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Saudi Journal of Kidney Diseases and Transplantation
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Year : 2012  |  Volume : 23  |  Issue : 4  |  Page : 827-831
Evaluation and applicability of predictive equations of the glomerular filtration rate in chronic kidney disease


Department of Biochemistry, PSG Institute of Medical Sciences and Research, Coimbatore, Tamilanadu, India

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Date of Web Publication9-Jul-2012
 

   Abstract 

Prediction equations of glomerular filtration rate (GFR) may facilitate early detec­tion, evaluation and management of chronic kidney disease (CKD). However, the reliability of these equations was not extensively studied in our CKD population. Hence, the present study was aimed to determine the performance of modification of diet in renal disease (MDRD) and Cock-croft Gault formulas in predicting GFR in CKD patients and their relationship with the measured GFR. A total of 104 subjects (71 male and 33 female, aged 26-68 years) with different stages of CKD were recruited for this study; we excluded 51 patients due to improper collection of 24-h urine. The GFR was measured using 24-h creatinine clearance and predicted by the Cockcroft Gault, the 4-variable MDRD and the 6-variable MDRD equations. Prediction equations corre­lated well with the measured GFR. However, the predicted GFR using the 4-variable MDRD equation revealed a highly significant positive correlation with the GFR measured by creatinine clearance (r = 0.86, P < 0.001), followed by the 6-variable MDRD and Cockcroft-Gault equations with r = 0.85 and 0.77, P < 0.001, respectively. In conclusion, the present study predicts that the 4-variable MDRD is the best available equation for predicting GFR in our CKD population.

How to cite this article:
Raghul M, Kannapiran M, Rao AM. Evaluation and applicability of predictive equations of the glomerular filtration rate in chronic kidney disease. Saudi J Kidney Dis Transpl 2012;23:827-31

How to cite this URL:
Raghul M, Kannapiran M, Rao AM. Evaluation and applicability of predictive equations of the glomerular filtration rate in chronic kidney disease. Saudi J Kidney Dis Transpl [serial online] 2012 [cited 2018 Nov 15];23:827-31. Available from: http://www.sjkdt.org/text.asp?2012/23/4/827/98173

   Introduction Top


Chronic kidney disease (CKD) is a major health problem that results in end-stage renal disease (ESRD), and is associated with in­creased risk of morbidity and mortality. [1] Hence, early identification and management of these patients may delay the progression of renal disease. Measuring glomerular filtration rate (GFR) is widely accepted as the best overall index of kidney function. However, all the methods for the assessment of GFR have limitations. For instance, direct measurement of GFR with timed urine collection is incon­venient as a result of improper collection and overestimation of GFR due to renal tubular secretion of creatinine. [2] Inulin clearance and radioisotopes are widely regarded as the gold standard for measuring GFR, but they are ex­pensive, time consuming and require hospitalization. [3] Therefore, efforts have been directed at more convenient "urine-free" estimates of GFR.

More recently, calculation of GFR using pre­diction equations has been encouraged as a simple, rapid and reliable means of assessing kidney function. Among 46 different predic­tive equations, the most commonly used are Cockcroft Gault formula, [4] Modification of Diet in Renal Disease (MDRD) formula, [5] Schwartz equation for adults [6] and Counahan Barrett equation for children. [7] Recently, national kid­ney foundations have advocated the use of the predictive formulas for the evaluation of kid­ney disease; [8] the Kidney Disease Outcomes Quality Initiative (K/DOQI) clinical practical guidelines for evaluation and stratification of CKD have also recommended the use of the predictive formulas for estimating GFR. [9]

The present study was aimed to determine the best predictive formula to employ in clinical practice for the estimation of GFR in our CKD population.


   Materials and Methods Top


We studied the patients followed-up in the Nephrology clinics of the PSG hospitals, Coimbatore, India, from July 2007 to Novem­ber 2008. Informed consents were obtained from the patients and the study was approved by the Institutional Human Ethics Committee. A total of 104 CKD patients (71 male and 33 female, aged 26-68 years) were recruited for the study; 51 patients were excluded due to improper collection of 24-h urine. Patients with age <20 years were excluded from the study as the MDRD equation was not appli­cable. Renal transplant patients and patients on dialysis were also excluded from the study. The causes of CKD included diabetes mellitus and hypertension.

Samples of blood were collected from the patients using vacutainers (BD, Franklin Lakes, USA). The samples were centrifuged for 15 min at 2000 rpm and serum was separated and stored at -20°C until analysis. Serum creatinine was measured by the kinetic compensated Jaffe assay on an Integra 400 analyser (cat. no. 20764345; Roche Diagnostics Ltd. Burgess Hill, West Sussex, UK). To compensate for non-creatinine chromogens, the values were automatically corrected by subtracting 0.2 mg/dL. Quality control checks were done twice daily. The between-day coefficients of varia­tion were 3.2% and 2.7% at concentrations of 1.73 mg/dL and 5.24 mg/dL, respectively. No significant interference was seen with hemo­globin up to 800 mg/dL and with bilirubin 5 mg/dL. Serum urea, total proteins and albumin were measured using the standard clinical che­mistry procedure on a Cobas Integra 400 plus auto analyzer, using Roche reagents. The 24-h urine samples were collected to calculate creatinine clearance. GFR was measured using the conventional creatinine clearance formula:

GFR = Ucr × V/Pcr × 1.73 / BSA (estimated by Mosteller, formula) [10]

Ucr - urine creatinine (mg/dL); V - volume of 24-h urine collection; Pcr - plasma creatinine (mg/dL); BSA - body surface area

GFR was predicted using the following equa­tions:

Cockcroft and Gault formula: [4] GFR = [(140 - age) × weight × 0.85 (if female) × 1.73 / 72 SCr × BSA]

Re-expressed 6-variable MDRD study [5] equation: GFR = 161.5 × Scr -0 .999 × age × S. urea -0 .176 × S. albumin 0 .318 × 1.18 (if black)× 0.762 (if female) 3. Re-expressed 4-variable MDRD equation: [11] GFR = 175 × standardized Scr -1 .154 × age -0 .203 × 1.212 (if black) × 0.742 (if female)


   Statistical Analysis Top


All the values obtained were expressed as mean and standard deviation (SD). Statistical analysis was carried using Medcalc Software. Wilcoxon's test was applied to compare the difference between the means and to assess the significance. The differences were considered as significant if the P-value was <0.05. Linear regression analysis and Spearman's correlation were used to identify the association between the variables.


   Results Top


[Table 1] shows the mean and standard devia­tion, range and median of anthropometric and biochemical characteristics of 53 CKD patients (30 male and 23 female).
Table 1: Demographic data of the study patients.

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[Table 2] shows the mean values for the esti­mated GFR using the predictive equations and compared with GFR measured by 24-h creatinine clearance of the study patients. The dif­ference between the predicted GFR using Cockcroft Gault, 6-variable MDRD and 4-variable MDRD equations and the creatinine clearance were 11.96 ± 6.40, 17.37 ± 5.76 and 21.43 ± 3.18 mL/min, respectively.
Table 2: Mean and standard deviation of the estimated and measured GFR in the study patients.

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[Table 3] shows the correlation of the 24-h creatinine clearance with other variables of the study. The estimated GFR using the predictive equations (Cockcroft-Gault, 6-variable MDRD, 4-variable MDRD) correlated well with the 24-h creatinine clearance (r = 0.77, 0.85 and 0.86, respectively, P <0.001).
Table 3: Correlation between the GFR measured with creatinine clearance and the GFR estimated using predictive equations.

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[Table 4] shows the results of multiple regres­sion analysis. Creatinine clearance was consi­dered as the dependent variable and the esti­mated GFR by the Cockcroft-Gault, the 6-variable MDRD and the 4-variable MDRD equations were considered as the independent variables. In this model of analysis, the 4-variable MDRD was superior to the Cockcroft-Gault and the 6-variable MDRD equations, although it was not statistically significant (P = 0.56).
Table 4: Multiple linear regression analysis for the determinants of the GFR.

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   Discussion Top


In the present study, the GFR estimated using the predictive equations in CKD patients showed high precision with the measured GFR using 24-h creatinine clearance. Lamb et al, [12] tested the accuracy of prediction equations for medical diagnosis, and reported that the Cock-croft-Gault, the 6-variable MDRD and the 4-variable MDRD equations showed the greatest precision (r = 0.83, 0.84 and 0.84, respec­tively). Similar findings were found in the Levey et al, study also. [10] Furthermore, studies have confirmed that the 4-variable MDRD equation shows generally superior precision and accuracy than the Cockcroft-Gault formula in CKD patients (GFR <60 mL/min/1.73 m 2 ). [12],[13],[14],[15]

Furthermore, both the MDRD equations cor­related well with the creatinine clearance, with relatively more advantage for the 4-variable than the 6-variable MDRD equation. [12],[15] Simi­lar association was observed in the linear re­gression model in our study; however, it was not statistically significant. We acknowledge that this is due to the small sample size. More­over, the 4-variable MDRD equation is simpler to use because it does not require inclusion of serum urea nitrogen levels or serum albumin concentration, which would also require calib­ration among laboratories for optimal use.

There are certain limitations in this study that ought to be mentioned. First, we did not diffe­rentiate the patients into different stages. Thus, results of the present study cast doubt on the use of GFR prediction equations near normal renal function as shown by several other studies [16],[17],[18] that observed a significant impreci­sion of the predictive equations compared with iodo-thalamate GFR in subjects with normal or near-normal kidney function. Moreover, the pre­dictive equations tend to be more biased with significant underestimation of the measured GFR in subjects with normal or near-normal function Accordingly, the prediction equations should be used with caution in patients with GFR >60 mL/min/1.73 m 2 . A second short­coming of our study is the lack of a "gold standard" for measurement of GFR, which is difficult in our population due to cost com­plexity. Hence, we compared the estimated with the measured GFR using 24-h creatinine clea­rance. Other limitations include the small sample size and the cross-sectional design.

In conclusion, this study suggests that the 4-variable MDRD has better estimation of the GFR compared with the 6-variable MDRD and Cockcroft-Gault formulas. Validation in a lar­ger population-based study is warranted.


   Acknowledgments Top


The authors are indebted to the PSG Hospitals, Coimbatore, for their enduring support.

 
   References Top

1.Udayakumar N. Chronic kidney disease in India: From a resident's physician's perspec­tive. Post grad Med J 2006;82:697-8.  Back to cited text no. 1
[PUBMED]  [FULLTEXT]  
2.Johnson CA, Levey AS, Coresh J, Levin A, Lau J, Eknoyan G. Clinical practice guidelines for chronic kidney disease in adults: Part II. Glomerular filtration rate, proteinuria and other markers. Am Fam Physician 2004;70:1091-7.  Back to cited text no. 2
[PUBMED]  [FULLTEXT]  
3.Al Wakeel JS, Hammad D, Al Suwaida A, et al. Validation of predictive equations for glomerular filtration rate in the Saudi population. Saudi J Kidney Dis Transpl 2009;20:1030-7.  Back to cited text no. 3
    
4.Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron 1976;16:13-41.  Back to cited text no. 4
[PUBMED]    
5.Levey AS, Coresh J, Greene T, Stevens LA, Zhang Y. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med 2006; 145:247-54.  Back to cited text no. 5
    
6.Schwartz GJ, Haycock GB, Edelmann CM, Sitzer A. A simple estimate of glomerular fil­tration rate in children derived from body length and plasma creatinine. Pediatrics 1976;5 8:259-63.  Back to cited text no. 6
    
7.Counahan R, Chantler C, Ghazali S, Krisiwood B, Rose F, Barratt TM. Estimation of glomerular filtration rate from plasma creatinine in children. Arch Dis Child 1976;51:875-8.  Back to cited text no. 7
    
8.National Kidney Foundation K/DOQI: Clinical practice guidelines for chronic kidney disease: Evaluation, Classification and stratification. Am J Kidney Dis 2002;39:S1-2000.  Back to cited text no. 8
    
9.K/DOQI: Clinical practice guidelines for chronic kidney disease: Evaluation, Classification and stratification. Am J Kidney Dis 2002;39:S1-266.  Back to cited text no. 9
    
10.Mosteller RD. Simplified calculation of body surface area. N Engl J Med 1987;317:1098.  Back to cited text no. 10
[PUBMED]  [FULLTEXT]  
11.Levey AS, Coresh J, Greene T, et al. Expres­sing the MDRD study equation for estimating GFR with IDMS traceable (gold standard) serum creatinine values. J Am Soc Nephrol 2005;16:69.  Back to cited text no. 11
    
12.Lamb EJ, Webb MC, Simpson DE, Coakley AJ, Newman DJ, Riordan SE. Estimation of glomerular filtration rate in older patients with chronic renal insufficiency: Is the Modification of Diet in Renal Disease formula an im­provement? J Am Geriatrics Soc 2003;51: 1012-7.  Back to cited text no. 12
    
13.Froissart M, Rossert J, Jacquot C, Paillard M, Houillier P. Predictive performance of the modification of diet in renal disease and Cockcroft-Gault equations for estimating renal function. J Am Soc Nephrol 2005;16:763-73.  Back to cited text no. 13
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14.Poggio ED, Wang X, Greene T, Van Lente F, Hall PM. Performance of the modification of diet in renal disease and Cockcroft-Gault equations in the estimation of GFR in health and in chronic kidney disease. J Am Soc Nephrol 2005;16:459-66.  Back to cited text no. 14
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15.Coresh J, Stevens LA. Kidney Function estimating equations: WHERE do we stand? Curr Opin Nephrol Hypertene 2006;15:276-84.  Back to cited text no. 15
[PUBMED]  [FULLTEXT]  
16.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.  Back to cited text no. 16
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17.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.  Back to cited text no. 17
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18.Stevens LA, Coresh J, Greene T. Assessing kidney function- Measured and Estimated Glomerular filtration rate. N Engl J Med 2006; 354:2473-83.  Back to cited text no. 18
    

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Correspondence Address:
Manickavasagam Kannapiran
Department of Biochemistry, PSG Institute of Medical Sciences and Research, Coimbatore - 641 004, Tamilanadu
India
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DOI: 10.4103/1319-2442.98173

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