Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

The applicability of eGFR equations to different populations

Nature Reviews Nephrology volume 9, pages 513–522 (2013)Cite this article

Subjects

Abstract

The Cockcroft–Gault equation for estimating glomerular filtration rate has been learnt by every generation of medical students over the decades. Since the publication of the Modification of Diet in Renal Disease (MDRD) study equation in 1999, however, the supremacy of the Cockcroft–Gault equation has been relentlessly disputed. More recently, the Chronic Kidney Disease Epidemiology (CKD-EPI) consortium has proposed a group of novel equations for estimating glomerular filtration rate (GFR). The MDRD and CKD-EPI equations were developed following a rigorous process, are expressed in a way in which they can be used with standardized biomarkers of GFR (serum creatinine and/or serum cystatin C) and have been evaluated in different populations of patients. Today, the MDRD Study equation and the CKD-EPI equation based on serum creatinine level have supplanted the Cockcroft–Gault equation. In many regards, these equations are superior to the Cockcroft–Gault equation and are now specifically recommended by international guidelines. With their generalized use, however, it has become apparent that those equations are not infallible and that they fail to provide an accurate estimate of GFR in certain situations frequently encountered in clinical practice. After describing the processes that led to the development of the new GFR-estimating equations, this Review discusses the clinical situations in which the applicability of these equations is questioned.

Key Points

  • Even when developed through rigorous process, creatinine-based equations for estimating glomerular filtration rate (GFR) are not systematically applicable to every clinical situation

  • Examples of clinical situations in which the applicability of the commonly used equations has been questioned include particular ethnic groups, kidney transplant recipients and the elderly

  • The applicability of the MDRD study equation and of the CKD-EPI equation has been more extensively documented than the applicability of the Cockcroft–Gault equation

  • Although cystatin C—as a GFR marker—outperforms creatinine in many situations where equations are usually less accurate, cystatin-C-based GFR estimating equations have not yet been definitively validated

  • The use of reference methods of GFR measurement in situations in which equations are known to be suboptimal should be considered

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

References

  1. Inker, L. A. et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N. Engl. J. Med. 367, 20–29 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Levey, A. S. et al. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann. Intern. Med. 145, 247–254 (2006).

    Article  CAS  PubMed  Google Scholar 

  3. Levey, A. S. et al. A new equation to estimate glomerular filtration rate. Ann. Intern. Med. 150, 604–612 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Stevens, L. A. et al. Evaluation of the modification of diet in renal disease study equation in a large diverse population. J. Am. Soc. Nephrol. 18, 2749–2757 (2007).

    Article  PubMed  Google Scholar 

  5. Botev, R., Mallie, J. P., Wetzels, J. F., Couchoud, C. & Schuck, O. The clinician and estimation of glomerular filtration rate by creatinine-based formulas: current limitations and quo vadis. Clin. J. Am. Soc. Nephrol. 6, 937–950 (2011).

    Article  PubMed  Google Scholar 

  6. Delanaye, P. & Cohen, E. P. Formula-based estimates of the GFR: equations variable and uncertain. Nephron Clin. Pract. 110, c48–c53 (2008).

    Article  CAS  PubMed  Google Scholar 

  7. Delanaye, P., Pottel, H. & Botev, R. Should we abandon the use of the MDRD equation in favour of the CKD-EPI equation? Nephrol. Dial. Transplant. 28, 1396–1403 (2013).

    Article  PubMed  Google Scholar 

  8. Stevens, L. A. 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. 56, 486–495 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Stevens, L. A. et al. Development and validation of GFR-estimating equations using diabetes, transplant and weight. Nephrol. Dial. Transplant. 25, 449–457 (2010).

    Article  PubMed  Google Scholar 

  10. Stevens, L. A. et al. Evaluation of the Chronic Kidney Disease Epidemiology Collaboration equation for estimating the glomerular filtration rate in multiple ethnicities. Kidney Int. 79, 555–562 (2011).

    Article  PubMed  Google Scholar 

  11. Iliadis, F. et al. Glomerular filtration rate estimation in patients with type 2 diabetes: creatinine- or cystatin C-based equations? Diabetologia 54, 2987–2994 (2011).

    Article  CAS  PubMed  Google Scholar 

  12. Murata, K. et al. Relative performance of the MDRD and CKD-EPI equations for estimating glomerular filtration rate among patients with varied clinical presentations. Clin. J. Am. Soc. Nephrol. 6, 1963–1972 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Nyman, U., Grubb, A., Sterner, G. & Bjork, J. The CKD-EPI and MDRD equations to estimate GFR. Validation in the Swedish Lund-Malmo Study cohort. Scand. J. Clin. Lab. Invest. 71, 129–138 (2011).

    Article  PubMed  Google Scholar 

  14. Schaeffner, E. S. et al. Two novel equations to estimate kidney function in persons aged 70 years or older. Ann. Intern. Med. 157, 471–481 (2012).

    Article  PubMed  Google Scholar 

  15. Rehberg, P. B. Studies on kidney function: the rate of filtration and reabsorption in the human kidney. Biochem. J. 20, 447–460 (1926).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Shannon, J. A. The renal excretion of creatinine in man. J. Clin. Invest. 14, 403–410 (1935).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Miller, B. F. & Winkler, A. W. The renal excretion of endogenous creatinine in man. Comparison with exogenous creatinine and inulin. J. Clin. Invest. 17, 31–40 (1938).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Shemesh, O., Golbetz, H., Kriss, J. P. & Myers, B. D. Limitations of creatinine as a filtration marker in glomerulopathic patients. Kidney Int. 28, 830–838 (1985).

    Article  CAS  PubMed  Google Scholar 

  19. Perrone, R. D., Madias, N. E. & Levey, A. S. Serum creatinine as an index of renal function: new insights into old concepts. Clin. Chem. 38, 1933–1953 (1992).

    CAS  PubMed  Google Scholar 

  20. Heymsfield, S. B., Arteaga, C., McManus, C., Smith, J. & Moffitt, S. Measurement of muscle mass in humans: validity of the 24-hour urinary creatinine method. Am. J. Clin. Nutr. 37, 478–494 (1983).

    Article  CAS  PubMed  Google Scholar 

  21. Preiss, D. J., Godber, I. M., Lamb, E. J., Dalton, R. N. & Gunn, I. R. The influence of a cooked-meat meal on estimated glomerular filtration rate. Ann. Clin. Biochem. 44, 35–42 (2007).

    Article  CAS  PubMed  Google Scholar 

  22. Mayersohn, M., Conrad, K. A. & Achari, R. The influence of a cooked meat meal on creatinine plasma concentration and creatinine clearance. Br. J. Clin. Pharmacol. 15, 227–230 (1983).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Cobbaert, C. M., Baadenhuijsen, H. & Weykamp, C. W. Prime time for enzymatic creatinine methods in pediatrics. Clin. Chem. 55, 549–558 (2009).

    Article  CAS  PubMed  Google Scholar 

  24. Panteghini, M. Enzymatic assays for creatinine: time for action. Scand. J. Clin. Lab. Invest. Suppl. 241, 84–88 (2008).

    Article  CAS  PubMed  Google Scholar 

  25. Myers, G. L. et al. Recommendations for improving serum creatinine measurement: a report from the laboratory working group of the national kidney disease education program. Clin. Chem. 52, 5–18 (2006).

    Article  CAS  PubMed  Google Scholar 

  26. Pieroni, L. et al. A multicentric evaluation of IDMS-traceable creatinine enzymatic assays. Clin. Chim. Acta 412, 2070–2075 (2011).

    Article  CAS  PubMed  Google Scholar 

  27. Lewis, J. 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. 38, 744–753 (2001).

    Article  CAS  PubMed  Google Scholar 

  28. Levey, A. S. 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. 130, 461–470 (1999).

    Article  CAS  PubMed  Google Scholar 

  29. Effersoe, P. Relationship between endogenous 24-hour creatinine clearance and serum creatinine concentration in patients with chronic renal disease. Acta Med. Scand. 156, 429–434 (1957).

    Article  CAS  PubMed  Google Scholar 

  30. Cockcroft, D. W. & Gault, M. H. Prediction of creatinine clearance from serum creatinine. Nephron 16, 31–41 (1976).

    Article  CAS  PubMed  Google Scholar 

  31. Delanaye, P. & Krzesinski, J. M. Indexing of renal function parameters by body surface area: intelligence or folly? Nephron Clin. Pract. 119, c289–c292 (2011).

    Article  PubMed  Google Scholar 

  32. Millar, J. A. The Cockroft and Gault formula for estimation of creatinine clearance: a friendly deconstruction. N. Z. Med. J. 125, 119–122 (2012).

    PubMed  Google Scholar 

  33. Eriksen, B. O. et al. GFR normalized to total body water allows comparisons across genders and body sizes. J. Am. Soc. Nephrol. 22, 1517–1525 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Stevens, L. A. et al. Comparison of drug dosing recommendations based on measured GFR and kidney function estimating equations. Am. J. Kidney Dis. 54, 33–42 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Park, E. J. et al. A systematic comparison of Cockcroft-Gault and modification of diet in renal disease equations for classification of kidney dysfunction and dosage adjustment. Ann. Pharmacother. 46, 1174–1187 (2012).

    Article  PubMed  Google Scholar 

  36. Nyman, H. A. et al. Comparative evaluation of the Cockcroft-Gault Equation and the Modification of Diet in Renal Disease (MDRD) study equation for drug dosing: an opinion of the Nephrology Practice and Research Network of the American College of Clinical Pharmacy. Pharmacotherapy 31, 1130–1144 (2011).

    Article  PubMed  Google Scholar 

  37. Levey, A. S. et al. Expressing the modification of diet in renal disease study equation for estimating glomerular filtration rate with standardized serum creatinine values. Clin. Chem. 53, 766–772 (2007).

    Article  CAS  PubMed  Google Scholar 

  38. 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. 16, 763–773 (2005).

    Article  PubMed  Google Scholar 

  39. Ibrahim, H. 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. 16, 1051–1060 (2005).

    Article  PubMed  Google Scholar 

  40. Poggio, E. D., Wang, X., Greene, T., Van Lente, F. & Hall, P. M. 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. 16, 459–466 (2005).

    Article  PubMed  Google Scholar 

  41. Rigalleau, V. et al. Estimation of glomerular filtration rate in diabetic subjects: Cockcroft formula or modification of Diet in Renal Disease study equation? Diabetes Care 28, 838–843 (2005).

    Article  PubMed  Google Scholar 

  42. Flamant, M. et al. GFR estimation using the Cockcroft-Gault, MDRD Study, and CKD-EPI equations in the elderly. Am. J. Kidney Dis. 60, 847–849 (2012).

    Article  PubMed  Google Scholar 

  43. Hallan, S., Asberg, A., Lindberg, M. & Johnsen, H. Validation of the Modification of Diet in Renal Disease formula for estimating GFR with special emphasis on calibration of the serum creatinine assay. Am. J. Kidney Dis. 44, 84–93 (2004).

    Article  PubMed  Google Scholar 

  44. Macisaac, R. J. et al. Estimating glomerular filtration rate in diabetes: a comparison of cystatin-C- and creatinine-based methods. Diabetologia 49, 1686–1689 (2006).

    Article  CAS  PubMed  Google Scholar 

  45. Ibrahim, H. N., Rogers, T., Tello, A. & Matas, A. The performance of three serum creatinine-based formulas in estimating GFR in former kidney donors. Am. J. Transplant. 6, 1479–1485 (2006).

    Article  CAS  PubMed  Google Scholar 

  46. Rule, A. D. et al. Using serum creatinine to estimate glomerular filtration rate: accuracy in good health and in chronic kidney disease. Ann. Intern. Med. 141, 929–937 (2004).

    Article  CAS  PubMed  Google Scholar 

  47. Rule, A. D., Bergstralh, E. J., Slezak, J. M., Bergert, J. & Larson, T. S. Glomerular filtration rate estimated by cystatin C among different clinical presentations. Kidney Int. 69, 399–405 (2006).

    Article  CAS  PubMed  Google Scholar 

  48. Murthy, K., Stevens, L. A., Stark, P. C. & Levey, A. S. Variation in the serum creatinine assay calibration: a practical application to glomerular filtration rate estimation. Kidney Int. 68, 1884–1887 (2005).

    Article  CAS  PubMed  Google Scholar 

  49. Schwartz, G. J. et al. Improved equations estimating GFR in children with chronic kidney disease using an immunonephelometric determination of cystatin C. Kidney Int. 82, 445–453 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Delanaye, P., Cavalier, E., Mariat, C., Maillard, N. & Krzesinski, J. M. MDRD or CKD-EPI study equations for estimating prevalence of stage 3 CKD in epidemiological studies: which difference? Is this difference relevant? BMC Nephrol. 11, 8 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Matsushita, K., Selvin, E., Bash, L. D., Astor, B. C. & Coresh, J. Risk implications of the new CKD Epidemiology Collaboration (CKD-EPI) equation compared with the MDRD Study equation for estimated GFR: the Atherosclerosis Risk in Communities (ARIC) Study. Am. J. Kidney Dis. 55, 648–659 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  52. White, S. L., Polkinghorne, K. R., Atkins, R. C. & Chadban, S. J. Comparison of the prevalence and mortality risk of CKD in Australia using the CKD Epidemiology Collaboration (CKD-EPI) and Modification of Diet in Renal Disease (MDRD) Study GFR estimating equations: the AusDiab (Australian Diabetes, Obesity and Lifestyle) Study. Am. J. Kidney Dis. 55, 660–670 (2010).

    Article  PubMed  Google Scholar 

  53. Bjork, J., Jones, I., Nyman, U. & Sjostrom, P. Validation of the Lund-Malmo, Chronic Kidney Disease Epidemiology (CKD-EPI) and Modification of Diet in Renal Disease (MDRD) equations to estimate glomerular filtration rate in a large Swedish clinical population. Scand. J. Urol. Nephrol. 46, 212–222 (2012).

    Article  CAS  PubMed  Google Scholar 

  54. Buron, F. et al. Estimating glomerular filtration rate in kidney transplant recipients: performance over time of four creatinine-based formulas. Transplantation 92, 1005–1011 (2011).

    CAS  PubMed  Google Scholar 

  55. Nyman, U. et al. Standardization of p-creatinine assays and use of lean body mass allow improved prediction of calculated glomerular filtration rate in adults: a new equation. Scand. J. Clin. Lab. Invest. 66, 451–468 (2006).

    Article  CAS  PubMed  Google Scholar 

  56. Cirillo, M. et al. Estimation of GFR: a comparison of new and established equations. Am. J. Kidney Dis. 56, 802–804 (2010).

    Article  PubMed  Google Scholar 

  57. Eriksen, B. O. et al. Cystatin C is not a better estimator of GFR than plasma creatinine in the general population. Kidney Int. 78, 1305–1311 (2010).

    Article  CAS  PubMed  Google Scholar 

  58. Redal-Baigorri, B., Stokholm, K. H., Rasmussen, K. & Jeppesen, N. Estimation of kidney function in cancer patients. Dan. Med. Bull. 58, A4236 (2011).

    PubMed  Google Scholar 

  59. Butler, E. A. & Flynn, F. V. The occurrence of post-gamma protein in urine: a new protein abnormality. J. Clin Pathol. 14, 172–178 (1961).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Clausen, J. Proteins in normal cerebrospinal fluid not found in serum. Proc. Soc. Exp. Biol. Med. 107, 170–172 (1961).

    Article  CAS  PubMed  Google Scholar 

  61. Abrahamson, M. Human cysteine proteinase inhibitors. Isolation, physiological importance, inhibitory mechanism, gene structure and relation to hereditary cerebral hemorrhage. Scand. J. Clin. Lab. Invest. Suppl. 191, 21–31 (1988).

    CAS  PubMed  Google Scholar 

  62. Abrahamson, M. et al. Structure and expression of the human cystatin C gene. Biochem. J. 268, 287–294 (1990).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Jacobsson, B., Lignelid, H. & Bergerheim, U. S. Transthyretin and cystatin C are catabolized in proximal tubular epithelial cells and the proteins are not useful as markers for renal cell carcinomas. Histopathology 26, 559–564 (1995).

    Article  CAS  PubMed  Google Scholar 

  64. Tenstad, O., Roald, A. B., Grubb, A. & Aukland, K. Renal handling of radiolabelled human cystatin C in the rat. Scand. J. Clin. Lab. Invest. 56, 409–414 (1996).

    Article  CAS  PubMed  Google Scholar 

  65. Grubb, A., Simonsen, O., Sturfelt, G., Truedsson, L. & Thysell, H. Serum concentration of cystatin C, factor D and beta 2-microglobulin as a measure of glomerular filtration rate. Acta Med. Scand. 218, 499–503 (1985).

    Article  CAS  PubMed  Google Scholar 

  66. Vinge, E., Lindergard, B., Nilsson-Ehle, P. & Grubb, A. Relationships among serum cystatin C, serum creatinine, lean tissue mass and glomerular filtration rate in healthy adults. Scand. J. Clin. Lab. Invest. 59, 587–592 (1999).

    Article  CAS  PubMed  Google Scholar 

  67. Macdonald, J. et al. GFR estimation using cystatin C is not independent of body composition. Am. J. Kidney Dis. 48, 712–719 (2006).

    Article  CAS  PubMed  Google Scholar 

  68. Knight, E. L. et al. Factors influencing serum cystatin C levels other than renal function and the impact on renal function measurement. Kidney Int. 65, 1416–1421 (2004).

    Article  CAS  PubMed  Google Scholar 

  69. Stevens, L. A. et al. Factors other than glomerular filtration rate affect serum cystatin C levels. Kidney Int. 75, 652–660 (2009).

    Article  CAS  PubMed  Google Scholar 

  70. Galteau, M. M., Guyon, M., Gueguen, R. & Siest, G. Determination of serum cystatin C: biological variation and reference values. Clin. Chem. Lab. Med. 39, 850–857 (2001).

    Article  CAS  PubMed  Google Scholar 

  71. Fricker, M., Wiesli, P., Brandle, M., Schwegler, B. & Schmid, C. Impact of thyroid dysfunction on serum cystatin C. Kidney Int. 63, 1944–1947 (2003).

    Article  CAS  PubMed  Google Scholar 

  72. Gagneux-Brunon, A., Mariat, C. & Delanaye, P. Cystatin C in HIV-infected patients: promising but not yet ready for prime time. Nephrol. Dial. Transplant. 27, 1305–1313 (2012).

    Article  CAS  PubMed  Google Scholar 

  73. Naour, N. et al. Potential contribution of adipose tissue to elevated serum cystatin C in human obesity. Obesity (Silver Spring) 17, 2121–2126 (2009).

    Article  CAS  Google Scholar 

  74. Risch, L. & Huber, A. R. Glucocorticoids and increased serum cystatin C concentrations. Clin. Chim. Acta 320, 133–134 (2002).

    Article  CAS  PubMed  Google Scholar 

  75. Segarra, A. et al. Assessing glomerular filtration rate in hospitalized patients: a comparison between CKD-EPI and four cystatin C-based equations. Clin. J. Am. Soc. Nephrol. 6, 2411–2420 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Seronie-Vivien, S. et al. Cystatin C: current position and future prospects. Clin. Chem. Lab. Med. 46, 1664–1686 (2008).

    CAS  PubMed  Google Scholar 

  77. Delanaye, P. et al. Analytical study of three cystatin C assays and their impact on cystatin C-based GFR-prediction equations. Clin. Chim. Acta 398, 118–124 (2008).

    Article  CAS  PubMed  Google Scholar 

  78. White, C. A. et al. The impact of interlaboratory differences in cystatin C assay measurement on glomerular filtration rate estimation. Clin. J. Am. Soc. Nephrol. 6, 2150–2156 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Voskoboev, N. V., Larson, T. S., Rule, A. D. & Lieske, J. C. Importance of cystatin C assay standardization. Clin. Chem. 57, 1209–1211 (2011).

    Article  CAS  PubMed  Google Scholar 

  80. Larsson, A., Hansson, L. O., Flodin, M., Katz, R. & Shlipak, M. G. Calibration of the Siemens cystatin C immunoassay has changed over time. Clin. Chem. 57, 777–778 (2011).

    Article  CAS  PubMed  Google Scholar 

  81. Grubb, A. et al. First certified reference material for cystatin C in human serum ERM-DA471/IFCC. Clin Chem. Lab. Med. 48, 1619–1621 (2010).

    Article  CAS  PubMed  Google Scholar 

  82. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. Suppl. 3, 1–150 (2013).

  83. Coresh, J. et al. Creatinine clearance as a measure of GFR in screenees for the African-American Study of Kidney Disease and Hypertension pilot study. Am. J. Kidney Dis. 32, 32–42 (1998).

    Article  CAS  PubMed  Google Scholar 

  84. Goldwasser, P., Aboul-Magd, A. & Maru, M. Race and creatinine excretion in chronic renal insufficiency. Am. J. Kidney Dis. 30, 16–22 (1997).

    Article  CAS  PubMed  Google Scholar 

  85. Delanaye, P., Mariat, C., Maillard, N., Krzesinski, J. M. & Cavalier, E. Are the creatinine-based equations accurate to estimate glomerular filtration rate in African American populations? Clin. J. Am. Soc. Nephrol. 6, 906–912 (2011).

    Article  PubMed  Google Scholar 

  86. Peralta, C. A. et al. Race differences in prevalence of chronic kidney disease among young adults using creatinine-based glomerular filtration rate-estimating equations. Nephrol. Dial. Transplant. 25, 3934–3939 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  87. Rule, A. D. et al. Comparison of methods for determining renal function decline in early autosomal dominant polycystic kidney disease: the consortium of radiologic imaging studies of polycystic kidney disease cohort. J. Am. Soc. Nephrol. 17, 854–862 (2006).

    Article  CAS  PubMed  Google Scholar 

  88. van Deventer, H. E., Paiker, J. E., Katz, I. J. & George, J. A. A comparison of cystatin C- and creatinine-based prediction equations for the estimation of glomerular filtration rate in black South Africans. Nephrol. Dial. Transplant. 26, 1553–1558 (2011).

    Article  CAS  PubMed  Google Scholar 

  89. Maple-Brown, L. J. et al. Accurate assessment of kidney function in indigenous Australians: the estimated GFR study. Am. J. Kidney Dis. 60, 680–682 (2012).

    Article  PubMed  Google Scholar 

  90. Delanaye, P., Cavalier, E., Mariat, C., Krzesinski, J. M. & Rule, A. D. Estimating glomerular filtration rate in Asian subjects: where do we stand? Kidney Int. 80, 439–440 (2011).

    Article  PubMed  Google Scholar 

  91. Ma, Y. C. et al. Modified glomerular filtration rate estimating equation for Chinese patients with chronic kidney disease. J. Am. Soc. Nephrol. 17, 2937–2944 (2006).

    Article  PubMed  Google Scholar 

  92. Horio, M., Imai, E., Yasuda, Y., Watanabe, T. & Matsuo, S. Modification of the CKD epidemiology collaboration (CKD-EPI) equation for Japanese: accuracy and use for population estimates. Am. J. Kidney Dis. 56, 32–38 (2010).

    Article  PubMed  Google Scholar 

  93. Dai, S. S. et al. Evaluation of GFR measurement method as an explanation for differences among GFR estimation equations. Am. J. Kidney Dis. 58, 496–498 (2011).

    Article  PubMed  Google Scholar 

  94. Teo, B. W. et al. GFR estimating equations in a multiethnic Asian population. Am. J. Kidney Dis. 58, 56–63 (2011).

    Article  PubMed  Google Scholar 

  95. Bargnoux, A. S. et al. Accuracy of GFR predictive equations in renal transplantation: validation of a new turbidimetric cystatin C assay on Architect c8000®. Clin. Biochem. 45, 151–153 (2012).

    Article  CAS  PubMed  Google Scholar 

  96. Kukla, A. et al. GFR-estimating models in kidney transplant recipients on a steroid-free regimen. Nephrol. Dial. Transplant. 25, 1653–1661 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  97. Masson, I. et al. MDRD versus CKD-EPI equation to estimate glomerular filtration rate in kidney transplant recipients. Transplantation 95, 1211–1217 (2013).

    Article  PubMed  Google Scholar 

  98. Poge, U., Gerhardt, T., Stoffel-Wagner, B., Sauerbruch, T. & Woitas, R. P. Validation of the CKD-EPI formula in patients after renal transplantation. Nephrol. Dial. Transplant. 26, 4104–4108 (2011).

    Article  PubMed  Google Scholar 

  99. White, C. A., Akbari, A., Doucette, S., Fergusson, D. & Knoll, G. A. Estimating glomerular filtration rate in kidney transplantation: is the new chronic kidney disease epidemiology collaboration equation any better? Clin. Chem. 56, 474–477 (2010).

    Article  CAS  PubMed  Google Scholar 

  100. Harman, G. et al. Accuracy of cystatin C-based estimates of glomerular filtration rate in kidney transplant recipients: a systematic review. Nephrol. Dial. Transplant. 28, 741–757 (2013).

    Article  CAS  PubMed  Google Scholar 

  101. Masson, I. et al. GFR estimation using standardized cystatin C in kidney transplant recipients. Am. J. Kidney Dis. 61, 279–284 (2013).

    Article  CAS  PubMed  Google Scholar 

  102. Rigalleau, V. et al. Cockcroft-Gault formula is biased by body weight in diabetic patients with renal impairment. Metabolism 55, 108–112 (2006).

    Article  CAS  PubMed  Google Scholar 

  103. Nair, S., Hardy, K. J. & Wilding, J. P. 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. 28, 1279 (2011).

    Article  CAS  PubMed  Google Scholar 

  104. Rognant, N., Lemoine, S., Laville, M., Hadj-Aissa, A. & Dubourg, L. Performance of the chronic kidney disease epidemiology collaboration equation to estimate glomerular filtration rate in diabetic patients. Diabetes Care 34, 1320–1322 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  105. Silveiro, S. P. et al. Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation pronouncedly underestimates glomerular filtration rate in type 2 diabetes. Diabetes Care 34, 2353–2355 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Gaspari, F. et al. The GFR and GFR decline cannot be accurately estimated in type 2 diabetics. Kidney Int. 84, 164–173 (2013).

    Article  CAS  PubMed  Google Scholar 

  107. Oddoze, C., Morange, S., Portugal, H., Berland, Y. & Dussol, B. Cystatin C is not more sensitive than creatinine for detecting early renal impairment in patients with diabetes. Am. J. Kidney Dis. 38, 310–316 (2001).

    Article  CAS  PubMed  Google Scholar 

  108. Perkins, B. A. et al. Detection of renal function decline in patients with diabetes and normal or elevated GFR by serial measurements of serum cystatin C concentration: results of a 4-year follow-up study. J. Am. Soc. Nephrol. 16, 1404–1412 (2005).

    Article  PubMed  Google Scholar 

  109. Rigalleau, V. et al. The combination of cystatin C and serum creatinine improves the monitoring of kidney function in patients with diabetes and chronic kidney disease. Clin. Chem. 53, 1988–1989 (2007).

    Article  CAS  PubMed  Google Scholar 

  110. Macisaac, R. J. et al. The accuracy of cystatin C and commonly used creatinine-based methods for detecting moderate and mild chronic kidney disease in diabetes. Diabet. Med. 24, 443–448 (2007).

    Article  CAS  PubMed  Google Scholar 

  111. Tan, G. D. et al. Clinical usefulness of cystatin C for the estimation of glomerular filtration rate in type 1 diabetes: reproducibility and accuracy compared with standard measures and iohexol clearance. Diabetes Care 25, 2004–2009 (2002).

    Article  CAS  PubMed  Google Scholar 

  112. Pucci, L. et al. Cystatin C and estimates of renal function: searching for a better measure of kidney function in diabetic patients. Clin. Chem. 53, 480–488 (2007).

    Article  CAS  PubMed  Google Scholar 

  113. Delanaye, P. et al. Normal reference values for glomerular filtration rate: what do we really know? Nephrol. Dial. Transplant. 27, 2664–2672 (2012).

    Article  PubMed  Google Scholar 

  114. Van Den Noortgate, N. J., Janssens, W. H., Delanghe, J. R., Afschrift, M. B. & Lameire, N. H. Serum cystatin C concentration compared with other markers of glomerular filtration rate in the old old. J. Am. Geriatr. Soc. 50, 1278–1282 (2002).

    Article  PubMed  Google Scholar 

  115. Kilbride, H. S. et al. Accuracy of the MDRD (Modification of Diet in Renal Disease) Study and CKD-EPI (CKD Epidemiology Collaboration) equations for estimation of GFR in the elderly. Am. J. Kidney Dis. 61, 57–66 (2013).

    Article  PubMed  Google Scholar 

  116. Delanghe, J. R. How to estimate GFR in children. Nephrol. Dial. Transplant. 24, 714–716 (2009).

    Article  PubMed  Google Scholar 

  117. Schwartz, G. J. & Work, D. F. Measurement and estimation of GFR in children and adolescents. Clin. J. Am. Soc. Nephrol. 4, 1832–1843 (2009).

    Article  PubMed  Google Scholar 

  118. Selistre, L. et al. GFR estimation in adolescents and young adults. J. Am. Soc. Nephrol. 23, 989–996 (2012).

    Article  CAS  PubMed  Google Scholar 

  119. Finney, H., Newman, D. J., Thakkar, H., Fell, J. M. & Price, C. P. Reference ranges for plasma cystatin C and creatinine measurements in premature infants, neonates, and older children. Arch. Dis. Child. 82, 71–75 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Filler, G. et al. Beta-trace protein, cystatin C, beta(2)-microglobulin, and creatinine compared for detecting impaired glomerular filtration rates in children. Clin. Chem. 48, 729–736 (2002).

    CAS  PubMed  Google Scholar 

  121. Grubb, A. et al. Simple cystatin C-based prediction equations for glomerular filtration rate compared with the modification of diet in renal disease prediction equation for adults and the Schwartz and the Counahan-Barratt prediction equations for children. Clin. Chem. 51, 1420–1431 (2005).

    Article  CAS  PubMed  Google Scholar 

  122. Zappitelli, M. et al. Derivation and validation of cystatin C-based prediction equations for GFR in children. Am. J. Kidney Dis. 48, 221–230 (2006).

    Article  CAS  PubMed  Google Scholar 

  123. Schwartz, G. J. et al. New equations to estimate GFR in children with CKD. J. Am. Soc. Nephrol. 20, 629–637 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  124. Gao, A. et al. Comparison of the glomerular filtration rate in children by the new revised Schwartz formula and a new generalized formula. Kidney Int. 83, 524–530 (2013).

    Article  CAS  PubMed  Google Scholar 

  125. Delanaye, P. & Ebert, N. Assessment of kidney function: estimating GFR in children. Nat. Rev. Nephrol. 8, 503–504 (2012).

    Article  CAS  PubMed  Google Scholar 

  126. Poggio, E. D. et al. Performance of the Cockcroft-Gault and modification of diet in renal disease equations in estimating GFR in ill hospitalized patients. Am. J. Kidney Dis. 46, 242–252 (2005).

    Article  PubMed  Google Scholar 

  127. Delanaye, P. et al. Cystatin C or creatinine for detection of stage 3 chronic kidney disease in anorexia nervosa. Nephron Clin. Pract. 110, c158–c163 (2008).

    Article  CAS  PubMed  Google Scholar 

  128. Xirouchakis, E. et al. Comparison of cystatin C and creatinine-based glomerular filtration rate formulas with 51Cr-EDTA clearance in patients with cirrhosis. Clin. J. Am. Soc. Nephrol. 6, 84–92 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Grootendorst, D. C. et al. The MDRD formula does not reflect GFR in ESRD patients. Nephrol. Dial. Transplant. 26, 1932–1937 (2011).

    Article  PubMed  Google Scholar 

  130. Carrie, B. J., Golbetz, H. V., Michaels, A. S. & Myers, B. D. Creatinine: an inadequate filtration marker in glomerular diseases. Am. J. Med. 69, 177–182 (1980).

    Article  CAS  PubMed  Google Scholar 

  131. Branten, A. J., Vervoort, G. & Wetzels, J. F. Serum creatinine is a poor marker of GFR in nephrotic syndrome. Nephrol. Dial. Transplant. 20, 707–711 (2005).

    Article  CAS  PubMed  Google Scholar 

  132. Kwong, Y. T. et al. Imprecision of urinary iothalamate clearance as a gold-standard measure of GFR decreases the diagnostic accuracy of kidney function estimating equations. Am. J. Kidney Dis. 56, 39–49 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  133. Hsu, C. Y. et al. Measured GFR does not outperform estimated GFR in predicting CKD-related complications. J. Am. Soc. Nephrol. 22, 1931–1937 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Nephrology, Dialysis and Transplantation, University of Liège, CHU Sart Tilman, Liège, 4000, Belgium

    Pierre Delanaye

  2. Department of Nephrology, Dialysis, Transplantation and Hypertension, CHU Hôpital Nord, Université Jean Monnet, PRES Université de Lyon, Saint-Etienne, 42055, France

    Christophe Mariat

Authors
  1. Pierre Delanaye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christophe Mariat
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

P. Delanaye researched data for the article. Both authors discussed the content, wrote the article and reviewed/edited it before submission.

Corresponding author

Correspondence to Pierre Delanaye.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Delanaye, P., Mariat, C. The applicability of eGFR equations to different populations. Nat Rev Nephrol 9, 513–522 (2013). https://doi.org/10.1038/nrneph.2013.143

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrneph.2013.143

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing