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Clinical Pharmacokinetics

, Volume 56, Issue 2, pp 193–205 | Cite as

Discrepancies between the Cockcroft–Gault and Chronic Kidney Disease Epidemiology (CKD-EPI) Equations: Implications for Refining Drug Dosage Adjustment Strategies

  • Pierre DelanayeEmail author
  • Fabrice Guerber
  • André Scheen
  • Timothy Ellam
  • Antoine Bouquegneau
  • Dorra Guergour
  • Christophe Mariat
  • Hans Pottel
Original Research Article

Abstract

Introduction

The dosages of many medications require adjustment for renal function. There is debate regarding which equation, the Chronic Kidney Disease Epidemiology (CKD-EPI) equation vs. the Cockcroft–Gault (CG) equation, should be recommended to estimate glomerular filtration rate.

Methods

We used a mathematical simulation to determine how patient characteristics influence discrepancies between equations and analyzed clinical data to demonstrate the frequency of such discrepancies in clinical practice. In the simulation, the modifiable variables were sex, age, serum creatinine, and weight. We considered estimated glomerular filtration rate results in mL/min, deindexed for body surface area, because absolute excretory function (rather than per 1.73 m2 body surface area) determines the rate of filtration of a drug at a given plasma concentration. An absolute and relative difference of maximum (±) 10 mL/min and 10 %, respectively, were considered concordant. Clinical data for patients aged over 60 years (n = 9091) were available from one hospital and 25 private laboratories.

Results

In the simulation, differences between the two equations were found to be influenced by each variable but age and weight had the biggest effect. Clinical sample data demonstrated concordance between CKD-EPI and CG results in 4080 patients (45 %). The majority of discordant results reflected a CG result lower than the CKD-EPI equation. With aging, the CG result became progressively lower than the CKD-EPI result. When weight increased, the opposite occurred.

Discussion

The choice of equation for excretory function adjustment of drug dosage will have different implications for patients of different ages and body habitus.

Conclusions

The optimum equation for drug dosage adjustment should be defined with consideration of individual patient characteristics.

Keywords

Glomerular Filtration Rate Isotope Dilution Mass Spectrometry Estimate Glomerular Filtration Rate Excretory Function Measured Glomerular Filtration Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Compliance with Ethical Standards

Funding

No external funding was used in the preparation of this manuscript.

Conflict of interest

Pierre Delanaye, Fabrice Guerber, André Scheen, Timothy Ellam, Antoine Bouquegneau, Dorra Guergour, Christophe Mariat, and Hans Pottel declare that they have no conflict of interest that might be relevant to the contents of this manuscript.

Supplementary material

40262_2016_434_MOESM1_ESM.xlsx (122 kb)
Supplementary material 1 (XLSX 121 kb)

References

  1. 1.
    Dreisbach AW, Flessner MF. Drug metabolism and chronic kidney disease. In: Kimmel PL, Rosenberg, MK, editors. Chronic renal disease. Academic Press; 2014. p. 674–81.Google Scholar
  2. 2.
    Verbeeck RK, Musuamba FT. Pharmacokinetics and dosage adjustment in patients with renal dysfunction. Eur J Clin Pharmacol. 2009;65:757–73.CrossRefPubMedGoogle Scholar
  3. 3.
    Aymanns C, Keller F, Maus S, Hartmann B, Czock D. Review on pharmacokinetics and pharmacodynamics and the aging kidney. Clin J Am Soc Nephrol. 2010;5:314–27.CrossRefPubMedGoogle Scholar
  4. 4.
    Elinder CG, Barany P, Heimburger O. The use of estimated glomerular filtration rate for dose adjustment of medications in the elderly. Drugs Aging. 2014;31:493–9.CrossRefPubMedGoogle Scholar
  5. 5.
  6. 6.
  7. 7.
    Stevens LA, Nolin TD, Richardson MM, Feldman HI, Lewis JB, Rodby R, et al. Comparison of drug dosing recommendations based on measured GFR and kidney function estimating equations. Am J Kidney Dis. 2009;54:33–42.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Park EJ, Wu K, Mi Z, Dong T, Lawrence JP, Ko CW, 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. 2012;46:1174–87.CrossRefPubMedGoogle Scholar
  9. 9.
    Bouquegneau A, Vidal-Petiot E, Moranne O, Mariat C, Boffa JJ, Vrtovsnik F, et al. Creatinine-based equations for the adjustment of drug dosage in an obese population. Br J Clin Pharmacol. 2016;81:349–61.CrossRefPubMedGoogle Scholar
  10. 10.
    Melloni C, Peterson ED, Chen AY, Szczech LA, Newby LK, Harrington RA, et al. Cockcroft-Gault versus modification of diet in renal disease: importance of glomerular filtration rate formula for classification of chronic kidney disease in patients with non-ST-segment elevation acute coronary syndromes. J Am Coll Cardiol. 2008;51:991–6.CrossRefPubMedGoogle Scholar
  11. 11.
    Corsonello A, Pedone C, Lattanzio F, Onder G, Inc A. Association between glomerular filtration rate and adverse drug reactions in elderly hospitalized patients: the role of the estimating equation. Drugs Aging. 2011;28:379–90.CrossRefPubMedGoogle Scholar
  12. 12.
    Delanaye P, Mariat C. The applicability of eGFR equations to different populations. Nat Rev Nephrol. 2013;9:513–22.CrossRefPubMedGoogle Scholar
  13. 13.
    Wargo KA, Eiland EH III, Hamm W, English TM, Phillippe HM. Comparison of the modification of diet in renal disease and Cockcroft-Gault equations for antimicrobial dosage adjustments. Ann Pharmacother. 2006;40:1248–53.CrossRefPubMedGoogle Scholar
  14. 14.
    Golik MV, Lawrence KR. Comparison of dosing recommendations for antimicrobial drugs based on two methods for assessing kidney function: Cockcroft-Gault and modification of diet in renal disease. Pharmacotherapy. 2008;28:1125–32.CrossRefPubMedGoogle Scholar
  15. 15.
    Hermsen ED, Maiefski M, Florescu MC, Qiu F, Rupp ME. Comparison of the modification of diet in renal disease and Cockcroft-Gault equations for dosing antimicrobials. Pharmacotherapy. 2009;29:649–55.CrossRefPubMedGoogle Scholar
  16. 16.
    Okparavero AA, Tighiouart H, Krishnasami Z, Wyatt CM, Graham H, Hellinger J, et al. Use of glomerular filtration rate estimating equations for drug dosing in HIV-positive patients. Antivir Ther. 2013;18:793–802.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Gouin-Thibault I, Pautas E, Mahe I, Descarpentries C, Nivet-Antoine V, Golmard JL, et al. Is Modification of Diet in Renal Disease formula similar to Cockcroft-Gault formula to assess renal function in elderly hospitalized patients treated with low-molecular-weight heparin? J Gerontol A Biol Sci Med Sci. 2007;62:1300–5.CrossRefPubMedGoogle Scholar
  18. 18.
    Gill J, Malyuk R, Djurdjev O, Levin A. Use of GFR equations to adjust drug doses in an elderly multi-ethnic group: a cautionary tale. Nephrol Dial Transplant. 2007;22:2894–9.CrossRefPubMedGoogle Scholar
  19. 19.
    Healy MF, Speroni KG, Eugenio KR, Murphy PM. Adjusting eptifibatide doses for renal impairment: a model of dosing agreement among various methods of estimating creatinine clearance. Ann Pharmacother. 2012;46:477–83.CrossRefPubMedGoogle Scholar
  20. 20.
    Maccallum PK, Mathur R, Hull S a, Saja K, Green L, Morris JK, et al. Patient safety and estimation of renal function in patients prescribed new oral anticoagulants for stroke prevention in atrial fibrillation: a cross-sectional study. BMJ Open 2013;3:e003343.Google Scholar
  21. 21.
    Helldén A, Odar-Cederlöf I, Nilsson G, Sjöviker S, Söderström A, Von Euler M, et al. Renal function estimations and dose recommendations for dabigatran, gabapentin and valaciclovir: a data simulation study focused on the elderly. BMJ Open. 2013;3:e002686.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Hijazi Z, Hohnloser SH, Oldgren J, Andersson U, Connolly SJ, Eikelboom JW, et al. Efficacy and safety of dabigatran compared with warfarin in relation to baseline renal function in patients with atrial fibrillation: a RE-LY (randomized evaluation of long-term anticoagulation therapy) trial analysis. Circulation. 2014;129:961–70.CrossRefPubMedGoogle Scholar
  23. 23.
    Chin PK, Wright DF, Zhang M, Wallace MC, Roberts RL, Patterson DM, et al. Correlation between trough plasma dabigatran concentrations and estimates of glomerular filtration rate based on creatinine and cystatin C. Drugs R D. 2014;14:113–23.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    de Lemos ML, Hsieh T, Hamata L, Levin A, Swenerton K, Djurdjev O, et al. Evaluation of predictive formulae for glomerular filtration rate for carboplatin dosing in gynecological malignancies. Gynecol Oncol. 2006;103:1063–9.CrossRefPubMedGoogle Scholar
  25. 25.
    Ainsworth NL, Marshall A, Hatcher H, Whitehead L, Whitfield GA, Earl HM. Evaluation of glomerular filtration rate estimation by Cockcroft-Gault, Jelliffe, Wright and Modification of Diet in Renal Disease (MDRD) formulae in oncology patients. Ann Oncol. 2012;23:1845–53.CrossRefPubMedGoogle Scholar
  26. 26.
    Craig AJ, Samol J, Heenan SD, Irwin AG, Britten A. Overestimation of carboplatin doses is avoided by radionuclide GFR measurement. Br J Cancer. 2012;107:1310–6.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Hartlev LB, Boeje CR, Bluhme H, Palshof T, Rehling M. Monitoring renal function during chemotherapy. Eur J Nucl Med Mol Imaging. 2012;39:1478–82.CrossRefPubMedGoogle Scholar
  28. 28.
    Tsao CK, Moshier E, Seng SM, Godbold J, Grossman S, Winston J, et al. Impact of the CKD-EPI equation for estimating renal function on eligibility for cisplatin-based chemotherapy in patients with urothelial cancer. Clin Genitourin Cancer. 2012;10:15–20.CrossRefPubMedGoogle Scholar
  29. 29.
    Kaag D. Carboplatin dose calculation in lung cancer patients with low serum creatinine concentrations using CKD-EPI and Cockcroft-Gault with different weight descriptors. Lung Cancer. 2013;79:54–8.CrossRefPubMedGoogle Scholar
  30. 30.
    Dooley MJ, Poole SG, Rischin D. Dosing of cytotoxic chemotherapy: impact of renal function estimates on dose. Ann Oncol. 2013;24:2746–52.CrossRefPubMedGoogle Scholar
  31. 31.
    Pal SK, Ruel N, Villegas S, Chang M, DeWalt K, Wilson TG, et al. CKD-EPI and Cockcroft-Gault equations identify similar candidates for neoadjuvant chemotherapy in muscle-invasive bladder cancer. PLoS One. 2014;9:e94471.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Shepherd ST, Gillen G, Morrison P, Forte C, Macpherson IR, White JD, et al. Performance of formulae based estimates of glomerular filtration rate for carboplatin dosing in stage 1 seminoma. Eur J Cancer. 2014;50:944–52.CrossRefPubMedGoogle Scholar
  33. 33.
    Cathomas R, Klingbiel D, Geldart TR, Mead GM, Ellis S, Wheater M, et al. Relevant risk of carboplatin underdosing in cancer patients with normal renal function using estimated GFR: lessons from a stage I seminoma cohort. Ann Oncol. 2014;25:1591–7.CrossRefPubMedGoogle Scholar
  34. 34.
    Bennis Y, Savry A, Rocca M, Gauthier-Villano L, Pisano P, Pourroy B. Cisplatin dose adjustment in patients with renal impairment, which recommendations should we follow? Int J Clin Pharm. 2014;36:420–9.CrossRefPubMedGoogle Scholar
  35. 35.
    Chew-Harris JS, Florkowski CM, George PM, Endre ZH. Comparative performances of the new chronic kidney disease epidemiology equations incorporating cystatin C for use in cancer patients. Asia Pac J Clin Oncol. 2014;11:142–51.CrossRefPubMedGoogle Scholar
  36. 36.
    Moranville MP, Jennings HR. Implications of using modification of diet in renal disease versus Cockcroft-Gault equations for renal dosing adjustments. Am J Heal Syst Pharm. 2009;66:154–61.CrossRefGoogle Scholar
  37. 37.
    McFarland MS, Markley BM, Zhang P, Hudson JQ. Evaluation of modification of diet in renal disease study and Cockcroft-Gault equations for sitagliptin dosing. J Nephrol. 2012;25:515–22.CrossRefPubMedGoogle Scholar
  38. 38.
    Lessard BA, Zaiken K. Comparison of equations for dosing of medications requiring renal adjustment. J Am Pharm Assoc. 2013;53:54–7.CrossRefGoogle Scholar
  39. 39.
    Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron. 1976;16:31–41.CrossRefPubMedGoogle Scholar
  40. 40.
    KDIGO 2012. Clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl. 2013;3:1–150.CrossRefGoogle Scholar
  41. 41.
    Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF III, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150:604–12.CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    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.CrossRefPubMedGoogle Scholar
  43. 43.
    Millar JA. The Cockroft and Gault formula for estimation of creatinine clearance: a friendly deconstruction. N Z Med J. 2012;125:119–22.PubMedGoogle Scholar
  44. 44.
    Piéroni L, Delanaye P, Boutten A, Bargnoux AS, Rozet E, Delatour V, et al. A multicentric evaluation of IDMS-traceable creatinine enzymatic assays. Clin Chim Acta. 2011;412:2070–5.CrossRefPubMedGoogle Scholar
  45. 45.
    Delanaye P, Cavalier E, Cristol JP, Delanghe JR. Calibration and precision of serum creatinine and plasma cystatin C measurement: impact on the estimation of glomerular filtration rate. J Nephrol. 2014;27:467–75.CrossRefPubMedGoogle Scholar
  46. 46.
    Earley A, Miskulin D, Lamb EJ, Levey AS, Uhlig K. Estimating equations for glomerular filtration rate in the era of creatinine standardization: a systematic review. Ann Intern Med. 2012;156:785–95.CrossRefPubMedGoogle Scholar
  47. 47.
    Matzke GR, Aronoff GR, Atkinson AJ Jr, Bennett WM, Decker BS, Eckardt K-UU, et al. Drug dosing consideration in patients with acute and chronic kidney disease: a clinical update from kidney disease: improving global outcomes (KDIGO). Kidney Int. 2011;80:1122–37.CrossRefPubMedGoogle Scholar
  48. 48.
    Spruill WJ, Wade WE, Cobb HH III. Comparison of estimated glomerular filtration rate with estimated creatinine clearance in the dosing of drugs requiring adjustments in elderly patients with declining renal function. Am J Geriatr Pharmacother. 2008;6:153–60.CrossRefPubMedGoogle Scholar
  49. 49.
    Nyman HA, Dowling TC, Hudson JQ, Peter WL, Joy MS, Nolin TD. 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. 2011;31:1130–44.CrossRefPubMedGoogle Scholar
  50. 50.
    Nutescu EA, Spinler SA, Wittkowsky A, Dager WE. Low-molecular-weight heparins in renal impairment and obesity: available evidence and clinical practice recommendations across medical and surgical settings. Ann Pharmacother. 2009;43:1064–83.CrossRefPubMedGoogle Scholar
  51. 51.
    Dowling TC, Matzke GR, Murphy JE, Burckart GJ. Evaluation of renal drug dosing: prescribing information and clinical pharmacist approaches. Pharmacotherapy. 2010;30:776–86.CrossRefPubMedGoogle Scholar
  52. 52.
    Dufour B, Toussaint-Hacquard M, Kearney-Schwartz A, Manckoundia MD, Laurain MC, Joly L, et al. Glomerular filtration rate estimated by Cockcroft-Gault formula better predicts anti-Xa levels than modification of the diet in renal disease equation in older patients with prophylactic enoxaparin. J Nutr Health Aging. 2012;16:647–52.CrossRefPubMedGoogle Scholar
  53. 53.
    Schaeffner ES, Ebert N, Delanaye P, Frei U, Gaedeke J, Jakob O, et al. Two novel equations to estimate kidney function in persons aged 70 years or older. Ann Intern Med. 2012;157:471–81.CrossRefPubMedGoogle Scholar
  54. 54.
    Flamant M, Haymann JP, Vidal-Petiot E, Letavernier E, Clerici C, Boffa JJ, et al. GFR estimation using the Cockcroft-Gault, MDRD Study, and CKD-EPI equations in the elderly. Am J Kidney Dis. 2012;60:847–9.CrossRefPubMedGoogle Scholar
  55. 55.
    Fotheringham J, Weatherley N, Kawar B, Fogarty DG, Ellam T. The body composition and excretory burden of lean, obese, and severely obese individuals has implications for the assessment of chronic kidney disease. Kidney Int. 2014;86:1221–8.CrossRefPubMedGoogle Scholar
  56. 56.
    Du Bois D, Du Bois EF. A formula to estimate the approximative surface area if height and weight be known. Arch Intern Med. 1916;17:863–71.CrossRefGoogle Scholar
  57. 57.
    Kuczmarski RJ, Ogden CL, Grummer-Strawn LM, et al. CDC growth charts: United States. Advance data from vital and health statistics; no 314. Hyattsville, Maryland: National Center for Health Statistics; 2000. www.cdc.gov/growthcharts.
  58. 58.
    Delanaye P, Krzesinski JM. Indexing of renal function parameters by body surface area: intelligence or folly? Nephron Clin Pract. 2011;119:c289–92.CrossRefPubMedGoogle Scholar
  59. 59.
    Delanaye P, Cavalier E, Froissart M, Krzesinski J-MM. Reproducibility of GFR measured by chromium-51-EDTA and iohexol. Nephrol Dial Transplant. 2008;23:4077–8.CrossRefPubMedGoogle Scholar
  60. 60.
    Stevens LA, Levey AS. Measured GFR as a confirmatory test for estimated GFR. J Am Soc Nephrol. 2009;20:2305–13.CrossRefPubMedGoogle Scholar
  61. 61.
    Delanaye P, Cavalier E, Morel J, Mehdi M, Maillard N, Claisse G, et al. Detection of decreased glomerular filtration rate in intensive care units: serum cystatin C versus serum creatinine. BMC Nephrol. 2014;15:9.CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    Pai MP. Treatment of bacterial infections in obese adult patients: how to appropriately manage antimicrobial dosage. Curr Opin Pharmacol. 2015;24:12–7.CrossRefPubMedGoogle Scholar
  63. 63.
    Delanaye P, Mariat C, Maillard N, Krzesinski JM, Cavalier E. Are the creatinine-based equations accurate to estimate glomerular filtration rate in african american populations? Clin J Am Soc Nephrol. 2011;6:906–12.CrossRefPubMedGoogle Scholar
  64. 64.
    DeCarolis DD, Thorson JG, Marraffa RA, Clairmont MA, Kuskowski MA. Comparison of equations with estimate renal function to predict serum vancomycin concentration in patients with spinal cord injury: does the use of cystatin C improve accuracy? Ther Drug Monit. 2014;36:632–9.CrossRefPubMedGoogle Scholar
  65. 65.
    Frazee EN, Rule AD, Herrmann SM, Kashani KB, Leung N, Virk A, et al. Serum cystatin C predicts vancomycin trough levels better than serum creatinine in hospitalized patients: a cohort study. Crit Care. 2014;18:R110.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Pierre Delanaye
    • 1
    Email author
  • Fabrice Guerber
    • 2
  • André Scheen
    • 3
  • Timothy Ellam
    • 4
  • Antoine Bouquegneau
    • 1
  • Dorra Guergour
    • 5
  • Christophe Mariat
    • 6
  • Hans Pottel
    • 7
  1. 1.Division of Nephrology, Dialysis and Transplantation, CHU Sart TilmanUniversity of Liège (ULg-CHU)LiègeBelgium
  2. 2.Oriade LaboratoryVizilleFrance
  3. 3.Division of Clinical Pharmacology, Center for Interdisciplinary Research on MedicinesUniversity of LiègeLiègeBelgium
  4. 4.Sheffield Kidney Institute, Northern General Hospital and Department of Infection, Immunity and Cardiovascular ScienceUniversity of SheffieldSheffieldUK
  5. 5.Biochemistry LaboratoryGrenoble University HospitalGrenobleFrance
  6. 6.Division of Nephrology, Dialysis, Transplantation and Hypertension, CHU Hôpital NordUniversity Jean Monnet, PRES Université de LYONSaint-EtienneFrance
  7. 7.Department of Public Health and Primary CareKU, Leuven KulakKortrijkBelgium

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