Estimating Glomerular Filtration Rate in 2012
We have recently shown that the AA ethnic factor is probably too high (compared with Caucasians) in the MDRD study equation when applied to subjects with an eGFR above 60 ml/min/1.73 m2.5
The third point concerns the prevalence of CKD in the AA population.5,60
This higher factor contributes to the
‘epidemiological paradox’ observed in the AA population. When using the MDRD study equation, the prevalence of stage 3 CKD is significantly lower in the AA population than in Caucasians (4.8 % versus 9.2 %, data from the NHANES study),61
although AA patients are
This oddity remains when the CKD-EPI equation is used (even if the difference in prevalence is somewhat lower, i.e., 4.8 % versus 7.8 %),21
at higher risk of end-stage renal disease (ESRD) and the proportion of AA patients with ESRD is much higher than that of Caucasian patients with ESRD.21,59–61
suggesting that the impact of the CKD-EPI equation remains limited with regard to this specific uncertainty.5
From a strict epidemiological point of view, it seems clear that the new CKD-EPI equation overestimates to a lesser degree stage 3 CKD prevalence in the general population, compared with the MDRD study equation. However, this assertion does not imply that this new equation does not overestimate CKD prevalence at all. There are theoretical arguments to suggest that the CKD-EPI equation could slightly overestimate the ‘true’ prevalence of CKD.25
limited data have shown that the prevalence of the stage 3 CKD in a population could be still lower than observed with the CKD-EPI equation when another biomarker, such as cystatin C, is used.46
The limitations of the MDRD study equation to accurately estimate GFR led the authors of that equation to develop another, better performing equation, the CKD-EPI equation. The latter was published in 2009, only 10 years after the MDRD study equation. This new
1. Smith HW, The Kidney: Structure and Function in Health and Disease, New York: Oxford University Press Inc., 1951.
2. Stevens LA, Levey AS, Measured GFR as a confirmatory test for estimated GFR, J Am Soc Nephrol, 2009;20:2305–13.
3. 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.
4. Ceriotti F, Boyd JC, Klein G, et al., Reference intervals for serum creatinine concentrations: assessment of available data for global application, Clin Chem, 2008;54:559–66.
5. Delanaye P, Mariat C, Maillard N, et al., Are the creatinine- based equations accurate to estimate glomerular filtration rate in african american populations? Clin J Am Soc Nephrol, 2011;6:906–12.
6. Cockcroft DW, Gault MH, Prediction of creatinine clearance from serum creatinine, Nephron, 1976;16:31–41.
7. Levey AS, Bosch JP, Lewis JB, et al., 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.
8. Stevens LA, Coresh J, Feldman HI, et al., Evaluation of the modification of diet in renal disease study equation in a large diverse population, J Am Soc Nephrol, 2007;18:2749–57.
9. Froissart M, Rossert J, Jacquot C, et al., 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.
10. Poggio ED, Wang X, Greene T, et al., 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.
11. Lewis J, Agodoa L, Cheek D, et al., Comparison of cross- sectional renal function measurements in African Americans with hypertensive nephrosclerosis and of primary formulas to estimate glomerular filtration rate, Am J Kidney Dis, 2001;38:744–53.
12. Levey AS, de Jong PE, Coresh J, et al., The definition, classification, and prognosis of chronic kidney disease: a KDIGO Controversies Conference report, Kidney Int, 2011;80:17–28.
13. Delanaye P, Cohen EP, Formula-based estimates of the GFR: equations variable and uncertain, Nephron Clin Pract, 2008;110:c48–c53.
14. Rule AD, Larson TS, Bergstralh EJ, et al., Using serum
equation alleviates one important defect of the MDRD study equation, in that it corrects the systematic error in GFR above 60 ml/min/1.73 m2. This improvement has important implications, notably from an epidemiological point of view, but this does not mean that every source of error has been deleted with the new equation.
We consider the CKD-EPI equation as an evolution (at best), not a revolution. Its precision remains suboptimal, notably in higher GFR levels and, for example, it is still not recommended to use this method of estimating GFR to decide whether or not a subject is a good candidate for living kidney donation.2
Moreover, we must
admit that one important publication demonstrated a lower performance of the CKD-EPI equation (with a significant overestimation in CKD stages) in CKD patients.45
in CKD stages could be the ‘price to pay’ for a better performance in higher GFR levels.
It must also be underlined that the creatinine-based equations are estimations and that these estimations may be particularly flawed and a source of errors in very specific patients: hyperfiltrating diabetic patients;37,39,62
cirrhotic patients;63,64 elderly patients45,64 and anorexic patients.64
renal transplanted patients;42,43,45 In such patients, there is
no miracle solution, and using the CKD-EPI equation will not give accurate results (sometimes the estimation will be even worse than with the MDRD study equation). In these specific patient populations, the future probably belongs to other biomarkers, such as cystatin C.64
The CKD-EPI equation is probably one of the best estimators based on serum creatinine of GFR currently available and, as such, must be recommended to the general physicians. However, as nephrologists, we feel that it is most important to be fully aware its limitations. n
creatinine to estimate glomerular filtration rate: accuracy in good health and in chronic kidney disease, Ann Intern Med, 2004;141:929–37.
15. Rule AD, Bergstralh EJ, Slezak JM, et al., Glomerular filtration rate estimated by cystatin C among different clinical presentations, Kidney Int, 2006;69:399–405.
16. Botev R, Mallie JP, Wetzels JF, et al., The clinician and estimation of glomerular filtration rate by creatinine-based formulas: current limitations and quo vadis, Clin J Am Soc Nephrol, 2011;6:937–50.
17. Ibrahim H, Mondress M, Tello A, et al., An alternative formula to the Cockcroft-Gault and the modification of diet in renal diseases formulas in predicting GFR in individuals with type 1 diabetes, J Am Soc Nephrol, 2005;16:1051–60.
18. 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;6:1479–85.
19. 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;86:223–30.
20. Rule AD, Gussak HM, Pond GR, et al., Measured and estimated GFR in healthy potential kidney donors, Am J Kidney Dis, 2004;43:112–9.
21. Levey AS, Stevens LA, Schmid CH, et al., A new equation to estimate glomerular filtration rate, Ann Intern Med, 2009;150:604–12.
22. Stevens LA, Schmid CH, Zhang YL, et al., Development and validation of GFR-estimating equations using diabetes, transplant and weight, Nephrol Dial Transplant, 2010;25:449–57.
23. Stevens LA, Schmid CH, Greene T, et al., Comparative performance of the CKD Epidemiology Collaboration (CKD-EPI) and the Modification of Diet in Renal Disease (MDRD) study equations for estimating GFR levels above 60 mL/min/1.73 m2
, Am J Kidney Dis, 2010;56:486–95.
24. Stevens LA, Claybon MA, Schmid CH, et al., Evaluation of the Chronic Kidney Disease Epidemiology Collaboration equation for estimating the glomerular filtration rate in multiple ethnicities, Kidney Int, 2011;79:555–62.
25. Delanaye P, Cavalier E, Mariat C, et al., MDRD or CKD-EPI study equations for estimating prevalence of stage 3 CKD in epidemiological studies: which difference? Is this difference relevant? BMC Nephrol, 2010;11:8.
26. Delanaye P, Cavalier E, Mariat C, et al., Estimating glomerular filtration rate in Asian subjects: where do we stand?
Kidney Int, 2011;80:439–40.
27. Teo BW, Xu H, Wang D, et al., GFR estimating equations in a multiethnic Asian population, Am J Kidney Dis, 2011;58:56–63.
28. Cirillo M, Lombardi C, Luciano MG, et al., Estimation of GFR: a comparison of new and established equations, Am J Kidney Dis, 2010;56:802–4.
29. Soares AA, Eyff TF, Campani RB, et al., Performance of the CKD Epidemiology Collaboration (CKD-EPI) and the Modification of Diet in Renal Disease (MDRD) Study equations in healthy South Brazilians, Am J Kidney Dis, 2010;55:1162–3.
30. Orskov B, Borresen ML, Feldt-Rasmussen B, et al., Estimating glomerular filtration rate using the new CKD-EPI equation and other equations in patients with autosomal dominant polycystic kidney disease, Am J Nephrol, 2010;31:53–7.
31. Iliadis F, Didangelos T, Ntemka A, et al., Glomerular filtration rate estimation in patients with type 2 diabetes: creatinine- or cystatin C-based equations? Diabetologia, 2011;54:2987–94.
32. Lane BR, Demirjian S, Weight CJ, et al., Performance of the chronic kidney disease-epidemiology study equations for estimating glomerular filtration rate before and after nephrectomy, J Urol, 2010;183:896–901.
33. Eriksen BO, Mathisen UD, Melsom T, et al., Cystatin C is not a better estimator of GFR than plasma creatinine in the general population, Kidney Int, 2010;78:1305–11.
34. Nyman U, Grubb A, Sterner G, Björk J, The CKD-EPI and MDRD equations to estimate GFR. Validation in the Swedish Lund- Malmö Study cohort, Scand J Clin Lab Invest, 2011;71:129–38.
35. Michels WM, Grootendorst DC, Verduijn M, et al., Performance of the Cockcroft-Gault, MDRD, and new CKD-EPI formulas in relation to GFR, age, and body size, Clin J Am Soc Nephrol, 2010;5:1003–9.
36. 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;5:1960–8.
37. Nair S, Hardy KJ, Wilding JP, The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula performs worse than the Modification of Diet in Renal Disease (MDRD) equation in estimating glomerular filtration rate in Type 2 diabetic chronic kidney disease, Diabet Med, 2011;28:1279.
38. Rognant N, Lemoine S, Laville M, et al., Performance of the chronic kidney disease epidemiology collaboration equation to estimate glomerular filtration rate in diabetic patients,
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