed that the accuracy ranges were exceptionally high for both Scr-and Cys C-based equations. They concluded that Filler and Le Bricon equations performed the best, with 30% accuracy of 87% and 89% and bias of –1.7 and –3.8, respectively. In addition, some of the Scr-based equations (Cockcroft–Gault, MDRD and Nankivell) performed better than some of the Cys C-based equations (Larsson showed the least accuracy of all equations). While in the third study, Poge et al. [32] showed that the Filler equation performed the least when compared with the Larsson, Hoek and MDRD equations. Furthermore, while the Larsson equation showed the highest performance, it was marginally better with only 10% superiority over the MDRD equation in regard to a 30% accuracy. Inconsistencies between these reports can be attributed to different isotope scan techniques among different centres. Current means to assess GFR in humans is limited to measurement of proxies for GFR such as Scr and mathematically estimated GFR. Cys C, which has recently been proposed as another accuracy measure for GFR, was favoured in a number of studies over Scr (reviewed in [60]). Comparison of Cys C and Scr-based equations gave conflicting results. Although most of the studies favoured Cys C-based equations over Scr-based equations, there was a lack of uniform superiority of these Cys C-based equations. Of more importance, we could not identify a consistent superiority of one equation over others in all the studies. Also, there was no consistency in the results in regard to correlation, bias, precision, accuracy and limits of agreement.

There are several inherent limitations to the use of Cys C-based GFR equations. Cys C was shown to be influenced by high-dose steroids. As such, Cys C level may be affected by differences in the steroid doses among patients [47,48]. It should be noted, however, that all our patients were on maintenance, small dose of prednisone of <10 mg daily. Risch et al. [47] showed, however, that renal transplant recipients on low-dose prednisone (5–10 mg/day) had a higher Cys C concentration compared with those on steroid-free immunosuppression. In contrast, Bökenkamp et al. [61] found no correlation between Cys C and the steroid dose. In addition, we did not simultaneously measure thyroid function to rule out hypothyroidism or hyperthyroidism, both of which can influence Cys C concentration [50]. However, we evaluated thyroid function as part of pretransplant work. Further, we are not aware of any recent study which compares isotope GFR to Clin using the current statistical methods including accuracy, precision and bias. These factors may explain, at least in part, some of the inconsistencies between these results and add more relevance to the importance of Clin as a reference method to settle the controversy between Scr-and Cys C-based equations. Of note, inulin was used as the comparison reference test in only two transplant studies [20,33]. In the larger study ROC analysis showed no superiority of Cys C over Scr and the authors concluded that Cys C is not a more sensitive marker than Scr or Cockcroft–Gault equation for detecting renal failure in transplant patients [20]. None of these two studies compared the performance of the mathematical GFR equations.

There are limitations to this study. The methods used to measure Scr differ among centres. In addition, evaluation of the MDRD 2 equation requires calibration of the Scr to the laboratory used in the MDRD 2 study, which was not done in this study. A small systematic error in Scr measurement could greatly affect the results of a GFR [62] The Scr values were measured at the same laboratory using the same method to avoid differences in calibration. Our method has a small reported bias, however. Hallan et al. [63] pointed out that the bias due to a missing calibration decreases as Scr increases. This is crucial since our cohort comprises a considerable percentage of patients with elevated Scr. In addition, we added the suggested MDRD 2 (IDMS) in our comparative analysis. Another limitation is the small number of 23 patients included in this analysis with GFR above 60 ml/min/m2. It is known, however, that most transplant patients have a modest GFR and as such our patient population represents the average transplant patients.

In conclusion, based on the data presented in a large renal transplant population, these Scr and Cys C-based GFR tests exhibited a considerable lack of agreement with the reference GFR. Although, some equations demonstrated a better accuracy than others, we concluded that none of these formulae seemed good enough to safely substitute for Clin for point estimates of GFR, but are acceptable for discrimination of patients with chronic transplant kidney disease. Within these limitations, the data shows that Nankivell and Hull are the most performant formulae in patients with GFR above 60 ml/min/1.73 m2, and the Gats and MDRD 2 demonstrated the best accuracy in patients with GFR below 60 ml/min/1.73 m2. The data also confirmed lack of superiority of the current Cys C based equations over Scr-based equations to estimate GFR in the renal transplant patient. Because of the high correlation between Scr and Cys C, we do not see a merit for Cys C over Scr-based equations as an ideal method to determine renal transplant function.

Conflicts of interest statement. None declared.



   References
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 

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Received for publication: 23.11.06
Accepted in revised form: 29. 3.07


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