Journal of Nephrology

, Volume 28, Issue 1, pp 59–66 | Cite as

Is there an age cutoff to apply adult formulas for GFR estimation in children?

  • Antonio Azzi
  • Francois Cachat
  • Mohamed Faouzi
  • Dolores Mosig
  • Pascal Ramseyer
  • Eric Girardin
  • Hassib ChehadeEmail author
Original Article



Estimation of glomerular filtration rate (eGFR) using a common formula for both adult and pediatric populations is challenging. Using inulin clearances (iGFRs), this study aims to investigate the existence of a precise age cutoff beyond which the Modification of Diet in Renal Disease (MDRD), the Chronic Kidney Disease Epidemiology Collaboration (CKD–EPI), or the Cockroft–Gault (CG) formulas, can be applied with acceptable precision. Performance of the new Schwartz formula according to age is also evaluated.


We compared 503 iGFRs for 503 children aged between 33 months and 18 years to eGFRs. To define the most precise age cutoff value for each formula, a circular binary segmentation method analyzing the formulas’ bias values according to the children’s ages was performed. Bias was defined by the difference between iGFRs and eGFRs. To validate the identified cutoff, 30 % accuracy was calculated.


For MDRD, CKD–EPI and CG, the best age cutoff was ≥14.3, ≥14.2 and ≤10.8 years, respectively. The lowest mean bias and highest accuracy were −17.11 and 64.7 % for MDRD, 27.4 and 51 % for CKD–EPI, and 8.31 and 77.2 % for CG. The Schwartz formula showed the best performance below the age of 10.9 years.


For the MDRD and CKD–EPI formulas, the mean bias values decreased with increasing child age and these formulas were more accurate beyond an age cutoff of 14.3 and 14.2 years, respectively. For the CG and Schwartz formulas, the lowest mean bias values and the best accuracies were below an age cutoff of 10.8 and 10.9 years, respectively. Nevertheless, the accuracies of the formulas were still below the National Kidney Foundation Kidney Disease Outcomes Quality Initiative target to be validated in these age groups and, therefore, none of these formulas can be used to estimate GFR in children and adolescent populations.


Child Chronic kidney disease CKD–EPI formula Cockroft–Gault formula Glomerular filtration rate MDRD formula Schwartz formula 


Conflict of interest

All the authors declare no competing interests.


  1. 1.
    Schwartz GJ, Haycock GB, Edelmann CM et al (1976) A simple estimate of glomerular filtration rate in children derived from body length and plasma creatinine. Pediatrics 58:259–263PubMedGoogle Scholar
  2. 2.
    Schwartz GJ, Muñoz A, Schneider MF et al (2009) New equations to estimate GFR in children with CKD. J Am Soc Nephrol 20(3):629–637CrossRefPubMedCentralPubMedGoogle Scholar
  3. 3.
    Levey AS, Coresh J, Greene T et al (2006) Chronic Kidney Disease Epidemiology Collaboration: using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med 145(4):247–254CrossRefPubMedGoogle Scholar
  4. 4.
    Levey AS, Stevens LA, Schmid CH et al (2009) CKD–EPI (Chronic Kidney Disease Epidemiology Collaboration) A new equation to estimate glomerular filtration rate. Ann Intern Med 150:604–612CrossRefPubMedCentralPubMedGoogle Scholar
  5. 5.
    Cockroft DW, Gault MH (1976) Prediction of creatinine clearance from serum creatinine. Nephron 16:31–41CrossRefGoogle Scholar
  6. 6.
    Chehade H, Girardin E, Iglesias K et al (2013) Assessment of adult formulas for glomerular filtration rate estimation in children. Pediatr Nephrol 28:105–114CrossRefPubMedGoogle Scholar
  7. 7.
    Pierrat A, Gravier E, Saunders C et al (2003) Predicting GFR in children and adults: a comparison between Cockroft–Gault, Schwartz, and MDRD formulas. Kidney Int 64:1425–1436CrossRefPubMedGoogle Scholar
  8. 8.
    Filler G, Foster J, Acker A (2005) The Cockroft–Gault formula should not be used in children. Kidney Int 67:2321–2324CrossRefPubMedGoogle Scholar
  9. 9.
    Selistre L, De Souza V, Cochat P et al (2012) GFR estimation in adolescents and young adults. J Am Soc Nephrol 23:989–996CrossRefPubMedGoogle Scholar
  10. 10.
    Venkatraman ES, Olshen AB (2007) A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics 23:657–663CrossRefPubMedGoogle Scholar
  11. 11.
    Sen AK, Srivastava MS (1975) On tests for detecting change in mean. Ann Stat 1:98–108CrossRefGoogle Scholar
  12. 12.
    Drion I, Joosten H, Santing L et al (2011) The Cockroft–Gault a better predictor of renal function in an overweight and obese diabetic population. Obes Facts 4:393–399CrossRefPubMedGoogle Scholar
  13. 13.
    Verhave JC, Fesler P, Ribstein J, du Cailar G, Mimran A (2005) Estimation of renal function in subjects with normal serum creatinine levels: influence of age and body mass index. Am J Kidney Dis 46:233–241CrossRefPubMedGoogle Scholar
  14. 14.
    Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D (1999) 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 130:461–470CrossRefPubMedGoogle Scholar
  15. 15.
    Vervoort G, Willems HL, Wetzels JF (2002) Assessment of glomerular filtration rate in healthy subjects and normoalbuminuric diabetic patients: validity of a new (MDRD) prediction equation. Nephrol Dial Transplant 17:1909–1913CrossRefPubMedGoogle Scholar
  16. 16.
    Teruel Briones JL, Gomis Couto A, Sabater J et al (2011) Validation of the Chronic Kidney Disease Epidemiology Collaboration (CKD–EPI) equation in advanced chronic renal failure. Nefrologia 31:677–682PubMedGoogle Scholar
  17. 17.
    Lewis J, Agodoa L, Cheek D et al (2001) Comparison of crosssectional renal function measurements in African Americans with hypertensive nephrosclerosis and of primary formulas to estimate glomerular filtration rate. Am J Kidney Dis 38:744–753CrossRefPubMedGoogle Scholar
  18. 18.
    Poggio ED, Wang X, Greene T, Van Lente F, Hall PM (2005) Performance of the modification of diet in renal disease and Cockroft–Gault equations in the estimation of GFR in health and in chronic kidney disease. J Am Soc Nephrol 16:459–466CrossRefPubMedGoogle Scholar
  19. 19.
    Kingdon EJ, Knight CJ, Dustan K et al (2003) Calculated glomerular filtration rate is a useful screening tool to identify scleroderma patients with renal impairment. Rheumatology 42:26–33CrossRefPubMedGoogle Scholar
  20. 20.
    Kang YS, Han KH, Han SY, Kim HK, Cha DR (2005) Characteristics of population with normal serum creatinine impaired renal function and: the validation of a MDRD formula in a healthy general population. Clin Nephrol 63:258–266CrossRefPubMedGoogle Scholar
  21. 21.
    Lin J, Knight EL, Hogan ML, Singh AK (2003) A comparison of prediction equations for estimating glomerular filtration rate in adults without kidney disease. J Am Soc Nephrol 14:2573–2580CrossRefPubMedGoogle Scholar
  22. 22.
    Townamchai N, Praditpornsilpa K, Chawatanarat T et al (2013) The validation of estimated glomerular filtration rate equation for renal transplant recipients. Clin Nephrol 79(3):206–213CrossRefPubMedGoogle Scholar
  23. 23.
    Levey AS, Coresh J et al (2002) National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease evaluation, classification, and stratification. Am J Kidney Dis 39(2 Suppl 1):S1–S266Google Scholar
  24. 24.
    Bacchetta J, Cochat P, Rognant N et al (2011) Which creatinine and cystatin C equations can be reliably used in children? Clin J Am Soc Nephrol 6:552–560CrossRefPubMedCentralPubMedGoogle Scholar
  25. 25.
    Staples A, LeBlond R, Watkins S et al (2010) Validation of the revised Schwartz estimating equation in a predominantly non-CKD population. Pediatr Nephrol 25:2321–2326CrossRefPubMedGoogle Scholar
  26. 26.
    Berg UB, Bäck R, Celsi G et al (2011) Comparison of plasma clearance of iohexol and urinary clearance of inulin for measurement of GFR in children. Am J Kidney Dis 57:55–61CrossRefPubMedGoogle Scholar
  27. 27.
    Gao A, Cachat F, Faouzi M et al (2013) Comparison of the glomerular filtration rate in children by the new revised Schwartz formula and a new generalized formula. Kidney Int 83(3):524–530CrossRefPubMedGoogle Scholar

Copyright information

© Italian Society of Nephrology 2014

Authors and Affiliations

  • Antonio Azzi
    • 1
  • Francois Cachat
    • 1
  • Mohamed Faouzi
    • 2
  • Dolores Mosig
    • 1
  • Pascal Ramseyer
    • 3
  • Eric Girardin
    • 1
  • Hassib Chehade
    • 1
    Email author
  1. 1.Division of Pediatric Nephrology, Department of PediatricsLausanne University HospitalLausanneSwitzerland
  2. 2.Institute of Social and Preventive MedicineLausanneSwitzerland
  3. 3.Division of Pediatric Urology, Department of PediatricsLausanne University HospitalLausanneSwitzerland

Personalised recommendations