Skip to main content
SpringerLink
Log in

A Population Pharmacokinetic Model for 51Cr EDTA to Estimate Renal Function

  • Short Communication
  • Published:
Clinical Pharmacokinetics Aims and scope Submit manuscript

Abstract

Background and Objectives

51Cr EDTA clearance (CL) from plasma is used to estimate glomerular filtration rate (GFR). We propose that current methods for analysing the raw 51Cr EDTA measurements over-simplifies the disposition of 51Cr EDTA and therefore could produce biased GFR estimates. The aim of this study was to develop a population pharmacokinetic model for 51Cr EDTA disposition and to compare model-predicted GFR to other methods of estimating renal function.

Patients and Methods

Data from 40 individuals who received ~7.4 MBq of 51Cr EDTA, as an intravenous bolus, were available for analysis. Plasma radioactivity (counts/min) was measured from timed collection points at 2, 4, 6 and 24 h after the dose. A population analysis was conducted using NONMEM® version 7.2. Model-predicted GFR was compared with other methods for estimating renal function using mean prediction error (MPE).

Results

A two-compartment pharmacokinetic model with first-order elimination best fit the data. Compared with the model predictions, creatinine CL from 24 h urine data was unbiased. The commonly used ‘slope-intercept’ method for estimating isotopic GFR was positively biased compared with the model (MPE 15.5 mL/min/1.73 m2 [95% confidence interval {CI} 8.9–22.2]. The Cockcroft Gault, Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-Epi) equations led to negatively biased GFR estimates (MPE −19.0 [95% CI −25.4 to −12.7], −20.1 [95% CI −27.2 to −13.1] and −16.5 [95% CI −22.2 to −10.1] mL/min/1.73 m2, respectively).

Conclusions

The biased GFR estimates were most obvious in patients with relatively normal renal function. This may lead to inaccurate dosing in patients who are receiving drugs with a narrow therapeutic range where dosing is adjusted according to GFR estimates (e.g. carboplatin).

Study Registration

The study is registered with the Australian New Zealand Clinical Trials Registry (ANZCTR), number: ACTRN 12611000035921.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

References

  1. Stevens LA, Levey AS. Measured GFR as a confirmatory test for estimated GFR. J Am Soc Nephrol. 2009;20(11):2305–13.

    Article  PubMed  Google Scholar 

  2. Doogue MP, Polasek TM. Drug dosing in renal disease. Clin Biochem Rev. 2011;32(2):69–73.

    PubMed  PubMed Central  Google Scholar 

  3. Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron. 1976;16(1):31–41.

    Article  CAS  PubMed  Google Scholar 

  4. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. 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(6):461–70.

    Article  CAS  PubMed  Google Scholar 

  5. Levey AS, Coresh J, Greene T, Stevens LA, Zhang YL, Hendriksen 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. 2006;145(4):247–54.

    Article  CAS  PubMed  Google Scholar 

  6. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604–12.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Piepsz A, Colarinha P, Gordon I, Hahn K, Olivier P, Sixt R, et al. Guidelines for glomerular filtration rate determination in children. Eur J Nucl Med. 2001;28(3):BP31–6.

    CAS  PubMed  Google Scholar 

  8. Calvert AH, Newell DR, Gumbrell LA, O’Reilly S, Burnell M, Boxall FE, et al. Carboplatin dosage: prospective evaluation of a simple formula based on renal function. J Clin Oncol. 1989;7(11):1748–56.

    Article  CAS  PubMed  Google Scholar 

  9. Fleming JS, Zivanovic MA, Blake GM, Burniston M, Cosgriff PS. Guidelines for the measurement of glomerular filtration rate using plasma sampling. Nucl Med Commun. 2004;25(8):759–69.

    Article  PubMed  Google Scholar 

  10. Gowda S, Desai PB, Kulkarni SS, Hull VV, Math AAK, Vernekar SN. Markers of renal function tests. North Am J Med Sci. 2010;2(4):170–3.

    Google Scholar 

  11. Chantler C, Garnett ES, Parsons V, Veall N. Glomerular filtration rate measurement in man by the single injection methods using 51Cr-EDTA. Clin Sci. 1969;37(1):169–80.

    CAS  PubMed  Google Scholar 

  12. Garnett ES, Parsons V, Veall N. Measurement of glomerular filtration rate in man using a 51Cr/edetic-acid complex. Lancet. 1967;289(7494):818–9.

    Article  Google Scholar 

  13. Grubb A, Nyman U, Bjork J, Lindstrom V, Rippe B, Sterner G, 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. 2005;51(8):1420–31.

    Article  CAS  PubMed  Google Scholar 

  14. Brochner-Mortensen J, Giese J, Rossing N. Renal inulin clearance versus total plasma clearance of 51Cr-EDTA. Scand J Clin Lab Invest. 1969;23(4):301–5.

    Article  CAS  PubMed  Google Scholar 

  15. Chantler C, Barratt TM. Estimation of glomerular filtration rate from plasma clearance of 51-chromium edetic acid. Arch Dis Child. 1972;47(254):613–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Groth S. Calculation of 51Cr-EDTA clearance in children from the activity in one plasma sample by transformation of the biexponential plasma time–activity curve into a monoexponential with identical integral area below the time–activity curve. Clin Physiol. 1984;4(1):61–74.

    Article  CAS  PubMed  Google Scholar 

  17. Brochner-Mortensen J. A simple method for the determination of glomerular filtration rate. Scand J Clin Lab Invest. 1972;30(3):271–4.

    Article  CAS  PubMed  Google Scholar 

  18. Putt TL, Duffull SB, Schollum JB, Walker RJ. GFR may not accurately predict aspects of proximal tubule drug handling. Eur J Clin Pharmacol. 2014;70(10):1221–6.

    Article  CAS  PubMed  Google Scholar 

  19. Janmahasatian S, Duffull SB, Ash S, Ward LC, Byrne NM, Green B. Quantification of lean bodyweight. Clin Pharmacokinet. 2005;44(10):1051–65.

    Article  PubMed  Google Scholar 

  20. Sheiner LB, Beal SL. Some suggestions for measuring predictive performance. J Pharmacokinet Biopharm. 1981;9(4):503–12.

    Article  CAS  PubMed  Google Scholar 

  21. Cole M, Price L, Parry A, Keir MJ, Pearson AD, Boddy AV, et al. Estimation of glomerular filtration rate in paediatric cancer patients using 51CR-EDTA population pharmacokinetics. Br J Cancer. 2004;90(1):60–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Bouvet Y, Bouissou F, Coulais Y, Seronie-Vivien S, Tafani M, Decramer S, et al. GFR is better estimated by considering both serum cystatin C and creatinine levels. Pediatr Nephrol. 2006;21(9):1299–306.

    Article  PubMed  Google Scholar 

  23. Leger F, Bouissou F, Coulais Y, Tafani M, Chatelut E. Estimation of glomerular filtration rate in children. Pediatr Nephrol. 2002;17(11):903–7.

    Article  PubMed  Google Scholar 

  24. Seronie-Vivien S, Bouissou F, Dattez S, Coulais Y, Hanser AM, Hym B, et al. Modeling the variability of creatinine measurements improves estimates of the glomerular filtration rate. Clin Chem Lab Med. 2008;46(2):215–8.

    Article  CAS  PubMed  Google Scholar 

  25. Wright JG, Boddy AV, Highley M, Fenwick J, McGill A, Calvert AH. Estimation of glomerular filtration rate in cancer patients. Br J Cancer. 2001;84(4):452–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Bouquegneau A, Vidal-Petiot E, Vrtovsnik F, Cavalier E, Rorive M, Krzesinski JM, et al. Modification of Diet in Renal Disease versus Chronic Kidney Disease Epidemiology Collaboration equation to estimate glomerular filtration rate in obese patients. Nephrol Dial Transpl. 2013;28(Suppl 4):iv122–30.

    Article  Google Scholar 

  27. Michels WM, Grootendorst DC, Verduijn M, Elliott EG, Dekker FW, Krediet RT. 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(6):1003–9.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Verhave JC, Fesler P, Ribstein J, du Cailar G, Mimran A. Estimation of renal function in subjects with normal serum creatinine levels: influence of age and body mass index. Am J Kidney Dis. 2005;46(2):233–41.

    Article  CAS  PubMed  Google Scholar 

  29. Medeiros FS, Sapienza MT, Prado ES, Agena F, Shimizu MH, Lemos FB, et al. Validation of plasma clearance of 51Cr-EDTA in adult renal transplant recipients: comparison with inulin renal clearance. Transpl Int. 2009;22(3):323–31.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge Dr. Jeremy Nicholl and the nuclear medicine team at Dunedin Hospital, Dunedin, New Zealand for their assistance with the 51Cr EDTA data.

Author information

Authors and Affiliations

  1. School of Pharmacy, University of Otago, PO Box 56, Dunedin, 9054, New Zealand

    Isabelle H. S. Kuan, Stephen B. Duffull & Daniel F. B. Wright

  2. Department of Medicine, University of Otago, Dunedin, New Zealand

    Tracey L. Putt, John B. W. Schollum & Robert J. Walker

Authors
  1. Isabelle H. S. Kuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stephen B. Duffull
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tracey L. Putt
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. John B. W. Schollum
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Robert J. Walker
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Daniel F. B. Wright
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel F. B. Wright.

Ethics declarations

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed, written, consent was obtained from all individual participants included in the study.

Funding

Isabelle Kuan was supported by a grant from the New Zealand Pharmacy Education and Research Foundation. The original data collection was funded by a Laurenson Award, Otago Medical Research Foundation, University of Otago, Dunedin, New Zealand.

Conflict of interest

The authors (Isabelle Kuan, Stephen Duffull, Tracey Putt, John Schollum, Robert Walker, Daniel Wright) declare that they have no conflicts of interest.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kuan, I.H.S., Duffull, S.B., Putt, T.L. et al. A Population Pharmacokinetic Model for 51Cr EDTA to Estimate Renal Function. Clin Pharmacokinet 56, 671–678 (2017). https://doi.org/10.1007/s40262-016-0489-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40262-016-0489-x

Keywords

Search

Navigation