|Year : 2019 | Volume
| Issue : 3 | Page : 587-596
|Estimating glomerular filtration rate in adult kidney transplant recipients in the Asian population
Lydia Kamaruzaman1, Rozita Mohd1, Faizah Mohd Zaki2, Rozita Hod3, Aini Ab Aziz4
1 Department of Medicine, Nephrology Unit, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, Malaysia
2 Department of Radiology, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, Malaysia
3 Department of Public Health, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, Malaysia
4 Department of Radiology, National Heart Institute, Kuala Lumpur, Malaysia
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|Date of Submission||01-Mar-2018|
|Date of Acceptance||29-Apr-2018|
|Date of Web Publication||26-Jun-2019|
| Abstract|| |
Estimation of glomerular filtration rate (GFR) in renal transplant patients is often assessed by application of creatinine-based equations. The aim was to correlate the estimated GFR (eGFR) using creatinine-based equations [Cockroft-Gault, Modification of Diet in Renal Disease (MDRD), Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), Nankivell] with gold standard 51Cr-EDTA in kidney transplant patients in the Asian population. This is a single-center, cross-sectional study involving adult renal transplant patients. Background demographic data, medications, office blood pressure, and baseline investigations were taken. Correlations between measured GFR and eGFR were analyzed and Pearson’s correlation coefficients, bias, and accuracy were assessed. Thirty-seven renal transplant patients with a mean age of 46 ± 13 years were recruited. Majority were Chinese (68%), Malay (24%), and Indian (8%). The median duration of the transplant was 84 (interquartile range 60,132) months. The mean measured GFR was 71 ± 21 mL/min/1.73 m2. Cockroft-Gault and CKD-EPI has the best correlation with 51Cr-EDTA with Pearson correlation coefficients of 0.733 (P <0.001) and 0.711 (P < 0.001), respectively. All formulae showed >80% accuracy with eGFR lies between 30% of the measured value. CKD-EPI and MDRD had the greatest accuracy with 89.2% each. Clinician may use any of these three serum creatinine-based equations to estimate GFR in kidney transplant recipients.
|How to cite this article:|
Kamaruzaman L, Mohd R, Zaki FM, Hod R, Aziz AA. Estimating glomerular filtration rate in adult kidney transplant recipients in the Asian population. Saudi J Kidney Dis Transpl 2019;30:587-96
|How to cite this URL:|
Kamaruzaman L, Mohd R, Zaki FM, Hod R, Aziz AA. Estimating glomerular filtration rate in adult kidney transplant recipients in the Asian population. Saudi J Kidney Dis Transpl [serial online] 2019 [cited 2020 May 27];30:587-96. Available from: http://www.sjkdt.org/text.asp?2019/30/3/587/261331
| Introduction|| |
Assessment of graft function is of utmost important in the surveillance of the renal trans-plant patients. Estimate of glomerular filtration rate (GFR) represents the best overall index of renal function, in kidney transplant patients. Kidney Disease Improving Global Outcomes Initiative (KDOQI) guideline recommends the use of serum creatinine-based GFR equations to estimate kidney function in the routine clinical care of kidney transplant recipients. However, at present, there is no standard equation to accurately measure the GFR in this population. The commonly used creatinine-based equations in both chronic kidney disease (CKD) and kidney transplant patients are Cockcroft-Gault (CG), Modification of Diet in Renal Disease (MDRD), and Chronic Kidney Disease Epidemiology Collaboration (CKD- EPI) formula. Unfortunately, serum creatinine itself is unreliable index of graft function,, as it is depended on various patients’ factors such as age, sex, nutrition status, race, muscle bulk, and immobility and affected by certain medications.,, Furthermore, GFR can decline to approximately half the normal value before the serum creatinine rises above the reference range.
One major flaw of using any of these equations in kidney transplant patients is because they were derived from different type of populations. CG was derived from normal kidney function population taking into consideration the patient’s serum creatinine, age, and weight., Hence, CG tends to overestimate GFR in obese or edematous patients. Whereas the MDRD was derived from CKD population and adjusted to the body surface area. CKD-EPI formula was derived from the MDRD introduces a “correction” for patients with lower creatinine values. It is as accurate as MDRD in estimating GFR in patients with GFR <60 mL/min/1.73 m2 and better than MDRD at estimating higher GFR. Later, additional variables such as diabetes, hypertension, and unspecified transplantation were added to the equation. On the other hand, the Nankivell equation was derived from an Australian-Caucasian kidney transplant recipients who treated with calcineurin inhibitors.
There were marked heterogeneity between studies in terms of equation bias and accuracy of each formula. The mean bias for the CG and MDRD 4 variables were reported to be -4–16 mL/min/1.73 m2 and -11.4–9.2 mL/ min/1.73 m2, respectively. The MDRD equation was consistently found to underestimate the GFR in both transplant and nontransplant patients with relatively preserved kidney function., Whereas CG formula was reported to be better at estimating GFR in patient with normal serum creatinine. Nankivell formula was also reported to overestimates GFR significantly compared to measured GFR., with a mean bias from -1.4–36 mL/min/1.73 m2. In terms of accuracy, studies showed that CG, MDRD, and Nankivell equation fared only 73%, 76%, and 68%, respectively. It is also worth noted that all three equations demonstrates a progressive decrease in GFR overestimation and/or increase in GFR underestimation as the graft function improved.,
These equations are also insensitive to the detection of mild-to-moderate reductions in GFR, and the available equations also have poor sensitivity at detecting changes in GFR over time in renal transplant patients. A study measuring GFR in 40 kidney transplant patients at six, nine, and 21 months post transplant and compared it with various creatinine-based equations, concluded that none of the 12 equations tested can be a valid alternative to the direct measurement of the GFR and prediction equations may not be sufficient to properly estimating the GFR.
It is clear that a specific formula with better accuracy to assess graft function need to be developed to monitor renal transplant patients. Until then, clinician will continue to adopt any of the available serum creatinine-based equations in estimating GFR in the transplant recipient in their clinical practice. Hence, we embarked on this study to assess the performance of eGFR equations in Asian population and compared with the gold standard measured GFR (mGFR) using 51Cr-EDTA.
| Methodology|| |
This is a cross-sectional, single-center study involving adult renal transplant patients in the University Kebangsaan Malaysia Medical Centre (UKMMC), Kuala Lumpur, Malaysia from September 2014 to February 2016. The study was approved by the Medical Research and Ethics Committee of the same institution (Research Code: FF-2014-337) and Atomic Energy Licensing Body (AELB).
We screened all patients in our renal transplant clinic registry and those who fulfilled the inclusion criteria and consented to the study were recruited. The inclusion criteria were adult age >18 years old, who have had renal transplant >1 year and have stable serum creatinine of at least two readings with minimum of three months aparts. We excluded those with actual or suspected acute graft rejection within one month, any hospitali-zation or major surgery within one month, patients on dialysis, pregnant women, lactating mothers, and patients with hematological or solid organ malignancy.
Data collection and sample handling
A full medical history and physical examination, office blood pressure, and socioeconomic data of the participants were obtained during recruitment. Analysis of the serum creatinine was performed by kinetic Jaffe reaction using Abbot® ARCHITECT C-8000 System in our hospital laboratory and the assay was traceable to isotope dilution-mass spectrometry (IDMS). Estimation of GFR using serum creatinine-based formula was calculated using CG, MDRD, CKD-EPI, and Nankivell equations.
51Cr-EDTA at the dose of 1.0 μCi/kg body weight in 10 mL volume of 5% dextrose was injected intravenously. Approximately 6 mL of blood was drawn at 2, 2.5, 3, and 4 h post 51Cr-EDTA injection and it is clearance from the body was calculated using a preset calculation program. From the calculation of area under the plasma clearance curve of 51Cr-EDTA, a measured GFR of patient’s kidney graft was obtained. As 51Cr-EDTA is exclusively excreted by the glomerular, the GFR value calculated is considered a gold standard “measured” GFR for the patient and was shown to be reliable when compared with inulin clearance. The protocol used for 51Cr-EDTA was based on the Department of Radio-Nuclear Imaging and Nuclear Medicine UKMMC standard of procedure.
| Statistical Analysis|| |
All data were stored and analyzed using Statistical Package for Social Sciences (SPSS) version 22.0 (IBM Corp., Armonk, NY, USA). A descriptive analysis and normality testing of the variables recorded were performed. Normally distributed parameters were expressed as mean ± standard deviation and analyzed with parametric tests, whereas the nonnormally distributed parameters were expressed as median [interquartile range (IQR)] and analyzed with nonparametric test for quantitative variables. Values of P < 0.05 were considered statistically significant.
The performances of serum-based equations were also assessed by the following criteria:
- Correlations between mGFR using 51Cr-EDTA and eGFR using serum creatinine based equations were studied computing simple linear regression analysis. Pearson’s correlation coefficients were presented
- Bias = mean difference between estimated and measured GFR
- Precision = standard deviation of bias
- Agreement was assessed using the Bland and Altman method. For each GFR estimate, a graph was constructed by plotting the difference between the estimated and measured GFR against their mean (bias). Ninety-five percent of differences lie between the two limits defining the interval of the agreement: the lower limits which is the mean difference minus 2 standard deviations, and the upper limit, which is the mean difference plus 2 standard deviations
- Accuracy = percentage of GFR estimates that lie within 30% of their respective measured GFR value.
| Results|| |
A total of 37 patients with different ethnic background were recruited into the study. These include 25 Chinese, nine Malay, and three Indian patients. The mean age of the study population at the time of recruitment was 46 ± 13 years. From the data, almost half of our patients had living related renal transplant (LRRT) and the other half undergone non-LRRT. Their comorbidities included diabetes mellitus, hypertension, dyslipidemia, and ische-mic heart disease. Of all 11 patients with diabetes mellitus, five of them had diabetes after kidney transplantation. Other demographic data are summarized in [Table 1].
Serum creatinine, measured, and estimated GFR
The median serum creatinine was 109 (IQR 85,116) umol/L. The mean measured GFR by 51Cr-EDTA was 71 ± 21 mL/kg/1.73 m2 and the mean estimated GFR (eGFR) by four different formula are summarized in [Table 2].
|Table 2: Serum creatinine (umol/L), measured GFR 51Cr-EDTA and different methods of GFR estimations (mL/min/1.73 m2)|
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Correlation between creatinine-based formula
The relationship between estimated and measured GFR are shown in [Table 3] and displayed in [Figure 1]. Each GFR tests from serum creatinine-based equation displayed a highly significant correlation (P <0.001) with 51Cr-EDTA. The Pearson’s correlation coefficient (r) varied from 0.733 in Cockroft-Gault, 0.711 in CKD-EPI, 0.684 in MDRD, and 0.581 in Nankivell equations.
|Table 3: Pearson correlation between Cockroft-Gault, MDRD, CKD-EPI, and Nankivell vs 51Cr-EDTA.|
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|Figure 1: Correlation between eGFR using Cockroft-Gault, MDRD, CKD-EPI and Nankivell equations.|
eGFR: Estimated glomerular filtration rate, MDRD: Modification of Diet in Renal Disease, CKD-EPI: Chronic Kidney Disease Epidemiology Collaboration.
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Bias, precision, agreement, and accuracy
The standard deviation of bias between CG, CKD-EPI, and MDRD were comparable with values of 14.47 mL/min/1.73 m2, 15.36 mL/ min/1.73 m2 and 15.78 mL/min/1.73 m2. Nankivell had the worse precision with standard deviation of bias of 18.77 mL/min/1.73 m2. The mean bias, standard deviation of bias (precision) and limit of agreement of different formulae were summarized in [Table 4].
The Bland-Altman graph plotted the difference between predicted and measured GFR against their mean [Figure 2]. It display of large scattering data between 2 × standard deviations enlightening the poor agreement of each formula with 51Cr-EDTA. All four serum creatinine-based formulae showed >80% accuracy with eGFR lies between 30% of the measured value. CKD-EPI and MDRD had the greatest accuracy with 89.2% eGFR were within 30% of measured GFR [Table 5].
|Figure 2: The Bland-Altman graph plotted the difference between predicted and measured GFR against their mean. (a) Mean bias and limit of agreement between 51CrEDTA and Cockroft-Gault. (b) Mean bias and limit of agreement between 51CrEDTA and MDRD. (c) Mean bias and limit of agreement between 51CrEDTA and CKD-EPI. (d) Mean bias and limit of agreement between 51CrEDTA and Nankivell|
GFR: Glomerular filtration rate, MDRD: Modification of Diet in Renal Disease, CKD-EPI: Chronic Kidney Disease Epidemiology Collaboration.
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|Table 5: Percentage of various glomerular filtration rate estimation within 30% and 10% range of measured glomerular filtration rate values.|
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| Discussion|| |
In this study, we evaluated the performance of four different creatinine-based equations (Cockroft-Gault, MDRD, CKD-EPI, and Nankivell) against the gold standard 51Cr-EDTA. The recommended creatinine measurement methods should be traceable to the reference methods based on IDMS. Miller reported the mean bias varied from -0.06 to 0.31 mg/dL in 50 instrument methods among 5624 laboratories participating in the 2003 College of American pathologist survey. The study was based on serum creatinine measured using Jaffe method calibrated with IDMS reference values.
We found all four estimating GFR formulae were correlated strongly with the mGFR and in keeping with other literatures. Rodrigo et al had shown significant correlation between eGFR by CG, MDRD and Nankivell with their respective mGFR values. However, the correlation r varies between equations across all difference studies, ranging between 0.64 and 0.89 for CG, 0.63 to 0.91 for MDRD, and 0.62 to 0.88 for Nankivell equation.,.,,,,
Cockroft-Gault was the first equation developed to assess creatinine clearance in CKD population and subsequently used widely to estimate GFR. Nankivell in 1995 first assessed the performance of CG formula using 99mDTPA when he was assessing his newly developed eGFR formula in renal transplantation cohort. In this study, the mean mGFR reported was 52.4 mL/min/1.73 m2 and the correlation r was 0.71 in CG equation. Subsequently, Mourad et al showed CG had a slightly lower correlation with r of 0.64 compared to 51Cr-EDTA as a reference method. His renal transplant population had lower mean GFR of 48 ± 16 mL/min/1.73 m2. A better performance of CG were seen in two studies by Pöge et al and by Poggio et al where they reported a better correlation with r of 0.791 and 0.89, respectively. Our data reported a correlation r of 0.777 (P < 0.001). Even though the result was slightly differed, our value was comparable with the previous studies. As a matter of fact, CG showed the best correlation in our study. We concluded this was due to the fact that our cohort had better GFR with mean of 71 mL/min/1.73 m2. These findings were similar to a study by Estrada-Zambrano et al where he found that in 33 transplant recipients with estimate GFR >60 mL/min/ 1.73 m2, CG had a better correlation than MDRD (4 variable) with r of 0.660 and 0.605, respectively. Even though our study showed CG had a better correlation, it did not imply that CG had a better overall performance. The mean bias for CG from our study was 0.55 mL/min/1.73 m2 indicating that CG tends to overestimate GFR. The previous studies also demonstrated that CG overestimated GFR by +0.7 to +16 mL/min/1.73 m2., In contrast, two studies, reported that CG underestimated the GFR with mean bias of −3.3–−15.2 mL/min/1.73 m2 in a mixed transplant and nontransplant population. The performance results of MDRD equations were heterogeneous, but in overall, it showed a better performance than other creatinine based formula. The correlation r ranges from 0.63 to 0.91.,
The r values were also varied between MDRD 4, 6 and 7 variables with r of 0.857, 0.905, and 0.861, respectively. Gaspari et al reported in 81 transplant patients using Iohexol plasma clearance as a reference method, that the correlation r of modified MDRD (4 variables) were 0.769. His cohort had a mean GFR of 56.1 ± 14.5 mL/min/1.73 m2. All these values were slightly better than our correlation value of 0.684. The discrepancies may be due to the fact that our cohort had a better GFR with mean of 71 mL/min/1.73 m2. As we know MDRD was derived from CKD population with a lower GFR thus correlation among lower GFR group tends to be better. Gaspari et al also reported that MDRD overestimated the GFR by +2.7 mL/min/1.73 m2. Later studies, showed that MDRD underestimated the GFR by −11.4–−0.5 mL/min/1.73 m2 which in keeping with our results. Overall, our correlation r was statistically significant and mean bias were as comparable as previous studies.
We found that the r value in our study was slightly better in CKD EPI with value of 0.711 than 0.684 in MDRD. However, when we correlate these two equations together, it yielded the best correlation with r of 0.984 (P < 0.001) compared to CKD-EPI against other equations. CKD-EPI also underestimated the GFR in our study by -0.22 mL/min/1.73 m2. The results were comparable with White et al who reported a mean bias of -4.3 mL/min/ 1.73 m2. However, other study suggested that CKD-EPI tends to overestimate GFR by +3.9 mL/min/1.73 m2. Again all these studies were on population with lower GFR between 40 and 57 mL/min/1.73 m2 and were using different reference values such as 99mDTPA and inulin clearance.
We found that the Nankivell formula had a poor correlation and bigger mean bias in our study. The reported correlation r for Nankivell varied between 0.62 and 0.88., The mean bias for Nankivell also varies and had bigger range between +0.3 and +36.3 mL/min/1.73 m2., Nankivell over eGFR comparable to our study. We also found that Nankivell has a larger limit of agreement. It also had the worst performance with mean bias of +5.07 mL/min/ 1.73 m2 and its standard deviation of mean bias was large at 18.77 mL/min/1.73 m2 and the limit of agreement between 41.9 and -31.7 mL/min/1.73 m2. Hence, we concluded that Nankivell formula is inferior compared to other formula.
Even though our studies showed CG had best correlation, its accuracy between 30% of measured values fell short compared to CKD-EPI, 86.5% versus 89.2% respectively. Nonetheless, all our serum creatinine-based formula had accuracy above 80%. Both CKD-EPI and MDRD had the best accuracy with 89.2% values lie within 30% measured GFR. This was actually better than other study which reported accuracy between 72% and 88% in MDRD and 64% to 81% in CKD-EPI.,, CG also had significant accuracy with 82.6% comparable to a study by Buron et al with accuracy between 67% and 88%. Not much data available on Nankivell regarding its accuracy in estimating GFR.
Overall, the heterogeneity between all these four equations in different studies can be explained by several factors: (1) The population tested were different with regard to sample size and demographic characteristics, (2) Mean GFR and its distribution also varied between studies, (3) Difference reference methods used for measuring GFR (51CrEDTA, Iohexol or urinary clearance of 99mDTPA, 125I- Iothalamate, inulin), (4) Variation in standardization of the creatinine assays methods, or calibrating the creatinine assays method on an IDMS reference method.
There are few limitations in our study. First, the sample size is small. Second, we did not use CG or MDRD or CKD-EPI equations that are validated for the Asian population. However, from the result of this study, we support the recommendation by the KDIGO guideline to use the current CKD-EPI, MDRD, and CG formulae to estimate the GFR and to monitor graft function during routine clinics follow-up.
Conflict of interest: None declared.
| References|| |
Perrone RD, Madias NE, Levey AS. Serum creatinine as an index of renal function: New insights into old concepts. Clin Chem 1992; 38:1933-53.
National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: Evaluation, classification, and stratification. Am J Kidney Dis 2002;39:S1-266.
Levey AS, Eckardt KU, Tsukamoto Y, et al.
Definition and classification of chronic kidney disease: A position statement from kidney disease: Improving global outcomes (KDIGO). Kidney Int 2005;67:2089-100.
Butterworth PC, Harris KP, Nicholson ML. Comparison of glomerular filtration rate and serum creatinine in renal transplant patients. Transplant Proc 1997;29:2776-7.
Levey AS, Perrone RD, Madias NE. Serum creatinine and renal function. Annu Rev Med 1988;39:465-90.
Ross EA, Wilkinson A, Hawkins RA, Danovitch GM. The plasma creatinine concentration is not an accurate reflection of the glomerular filtration rate in stable renal transplant patients receiving cyclosporine. Am J Kidney Dis 1987;10:113-7.
Horber FF, Scheidegger J, Frey FJ. Overesti-mation of renal function in glucocorticosteroid treated patients. Eur J Clin Pharmacol 1985; 28:537-41.
Berglund F, Killander J, Pompeius R. Effect of trimethoprim-sulfamethoxazole on the renal excretion of creatinine in man. J Urol 1975; 114:802-8.
Kemperman FA, Surachno J, Krediet RT, Arisz L. Cimetidine improves prediction of the glomerular filtration rate by the Cockcroft-Gault formula in renal transplant recipients. Transplantation 2002;73:770-4.
Levey AS, Coresh J, Balk Eet al. National kidney foundation practice guidelines for chronic kidney disease: Evaluation, classification, and stratification. Ann Intern Med 2003; 139: 137-47.
Cockcroft DW, Gault MH. Prediction of crea-tinine clearance from serum creatinine. Nephron 1976;16:31-41.
Gault MH, Longerich LL, Harnett JD, Wesolowski C. Predicting glomerular function from adjusted serum creatinine. Nephron 1992; 62:249-56.
Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: A new prediction equation. Modification of Diet in Renal Disease study group. Ann Intern Med 1999;130:461-70.
Levey AS, Stevens LA, Schmid CH, et al.
A new equation to estimate glomerular filtration rate. Ann Intern Med 2009;150:604-12.
Nankivell BJ, Gruenewald SM, Allen RD, Chapman JR. Predicting glomerular filtration rate after kidney transplantation. Transplantation 1995;59:1683-9.
White CA, Huang D, Akbari A, Garland J, Knoll GA. Performance of creatinine-based estimates of GFR in kidney transplant recipients: A systematic review. Am J Kidney Dis 2008;51:1005-15.
Rule AD, Larson TS, Bergstralh EJ, Slezak JM, Jacobsen SJ, Cosio FG. Using serum crea-tinine to estimate glomerular filtration rate: Accuracy in good health and in chronic kidney disease. Ann Intern Med 2004;141:929-37.
White C, Akbari A, Hussain N, et al.
Estimating glomerular filtration rate in kidney transplantation: A comparison between serum creatinine and cystatin C-based methods. J Am Soc Nephrol 2005;16:3763-70.
Bostom AG, Kronenberg F, Ritz E. Predictive performance of renal function equations for patients with chronic kidney disease and normal serum creatinine levels. J Am Soc Nephrol 2002;13:2140-4.
Raju DL, Grover VK, Shoker A. Limitations of glomerular filtration rate equations in the renal transplant patient. Clin Transplant 2005; 19:259-68.
Gaspari F, Ferrari S, Stucchi N, et al.
Performance of different prediction equations for estimating renal function in kidney transplantation. Am J Transplant 2004;4:1826-35.
Bosma RJ, Doorenbos CR, Stegeman CA, van der Heide JJ, Navis G. Predictive performance of renal function equations in renal transplant recipients: An analysis of patient factors in bias. Am J Transplant 2005;5:2193-203.
Medeiros FS, Sapienza MT, Prado ES, et al.
Validation of plasma clearance of 51Cr-EDTA in adult renal transplant recipients: Comparison with inulin renal clearance. Transpl Int 2009; 22:323-31.
Miller WG. Reporting estimated GFR: A laboratory perspective. Am J Kidney Dis 2008; 52:645-8.
Rodrigo E, Fernandez-Fesnedo G, Castaneda O, Arias M. Estimation of renal function in adult kidney transplant recipients by equations. Transplant Rev 2007;21:1-16.
Mariat C, Alamartine E, Barthelemy JC, et al.
Assessing renal graft function in clinical trials: Can tests predicting glomerular filtration rate substitute for a reference method? Kidney Int 2004;65:289-97.
Pöge U, Gerhardt T, Palmedo H, Klehr HU, Sauerbruch T, Woitas RP. MDRD equations for estimation of GFR in renal transplant recipients. Am J Transplant 2005;5:1306-11.
Poggio ED, Wang X, Weinstein DM, et al.
Assessing glomerular filtration rate by estimation equations in kidney transplant recipients. Am J Transplant 2006;6:100-8.
Mourad A, Carney S, Gillies A, Hibberd A, Trevillian P, Nanra R. Measurement of glome-rular filtration rate in renal transplant recipients: A comparison of methods. Nephrology 2002;7:77-82.
Estrada-Zambrano A, Biosca-Adzet C, Bayés-Genís B, Doladé-Botias M, Lauzurica-Valdemoros R, Romero-González R. Evaluation of the equations to estimate the glomerular filtration rate in kidney transplant recipients. Transplant Proc 2007;39:2210-3.
Pierrat A, Gravier E, Saunders C, et al.
Predicting GFR in children and adults: A comparison of the Cockcroft-Gault, Schwartz, and modification of diet in renal disease formulas. Kidney Int 2003;64:1425-36.
Mariat C, Alamartine E, Afiani A, et al.
Predicting glomerular filtration rate in kidney transplantation: Are the K/DOQI guidelines applicable? Am J Transplant 2005;5:2698-703.
Buron F, Hadj-Aissa A, Dubourg L, et al.
Estimating glomerular filtration rate in kidney transplant recipients: Performance over time of four creatinine-based formulas. Transplantation 2011;92:1005-11.
White CA, Knoll GA, Poggio ED. Measuring vs. estimating glomerular filtration rate in kidney transplantation. Transplant Rev (Orlando) 2010;24:18-27.
Department of Medicine, Nephrology Unit, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur 56000
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]
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