|Year : 2009 | Volume
| Issue : 6 | Page : 1030-1037
|Validation of predictive equations for glomerular filtration rate in the Saudi population
Jamal S Al Wakeel1, Durdana Hammad1, Abdulkareem Al Suwaida1, Nauman Tarif1, AbdulRauf Chaudhary2, Arthur Isnani2, Waleed Ahmed Albedaiwi3, Ahmad H Mitwalli1, Shaik Shaffi Ahmad4
1 Division of Nephrology, Department of Medicine, King Khalid Hospital, King Saud University, Riyadh, Saudi Arabia
2 Division of Nephrology, Research centre, King Khalid Hospital, King Saud University, Riyadh, Saudi Arabia
3 King Fahad National Guard Hospital Riyadh, Saudi Arabia
4 Division of Nephrology, Department of Community Medicine, King Khalid Hospital, King Saud University, Riyadh, Saudi Arabia
Click here for correspondence address and email
|Date of Web Publication||27-Oct-2009|
| Abstract|| |
Predictive equations provide a rapid method of assessing glomerular filtration rate (GFR). To compare the various predictive equations for the measurement of this parameter in the Saudi population, we measured GFR by the Modification of Diet in Renal Disease (MDRD) and Cockcroft-Gault formulas, cystatin C, reciprocal of cystatin C, creatinine clearance, reciprocal of creatinine, and inulin clearance in 32 Saudi subjects with different stages of renal disease. We com-pared GFR measured by inulin clearance and the estimated GFR by the equations. The study included 19 males (59.4%) and 13 (40.6%) females with a mean age of 42.3 ± 15.2 years and weight of 68.6 ± 17.7 kg. The mean serum creatinine was 199 ± 161 μmol/L. The GFR measured by inulin clearance was 50.9 ± 33.5 mL/min, and the estimated by Cockcroft-Gault and by MDRD equations was 56.3 ± 33.3 and 52.8 ± 32.0 mL/min, respectively. The GFR estimated by MDRD revealed the strongest correlation with the measured inulin clearance (r= 0.976, P= 0.0000) followed by the GFR estimated by Cockcroft-Gault, serum cystatin C, and serum creatinine (r= 0.953, P= 0.0000) (r= 0.787, P= 0.0001) (r= -0.678, P= 0.001), respectively. The reciprocal of cystatin C and serum creatinine revealed a correlation coefficient of 0.826 and 0.93, respectively. Cockroft-Gault formula overestimated the GFR by 5.40 ± 10.3 mL/min in comparison to the MDRD formula, which exhibited the best correlation with inulin clearance in different genders, age groups, body mass index, renal transplant recipients, chronic kidney disease stages when compared to other GFR predictive equations.
|How to cite this article:|
Al Wakeel JS, Hammad D, Al Suwaida A, Tarif N, Chaudhary A, Isnani A, Albedaiwi WA, Mitwalli AH, Ahmad SS. Validation of predictive equations for glomerular filtration rate in the Saudi population. Saudi J Kidney Dis Transpl 2009;20:1030-7
|How to cite this URL:|
Al Wakeel JS, Hammad D, Al Suwaida A, Tarif N, Chaudhary A, Isnani A, Albedaiwi WA, Mitwalli AH, Ahmad SS. Validation of predictive equations for glomerular filtration rate in the Saudi population. Saudi J Kidney Dis Transpl [serial online] 2009 [cited 2020 Apr 2];20:1030-7. Available from: http://www.sjkdt.org/text.asp?2009/20/6/1030/57259
| Introduction|| |
Chronic kidney disease (CKD) is a major health problem worldwide that results in endstage renal disease (ESRD), and is associated with increased risk of morbidity and mortality. Furthermore, it is an established risk factor for cardiovascular diseases. ,, Glomerular Filtration Rate (GFR) is a useful index of filtering capacity of kidney, and a decrease in GFR indicates progression to CKD and kidney failure.  Direct measurement of GFR is time consuming and difficult to perform routinely.  All the methods for the assessment of GFR have some or the other limitations.  For instance, serum creatinine is affected by muscle, age, liver disease, inflammation, muscle wasting. , Inulin clearance and radioisotopes are widely regarded as gold standard for measuring GFR but they are expensive, time consuming and require hospitalization. Cystatin C is more sensitive than serum creatinine in determining the deterioration of GFR. ,, Recently the National Kidney foundation has advocated the use of predictive for mulas for the evaluation of CKD, , and the K/DOQI clinical practice guidelines for evaluation and stratification of CKD have also recommended the use of predictive formulas for estimation of GFR. ,
The Cockcroft-Gault  and the Modification of Diet in Renal Disease  (MDRD) formulas are the most frequently used equations to estimate GFR. Unfortunately, their predictive capacity is affected by ethnicity,  muscle mass, diet, liver disease  and degree of renal impairment.  Their validity in healthy persons without kidney disease, and gender differences are not thoroughly evaluated.
We aim in this study to determine the best formula to use for the estimation of GFR in the Saudi healthy individuals and CKD patients of different stages and kidney transplant patients.
| Methods|| |
This is a cross sectional study conducted on patients followed-up in the nephrology clinics of the King Khalid University Hospital (KKUH), King Saud University, Riyadh, Saudi Arabia from January 2005 to June 2007.
We included in the study patients older than 18 years with the diagnosis of CKD as per K/DOQI guidelines for stratification of CKD,  post-kidney transplant patients stable for three months, and healthy subjects without any renal disease and not on any medication. We excluded patients with acute renal failure, edema, heart failure, ascites, pregnancy, or infection. Informed consent was obtained from all the participants in the study.
A total of 32 patients were recruited for the study; 15 CKD and 9 post-kidney transplant patients and 8 healthy subjects. We collected Data that included gender, age, weight, height, body surface area (BSA), and blood pressure (BP). In all the subjects, blood samples were drawn for estimation of serum cystatin C and serum creatinine simultaneously during inulin clearance tests.
GFR was calculated using the following predictive formulas:
1. The Cockcroft-Gault (CG) formula:
GFR = ((140 - Age (years) Χ Weight (kg) Χ 1.23/serum creatinine (μmols) for males
GFR = ((140 - - age (yrs) Χ Weight (kg) Χ 1.02 /serum creatinine (μmols) for females
2. The MDRD formula:
GFR = 1.86 Χ (Scr) -1.154 Χ (age) -0.203 Χ (0.742 if patient is female)
Measurement of GFR by inulin clearance test:
Inulin clearance test was performed in all the patients, who fasted over night and were off concurrent therapy.
Vital signs were monitored, and 2 i.v. canulas were placed in both arms, one for blood sample extraction and the other for inulin injection and infusion. Urine and blood samples were obtained at zero time for biochemical analysis. Loading dose of inulin was administered according to the manufacturer's instructions.
The loading dose was administered as 250 mg/L Χ 15 % of the total body weight (TBW).
The loading dose was followed by a constant infusion of inulin at a calculated rate = Required plasma concentration Χ Estimated GFR by Cockcroft-Gault Formula mL/min.
Oral hydration of 100 mL/hr was maintained. Blood samples (15 mL) were drawn from the arm opposite to the infusion site. After 60 minutes of infusion, the first urine and blood sample were collected, followed by sampling every 30 minutes until 5 hours for inulin concentration measurement. GFR was measured as:
Where U in and P in are inulin concentration in the urine and plasma and V is the urine flow rate mL/min.
Estimated GFR by serum cystatin C:
Serum cystatin C was measured by Nepheloimmunoassay as described in our previous published studies. ,, Serum creatinine was analyzed by Jaffe's method in the central laboratories at KKUH using the third generation automated clinical chemistry Dimension RxL analyzer (Dade Behring Inc, Germany) and the commercially available assay kits and reagents.
| Statistical Analysis|| |
Quantitative variables such as age, height, weight, BSA, BMI, serum creatinine, and GFR are presented as the means ± standard deviations. Statistical analysis was carried out using SPSS 11.5 for Windows. The estimated GFR by using the Cockcroft-Gault and MDRD formulas, reciprocal of cystatin C, and reciprocal of serum creatinine was compared with the measured by inulin clearance. Bland and Altman plots and Pearson's correlation were used. The level of the significance between the difference of GFR estimated by the Cockcroft Gault formula and inulin clearance and that between the MDRD formula and inulin clearance was calculated using Mann Whitney Utest. The P value < 0.05 was considered significant.
| Results|| |
The participants in the study included 19 males (59.4%) and 13 (40.6%) females with a mean age of 42.3 ± 15.2 years (19-74 years). The mean height was 160 ± 10.5 cm (134-178 cm), the mean weight was 68.6 ± 17.7 kg (42.6125.7 kg), and the mean body surface area was 1.7 ± 0.2 (1.35-2.55). The mean serum creatinine was 198 ± 161 (51-815 μmol/L). The mean measured GFR by the inulin clearance test was 50.87 ± 33.5 mL/min (5.47-128 mL/ min), [Table 1].
The estimated GFR by the MDRD formula was 52.7 ± 32 mL/min (7-123 mL/min), and 56.3 ± 33.3 mL/min (10.2 ± 128 mL/min) by the Cockroft-Gault formula, [Table 2]. The difference between the estimated GFR by the MDRD and the Cockroft-Gault formulas and that by inulin clearance were 0.464 ± 7.3 and -5.44 ± 10.2 mL/min, respectively, P= 0.001. Both predictive equations correlated well with inulin clearance with relative more advantage for the MDRD than the Cockroft-Gault formula (r = 0.976, P = 0.0000, r = 0.953, P = 0.0000, respectively), [Table 3]. Further Bland and Altman plots demonstrated that the estimated GRF by the MDRD formula was most accurate when compared with gold standard inulin clearance [Figure 1] and [Figure 2].
A linear relationship was observed between GFR Inulin and. A linear relationship was found between GFR estimated by the Cockroft-Gault and the MDRD formulas and inulin clearance (y = 0.9456 x + 8.21), R 2 =0.9073 [Figure 3] and (y = 0.9706 x + 1.14), R2 = 0.9518, [Figure 4], respectively. In addition, the estimated GFR by the other markers including cystatin C and serum creatinine, and their reciprocals exhibited a linear relationship with the GFR measured by inulin clearance. However, the correlation coefficients for these markers (cystatin C: r = - 0.76, serum creatinine: r = -0.687, reciprocal of cystatin C: r = 0.852, and reciprocal of serum creatinine: r = 0.929) were inferior to those obtained from the MDRD and the CockroftGault formulas.
We tested the versatility of the predictive efficacy of the formulas for estimation of GFR including(Cockcroft-Gault), MDRD, Cystatin C and serum creatinine for the different subgroups of patients with different gender, age, groups, BMI, and stages of kidney disease. We found that MDRD ranked the best amongst all GFR measured by MDRD MDRD was closest to the Inulin-GFR, [Table 3].
| Discussion|| |
Frequent and regular GFR assessments are mandatory to monitor the progression of CKD and the stability of the kidney transplant recipients. It can guide therapy and early interventions for complications. Measurement of true GFR is time consuming and difficult to perform. , Therefore, various formulas have been developed to provide convenience for physicians. The gold standard inulin clearance used for measuring GFR is also time consuming and impractical for clinical practice. 
Predictive formulas for estimation of GFR are rapid, feasible, and costless tools for follow-up of CKD patients. The Cockcroft-Gault and the MDRD formulas are the most popular to estimate GFR, , However, the prediction efficacy of these equations is not versatile, and is affected by body mass, ethnicity, status of renal function, and other factors. For instance the MDRD formula is considered unsuitable for Asians,  while it is accurate for Chinese. , Furthermore, Coresh  and Ying Kuan et al  reported that the MDRD formula better performed in the low range of GFR than the Cockcroft-Gault formula. In 2007, Ma YC emphasized that the validation of these formulas was necessary in the different ethnic populations and a modified MDRD formula was necessary to fit for the Chinese population, and earlier in 2005, Zuo  found that MDRD equation overestimated GFR in patients with CKD stages 4 and 5, while it underestimated GFR in CKD stage1.
Our results demonstrate the GFR estimated by the predictive formulas is more accurate and close to that measured by the inulin clearance test than the reciprocal of serum cystatin C or the reciprocal of serum creatinine, and the GFR estimated by the MDRD formula revealed the best correlation with that measured by the inulin clearance across ages, gender, different clinical presentations, and low and high GFR levels. Our results are in agreement with previous reports that Cockcroft-Gault formula overestimated GFR compared with the inulin clearance in all kidney disease stages.  This reduces the sensitivity of Cockcroft-Gault formula for estimating the GFR in CKD patients and their staging. 
Large frame individuals are not infrequent in the Saudi population, and fluid overload in CKD patients can overestimate their weight. The weight-dependant Cockcroft-Gault formula overestimates GFR, while the weight-independent MDRD formula does not overestimate GFR in individuals with various BMI. Similar to our results, the MDRD formula was considered the best option for estimation of GFR in the Japanese population as reported by Imai et al in 2007. 
In conclusion, our study found that the GFR estimated by the MDRD predictive formula equation revealed the best correlation with measured GFR by the inulin clearance test followed by that estimated by the Cockcroft-Gault formula. Serum cystatin C, serum creatinine, reciprocal of cystatin C, and reciprocal of serum creatinine were inferior to the MDRD and the Cockcroft-Gault predictive formulas in estimating GFR in healthy Saudis and patients with CKD and after renal transplantation.
| Acknowledgement|| |
Funded and Supported by King AbdulAziz City of Science and Technology (KACST) and King Saud University, Riyadh Saudi Arabia.
| References|| |
|1.||Weiner DE, Tighiouart H, Amin MG, et al. Chronic kidney disease as a risk factor for cardiovascular disease and all-cause mortality: a pooled analysis of community-based studies. J Am Soc Nephrol 2004;15(5):1307-15. |
|2.||Meisinger C, Doring A, Lowell H; KORA Study Group. Chronic kidney disease and risk of incident myocardial infarction and all-cause and cardiovascular disease mortality in middleaged men and women from the general population. Eur Heart J 2006;27(10):1245-50. |
|3.||Weiner DE, Tabatabai S, Tighiouart H, et al. Cardiovascular outcomes and all-cause mortality: exploring the interaction between CKD and cardiovascular disease. Am J Kidney Dis 2006;48:392-401. [PUBMED] [FULLTEXT] |
|4.||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. [PUBMED] [FULLTEXT] |
|5.||Vervoort G, Willems HL, Wetzels JF. Assessment of glomerular filtration rate in healthy subjects and normoalbuminuric diabetic patients: validity of a new (MDRD) prediction equation. Nephrol Dial Transplant 2002;17: 1909-13. [PUBMED] [FULLTEXT] |
|6.||Shemesh O, Golbetz H, Kriss JP, et al. Limitations of creatinine as a filtration marker in glomerulopathic patients. Kidney Int 1985; 28:830-3. [PUBMED] |
|7.||MacAulay J, Thompson K, Kiberd BA, Barnes DC, Peltekian KM. Serum creatinine in patients with advanced liver disease is of limited value for identification for moderate renal dysfunction: are the equations for estimating renal function better? Can J Gastroenterol 2006;20(8):521-6. |
|8.||Wilson DM, Bergert JH, Larson T, Robert Liedtke GF. Determined by Nonradiolabeled Using Capillary Electrophoresis. Am J Kidney Dis 1997;30:646-52. |
|9.||Tarif N, Alwakeel JS, Mitwalli AH, et al. Isnani Serum Cystatin C as a marker of renal function in acute renal failure patients. Saudi J Kidney Dis Transplant 2008;19:918-23. |
|10.||Al Wakeel JS, Memon NA, Chaudhary A, et al. Normal reference levels of serum cystatin C in Saudi adults. Saudi J Kidney Dis Transplant 2008;19(3):361-70. |
|11.||Babay Z, Al-Wakeel J, Addar M, et al. Serum cystatin C in pregnant women: reference values, reliable and superior diagnostic accuracy. Clin Exp Obstet Gynecol 2005;32(3):175-9. |
|12.||Lin J, Knight EL, Hogan ML, Singh AK. A Comparison of Prediction Equations for Estimating Glomerular Filtration Rate in Adults without Kidney Disease. J Am Soc Nephrol 2003;14:2573-80. [PUBMED] [FULLTEXT] |
|13.||National Kidney Foundation K/DOQI: Clinical Practice Guidelines for chronic kidney disease: Evaluation, classification, and stratification. Am J Kidney Dis 2002;39:S1-S2000. |
|14.||K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis 2002;39: S1-266. |
|15.||Poggio ED, Nef PC, Wang X, Greene T, Van Lente F, Hall PM. Performance of the modification of diet in renal disease and CockcrofGault equations in the estimation of GFR in health and in chronic kidney disease. J Am Soc Nephrol 2005;16:459-66. |
|16.||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. |
|17.||Ma YC, Zuo L, Chen JH, et al. Modified Glomerular Filtration Rate Estimating Equation for Chinese Patients with Chronic Kidney Disease. J Am Soc Nephrol 2006;17:2937-44. [PUBMED] [FULLTEXT] |
|18.||Kuan Y, Hossain M, Surman J, El Nahas AM, Haylor J. GFR prediction using the MDRD and Cockcroft and Gault equations in patients with end-stage renal disease. Nephrol Dial Transplant 2005;20(1):2394-401. |
|19.||Ibrahim H, Mondress M, Tello A, Fan Y, Koopmeiners J, Thomas W. An Alternative Formula to the Cockcroft-Gault and the Modification of Diet in Renal Diseases Formulas in Predictive GFR in Individuals with Type 1 Diabetes. J Am Soc Nephrol 2005;16:1501-6. |
|20.||Risch L, Blumberg A, Huber A. Rapid and accurate assessment of glomerualr filtration rate in patients with renal transplants using serum Cystatin C. Nephrol Dial Transplant 1994;14:1991-6. |
|21.||Hoek FJ, Kemperman FA, Krediet RT. A comparison between Cystatin C, plasma creatinine and the Cockcroft and Gault formula for the estimation of glomerular filtration rate. Nephrol Dial Transplant 2003;18:2024-31. [PUBMED] [FULLTEXT] |
|22.||Zuo L, Ma YC, Zhuo YH, Wang M, Xu GB, Wang HY. Application of GFR-estimating equations in Chinese patients with chronic kidney disease. Am J Kidney Dis 2005;45: 463-72. |
|23.||Jafar TH, Schmid, Levey AS. Serum creatinine as a marker of kidney function in South Asians: A study of reduced GFR in adults in Pakistan. J Am Soc Nephrol 2005;16:1413-9. |
|24.||Perrone RD, Madias NE, Levey AS. Serum creatinine as an index of renal function: new insights into old consepts. Clin Chem 1992; 38:1993-53. |
|25.||Imal E. Horio M, Nitta K, et al. Modification of the Modification of Diet in Renal Disease (MDRD) Study equation for Japan. Am J Kidney Dis 2007;50(6):927-37. |
|26.||Coresh J, Stevens LA. Kidney Function estimating equations: Where do we stand? Curr Opin Nephrol Hypertene 2006;15(3):276-84. |
Jamal S Al Wakeel
Department of Medicine (38), King Khalid University Hospital, P.O. Box 2925, Riyadh 11461
[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2], [Table 3]
|This article has been cited by|
||Routine determination of GFR in renal transplant recipients by HPLC quantification of plasma iohexol Concentrations and comparison with estimated GFR
| ||Castagnet, S. and Blasco, H. and Vourcćh, P. and Benz-De-Bretagne, I. and Veyrat-Durebex, C. and Barbet, C. and Alnajjar, A. and Ribourtout, B. and Buchler, M. and Halimi, J.-M. and Andres, C.R. |
| ||Journal of Clinical Laboratory Analysis. 2012; 26(5): 376-383 |
| Article Access Statistics|
| Viewed||3273 |
| Printed||101 |
| Emailed||0 |
| PDF Downloaded||697 |
| Comments ||[Add] |
| Cited by others ||1 |