Background: The performance of creatinine-based glomerular filtration rate (GFR) estimating equations may vary in subgroups defined by GFR, age and body mass index (BMI). This study compares the performance of the Modification of Diet in Renal Disease (MDRD) study and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations with the revised Lund-Malmö equation (LM Revised), a new equation that can be expected to handle changes in GFR across the life span more accurately.
Methods: The study included 3495 examinations in 2847 adult Swedish patients referred for measurement of GFR (mGFR) 2008–2010 by plasma clearance of iohexol (median 52 mL/min/1.73 m2). Bias, precision [interquartile range (IQR)] and accuracy [percentage of estimates ±10% (P10) and ±30% (P30) of mGFR] were compared.
Results: The overall results of LM Revised/MDRD/CKD-EPI were: median bias 2%/8%/11%, IQR 12/14/14 mL/min/1.73 m2, P10 40%/35%/35% and P30 84%/75%/76%. LM Revised was the most stable equation in terms of bias, precision and accuracy across mGFR, age and BMI intervals irrespective of gender. MDRD and CKD-EPI overestimated mGFR in patients with decreased kidney function, young adults and elderly. All three equations overestimated mGFR and had low accuracy in patients with BMI <20 kg/m2, most pronounced among men.
Conclusions: In settings similar to the investigated cohort LM Revised should be preferred to MDRD and CKD-EPI due to its higher accuracy and more stable performance across GFR, age and BMI intervals.
Librarian Elisabeth Sassersson for excellent service regarding literature references.
Conflict of interest statement
Authors’ conflict of interest disclosure: The authors stated that there are no conflicts of interest regarding the publication of this article.
Research funding: Swedish Science Research Council (Project 05196), the Medical Faculty of the Lund University, A. Påhlsson’s, A. Österlund’s, and G. and J. Kock’s Foundations.
Employment or leadership: None declared.
Honorarium: None declared.
Calculation of iohexol clearance (=measured GFR)
GFR was calculated from the iohexol concentration with corrections for lack of complete uniform distribution and non-immediate mixing. Initial GFR was calculated as follows:
where t=time interval between injection and sampling (min), ln=natural logarithm, Qtot=injected amount of iohexol (mg), Ct=iohexol concentration (mg/mL) at time (t) after injection and V=distribution volume (mL) calculated as a function of body weight (kilogram) :
To correct for lack of complete uniform distribution of iohexol the correction factor (m) for distribution volume was calculated :
The corrected distribution volume (V*=V/m) was used calculate the final GFR:
Body surface area equation of Dubois and Dubois .
BSA=0.007184×(weight in kg)0.425×(height in cm)0.725
Equations for estimating GFR
In all equations for estimating GFR given below plasma creatinine (pCr) is expressed in μmol/L (to convert pCr in mg/dL to μmol/L, multiply by 88.4), age in years, height in cm, weight in kg and estimated GFR in mL/min/1.73 m2 body surface area. ln=natural logarithm.
|Revised Lund-Malmö Study equation (LM Revised) |
|CKD-EPI Study equation for Caucasians |
|MDRD Study equation for caucasians based on IDMS-traceable creatinine assays |
|175×(pCr/88.4)−1.154×Age−0.203×0.742 (if female)|
1. KDIGO. KDIGO clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl 2013;3:1–150.Search in Google Scholar
2. Sarnak MJ, Levey AS, Schoolwerth AC, Coresh J, Culleton B, Hamm LL, et al. Kidney disease as a risk factor for development of cardiovascular disease: a statement from the American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention. Circulation 2003;108:2154–69.10.1161/01.CIR.0000095676.90936.80Search in Google Scholar PubMed
3. 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;145:247–54.10.7326/0003-4819-145-4-200608150-00004Search in Google Scholar PubMed
4. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, 3rd, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009;150:604–12.10.7326/0003-4819-150-9-200905050-00006Search in Google Scholar PubMed PubMed Central
5. Ma YC, Zuo L, Chen JH, Luo Q, Yu XQ, Li Y, et al. Modified glomerular filtration rate estimating equation for Chinese patients with chronic kidney disease. J Am Soc Nephrol 2006;17:2937–44.10.1681/ASN.2006040368Search in Google Scholar PubMed
7. Matsuo S, Imai E, Horio M, Yasuda Y, Tomita K, Nitta K, et al. Revised equations for estimated GFR from serum creatinine in Japan. Am J Kidney Dis 2009;53:982–92.10.1053/j.ajkd.2008.12.034Search in Google Scholar PubMed
8. Stevens LA, Claybon MA, Schmid CH, Chen J, Horio M, Imai E, et al. Evaluation of the chronic kidney disease epidemiology collaboration equation for estimating the glomerular filtration rate in multiple ethnicities. Kidney Int 2011;79:555–62.10.1038/ki.2010.462Search in Google Scholar PubMed PubMed Central
9. 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 2010;56:32–8.10.1053/j.ajkd.2010.02.344Search in Google Scholar PubMed
10. 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: 129–38.10.3109/00365513.2010.543143Search in Google Scholar PubMed
11. Segarra A, de la Torre J, Ramos N, Quiroz A, Garjau M, Torres I, 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 2011;6:2411–20.10.2215/CJN.01150211Search in Google Scholar PubMed PubMed Central
12. Björk J, Jones I, Nyman U, Sjöström P. Validation of the Lund-Malmö, 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 2012;46:212–22.10.3109/00365599.2011.644859Search in Google Scholar PubMed
13. 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;156:785–95.10.7326/0003-4819-156-11-201203200-00391Search in Google Scholar
15. Kwong YT, Stevens LA, Selvin E, Zhang YL, Greene T, Van Lente F, 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 2010;56:39–49.10.1053/j.ajkd.2010.02.347Search in Google Scholar PubMed PubMed Central
16. Stevens LA, Manzi J, Levey AS, Chen J, Deysher AE, Greene T, et al. Impact of creatinine calibration on performance of GFR estimating equations in a pooled individual patient database. Am J Kidney Dis 2007;50:21–35.10.1053/j.ajkd.2007.04.004Search in Google Scholar PubMed
17. Nyman U, Björk J, Lindström V, Grubb A. The Lund-Malmö creatinine-based glomerular filtration rate prediction equation for adults also performs well in children. Scand J Clin Lab Invest 2008;68:568–76.10.1080/00365510801915163Search in Google Scholar PubMed
18. SBU. Swedish Council on Health Technology Assessment (Statens Beredning för Medicinsk Utvärdering). Estimation of renal function (Skattning av njurfunktion) 2013; Report 214. Available from: http://www.sbu.se/214. Accessed on 25 October, 2013.Search in Google Scholar
19. Grubb A, Blirup-Jensen S, Lindstrom V, Schmidt C, Althaus H, Zegers I. First certified reference material for cystatin C in human serum ERM-DA471/IFCC. Clin Chem Lab Med 2010;48:1619–21.10.1515/CCLM.2010.318Search in Google Scholar PubMed
20. Bäck SE, Krutzen E, Nilsson-Ehle P. Contrast media as markers for glomerular filtration: a pharmacokinetic comparison of four agents. Scand J Clin Lab Invest 1988;48:247–53.10.3109/00365518809167491Search in Google Scholar PubMed
21. Bird NJ, Peters C, Michell AR, Peters AM. Comparison of GFR measurements assessed from single versus multiple samples. Am J Kidney Dis 2009;54:278–88.10.1053/j.ajkd.2009.03.026Search in Google Scholar PubMed
22. Nilsson-Ehle P. Iohexol clearance for the determination of glomerular filtration rate: 15 years’ experience in clinical practice. eJIFCC 2002;13. Available from: http://www.ifcc.org/ifccfiles/docs/130201005.pdf. Accessed on 25 October, 2013.Search in Google Scholar
23. Sterner G, Frennby B, Hultberg B, Almen T. Iohexol clearance for GFR-determination in renal failure – single or multiple plasma sampling? Nephrol Dial Transplant 1996;11:521–5.10.1093/ndt/11.3.521Search in Google Scholar
25. Krutzen E, Back SE, Nilsson-Ehle I, Nilsson-Ehle P. Plasma clearance of a new contrast agent, iohexol: a method for the assessment of glomerular filtration rate. J Lab Clin Med 1984;104:955–61.Search in Google Scholar
28. Stevens LA, Zhang Y, Schmid CH. Evaluating the performance of equations for estimating glomerular filtration rate. J Nephrol 2008;21:797–807.Search in Google Scholar
29. Spinler SA, Nawarskas JJ, Boyce EG, Connors JE, Charland SL, Goldfarb S. Predictive performance of ten equations for estimating creatinine clearance in cardiac patients. Iohexol Cooperative Study Group. Ann Pharmacother 1998;32:1275–83.10.1345/aph.18122Search in Google Scholar PubMed
30. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Part 5. Evaluation of laboratory measurements for clinical assessment of kidney disease. Am J Kidney Dis 2002;39:S76–110.10.1053/ajkd.2002.30944Search in Google Scholar
32. Delanaye P, Cavalier E. Staging chronic kidney disease and estimating glomerular filtration rate: an opinion paper about the new international recommendations. Clin Chem Lab Med 2013;51:1911–7.10.1515/cclm-2013-0223Search in Google Scholar PubMed
33. Evans M, van Stralen KJ, Schon S, Prutz KG, Stendahl M, Rippe B, et al. Glomerular filtration rate-estimating equations for patients with advanced chronic kidney disease. Nephrol Dial Transplant 2013;28:2518–26.10.1093/ndt/gft226Search in Google Scholar PubMed
34. Björk J, Grubb A, Sterner G, Nyman U. Revised equations for estimating glomerular filtration rate based on the Lund-Malmö Study cohort. Scand J Clin Lab Invest 2011;71:232–9.10.3109/00365513.2011.557086Search in Google Scholar PubMed
35. 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: 471–81.10.7326/0003-4819-157-7-201210020-00003Search in Google Scholar PubMed
36. Koppe L, Klich A, Dubourg L, Ecochard R, Hadj-Aissa A. Performance of creatinine-based equations compared in older patients. J Nephrol 2013;26:716–23.10.5301/jn.5000297Search in Google Scholar PubMed
37. 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;60:847–9.10.1053/j.ajkd.2012.08.001Search in Google Scholar PubMed
38. Murata K, Baumann NA, Saenger AK, Larson TS, Rule AD, Lieske JC. 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 2011;6:1963–72.10.2215/CJN.02300311Search in Google Scholar PubMed PubMed Central
39. Stevens LA, Coresh J, Feldman HI, Greene T, Lash JP, Nelson RG, et al. Evaluation of the modification of diet in renal disease study equation in a large diverse population. J Am Soc Nephrol 2007;18:2749–57.10.1681/ASN.2007020199Search in Google Scholar PubMed
40. Björk J, Bäck SE, Sterner G, Carlson J, Lindström V, Bakoush O, et al. Prediction of relative glomerular filtration rate in adults: new improved equations based on Swedish Caucasians and standardized plasma-creatinine assays. Scand J Clin Lab Invest 2007;67:678–95.10.1080/00365510701326891Search in Google Scholar PubMed
41. 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 Transplant 2013;28(Suppl 4):iv122–30.10.1093/ndt/gft329Search in Google Scholar PubMed
42. Eriksen BO, Melsom T, Mathisen UD, Jenssen TG, Solbu MD, Toft I. GFR normalized to total body water allows comparisons across genders and body sizes. J Am Soc Nephrol 2011;22: 1517–25.10.1681/ASN.2010121321Search in Google Scholar PubMed PubMed Central
43. Nyman U, Grubb A, Sterner G, Björk J. Different equations to combine creatinine and cystatin C to predict GFR. Arithmetic mean of existing equations performs as well as complex combinations. Scand J Clin Lab Invest 2009;69:619–27.10.1080/00365510902946992Search in Google Scholar PubMed
44. Stevens LA, Coresh J, Schmid CH, Feldman HI, Froissart M, Kusek J, et al. Estimating GFR using serum cystatin C alone and in combination with serum creatinine: a pooled analysis of 3,418 individuals with CKD. Am J Kidney Dis 2008;51: 395–406.10.1053/j.ajkd.2007.11.018Search in Google Scholar PubMed PubMed Central
45. Tidman M, Sjöström P, Jones I. A Comparison of GFR estimating formulae based upon s-cystatin C and s-creatinine and a combination of the two. Nephrol Dial Transplant 2008; 23:154–60.10.1093/ndt/gfm661Search in Google Scholar PubMed
46. Granerus G, Jacobsson L. Calculation of 51-Cr-EDTA single injection clearance. Comparison between a single sample and multiple sample formula. Swedish Soc Radiol Proc 1985;19:71–3.Search in Google Scholar
©2014 by Walter de Gruyter Berlin/Boston