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Publicly Available Published by De Gruyter October 24, 2022

The new, race-free, Chronic Kidney Disease Epidemiology Consortium (CKD-EPI) equation to estimate glomerular filtration rate: is it applicable in Europe? A position statement by the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM)

  • Pierre Delanaye ORCID logo , Elke Schaeffner , Mario Cozzolino , Michel Langlois , Mario Plebani ORCID logo , Tomris Ozben and Etienne Cavalier ORCID logo EMAIL logo

Abstract

The EFLM recommends not to implement the race-free Chronic Kidney Disease Epidemiology Consortium (CKD-EPI) equation in European laboratories and to keep the 2009 version of the CKD-EPI equation, without applying a race correction factor. This recommendation is completely in line with a recent Editorial published by the European Renal Association who has also proposed to change to a novel equation only when it has considerably better performance, trying to reach global consensus before implementing such a new glomerular filtration rate (GFR) estimation equation. In Europe, this equation could be for instance the new European Kidney Function Consortium (EKFC) equation, which is population-specific, developed from European cohorts and accurate from infants to the older old. Beyond serum creatinine, the estimating equations based on cystatin C will probably gain in popularity, especially because cystatin C seems independent of race. Finally, we must keep in mind that all GFR equations remain an estimation of GFR, especially rough at the individual level. Measuring GFR with a reference method, such as iohexol clearance, remains indicated in specific patients and/or specific situations, and here also, the role of the clinical laboratories is central and should still evolve positively in the future.

Compared to the “old” Cockcroft and Gault equation, the “new” creatinine-based equations to estimate glomerular filtration rate (GFR) allow a systematic estimation of the GFR (eGFR) with creatinine results by clinical laboratories. Yet, the decision to automatically report (or not) the eGFR on the results protocol and the choice of the equation used to estimate GFR engages the responsibility of the clinical laboratories [1]. However, the choice of the eGFR equation is not so easy. Indeed, several equations are available. As the Chronic Kidney Disease Epidemiology Consortium (CKD-EPI) [2] equation, developed in the United States, is clearly the most popular and is also currently recommended by the International Nephrology guidelines (KDIGO) [3], other creatinine-based equations have also been proposed. Among them, we can mention the Revised Lund-Malmö equation [4], currently used in Sweden, and the promising European Kidney Function Consortium equation (EKFC), which has been recently developed from (mostly) European cohorts [5]. Compared to the CKD-EPI equation, this last equation has the advantage to be applicable from children (>2 years) to the oldest population without any transition problem between adolescence and adulthood. Hence, the EKFC equation can also be automatically used by laboratories in children, since it does not use height as a variable like the Schwartz equation [6]. The particularity of the EKFC equation relies on the use of the “Q value”, defined as the median serum creatinine concentration in the population for which it is used. Recent data from Europe and Africa suggest that using a dedicated, population-specific Q value is easily feasible and improves the performance of the equation [7]. Indeed, it is obvious from the literature that the relationship between serum creatinine and GFR is different in different populations. The most illustrative example is observed in the USA. For the same level of GFR, the serum creatinine concentration will be different in Black and White Americans, with higher values observed in Black subjects [2, 8, 9]. We must acknowledge that we still do not really understand why such differences exist [8, 10, 11]. The observation of this difference prompted the authors of the MDRD and CKD-EPI equations to propose a correction coefficient to be applied to Black Americans [2, 9, 12]. This means that for the same level of serum creatinine, eGFR will be 16% higher in Black Americans than in White Americans. This correction has however been severely criticized, first from a strictly scientific point of view. Indeed, this correction factor is applied to all Black Americans, whereas the correction could be different in Black males and Black females. Indeed, the difference in serum creatinine between Black and White females in USA is minimal [10, 13], [14], [15]. More importantly for our purpose, the current race coefficient has been shown to be inapplicable in Black populations outside the USA, like in Europe, Africa and Brazil. In these areas, the CKD-EPI equation without the race coefficient demonstrated better performance than with the race coefficient [10, 13, 16], [17], [18], [19], [20]. These results strongly confirmed that the observed differences in serum creatinine have nothing to do with race (which is, in itself, a social construct, without any scientific or biologic justification) and even less with skin color [7]. In USA, the correction coefficient applied to CKD-EPI equation has even more been criticized for societal reasons. It is beyond the scope of the current editorial to list all valid arguments against the correction coefficient in the USA as a source of inequalities in health care [11, 21]. As a consequence to these arguments, both the National Kidney Foundation and the American Society of Nephrology have recommended to abandon the race coefficient and promote a new race-free version of the CKD-EPI equation which was been published in 2021 [22, 23]. This equation is very similar to the previous CKD-EPI equation published in 2009, but modified as to have the same bias in Black and non-Black populations. As a result, the absolute bias (between 3 and 4 mL/min/1.73 m2) of the race-free CKD-EPI equation is now similar in Black and White populations in the United States. Even if such bias may be considered minimal and not clinically relevant at the patient level, the impact on the chronic kidney disease (CKD) prevalence at the population level is high since it shifts the former overestimation of 0.5 to 3.9 mL/min/1.73 m2 in non-Black Americans and the former overestimation of 3.7 to an underestimation of GFR of 3.6 mL/min/1.73 m2 in Black Americans. If the biases (in absolute value) are now comparable in both populations, the race-free equation is performing worse than the previous version of CKD-EPI in White subjects, and not really better in Black Americans [23]. The impact on the prevalence of CKD (defined as an eGFR< 60 mL/min/1.73 m2) in the United States is important, with a higher prevalence of CKD in Black populations and a lower prevalence in White populations [24], [25], [26]. In Europe, using the new CKD-EPI equation has the same strong implications on CKD epidemiology. In Denmark for example, a country with a vast majority of non-Black population, using the new CKD-EPI equation will lead to a 25% lower prevalence of CKD [27]. According to these considerations, the utility of the new race-free equation in Europe is more than questionable [13]. First, there are obvious reasons to think that this equation is performing worse in White Europeans and the first results in Europe strongly confirm this fact [7, 28, 29]. Second, at the European level, but also in Africa and Brazil, there had never been a “race factor” (or no race correction) problem since it had been recommended not to use a racial coefficient with the previous CKD-EPI equation [10, 13, 16], [17], [18], [19], [20]. Hence, from a European perspective, we do wonder why we should use an equation that is performing worse in the majority of our patients and that is probably underestimating the real CKD prevalence in the European population due to a problem (race correction) that has been specific to the United States.

Thus, at this point, we recommend not to implement the race-free CKD-EPI equation in European laboratories and to keep the 2009 version of the CKD-EPI equation, without applying a race correction factor. This recommendation is completely in line with a recent Editorial published by the European Renal Association who has also proposed to change to a novel equation only when it has considerably better performance, trying to reach global consensus before implementing such a new GFR estimation equation [30]. In Europe, this equation could be for instance the new EKFC equation, which is population-specific, developed from European cohorts and accurate from infants to the older old. Beyond serum creatinine, the estimating equations based on cystatin C will probably gain in popularity, especially because cystatin C seems independent of race [31, 32]. Finally, we must keep in mind that all GFR equations remain an estimation of GFR, especially rough at the individual level [33], [34], [35], [36]. Measuring GFR with a reference method, such as iohexol clearance, remains indicated in specific patients and/or specific situations [37, 38], and here also, the role of the clinical laboratories is central and should still evolve positively in the future [39, 40].


EFLM Task Group Chronic Kidney Diseases: Etienne Cavalier (BE, Chair), Nataša Bogavac Stanojević (RS), Isabel Cachapuz-Guerra (PT), Laurence Chardon (FR), Pierre Delanaye (BE), Konstantinos Makris (GR), Ana Nikler (HR), Oytun Portakal (TR), Elke Schäffner (DE), Tomas Zima (CZ).
Corresponding author: Etienne Cavalier, Department of Clinical Chemistry, CIRM, University of Liège, CHU de Liège, Liège, Belgium, E-mail:

  1. Research funding: None declared.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Informed consent: Not applicable.

  5. Ethical approval: Not applicable.

References

1. Levey, AS, Stevens, LA, Hostetter, T. Automatic reporting of estimated glomerular filtration rate--just what the doctor ordered. Clin Chem 2006;52:2188–93. https://doi.org/10.1373/clinchem.2006.078733.Search in Google Scholar PubMed

2. 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. https://doi.org/10.7326/0003-4819-150-9-200905050-00006.Search in Google Scholar PubMed PubMed Central

3. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl 2013;3:1–150.Search in Google Scholar

4. Björk, J, Grubb, A, Larsson, A, Hansson, L-O, Flodin, M, Sterner, G, et al.. Accuracy of GFR estimating equations combining standardized cystatin C and creatinine assays: a cross-sectional study in Sweden. Clin Chem Lab Med 2015;53:403–14.10.1515/cclm-2014-0578Search in Google Scholar PubMed

5. Pottel, H, Björk, J, Courbebaisse, M, Couzi, L, Ebert, N, Eriksen, BO, et al.. Development and validation of a modified full age spectrum creatinine-based equation to estimate glomerular filtration rate. A cross-sectional analysis of pooled data. Ann Intern Med 2021;174:183–91. https://doi.org/10.7326/m20-4366.Search in Google Scholar PubMed

6. Pottel, H, Björk, J, Bökenkamp, A, Berg, U, Asling-Monemi, K, Selistre, L, et al.. Estimating glomerular filtration rate at the transition from pediatric to adult care. Kidney Int 2019;95:1234–43. https://doi.org/10.1016/j.kint.2018.12.020.Search in Google Scholar PubMed

7. Delanaye, P, Vidal-Petiot, E, Björk, J, Ebert, N, Eriksen, BO, Dubourg, L, et al.. Performance of creatinine-based equations to estimate glomerular filtration rate in white and black populations in Europe, Brazil, and Africa. Nephrol Dial Transplant 2022. https://doi.org/10.1093/ndt/gfac241.Search in Google Scholar PubMed

8. Hsu, C, Yang, W, Parikh, RV, Anderson, AH, Chen, TK, Cohen, DL, et al.. Race, genetic ancestry, and estimating kidney function in CKD. N Engl J Med 2021;385:1750–60. https://doi.org/10.1056/nejmoa2103753.Search in Google Scholar

9. Levey, AS, Tighiouart, H, Titan, SM, Inker, LA. Estimation of glomerular filtration rate with vs without including patient race. JAMA Intern Med 2020;180:793–5. https://doi.org/10.1001/jamainternmed.2020.0045.Search in Google Scholar PubMed PubMed Central

10. Delanaye, P, Mariat, C, Cavalier, E, Glassock, RJ, Gemenne, F, Pottel, H. The « race » correction in estimating glomerular filtration rate: an European point of view. Curr Opin Nephrol Hypertens 2021;30:525–30. https://doi.org/10.1097/mnh.0000000000000739.Search in Google Scholar

11. Eneanya, ND, Boulware, LE, Tsai, J, Bruce, MA, Ford, CL, Harris, C, et al.. Health inequities and the inappropriate use of race in nephrology. Nat Rev Nephrol 2022;18:84–94. https://doi.org/10.1038/s41581-021-00501-8.Search in Google Scholar PubMed PubMed Central

12. 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;130:461–70. https://doi.org/10.7326/0003-4819-130-6-199903160-00002.Search in Google Scholar PubMed

13. Delanaye, P, Pottel, H, Glassock, RJ. Americentrism in estimation of glomerular filtration rate equations. Kidney Int 2022;101:856–8. https://doi.org/10.1016/j.kint.2022.02.022.Search in Google Scholar PubMed

14. Shi, J, Lindo, EG, Baird, GS, Young, B, Ryan, M, Jefferson, JA, et al.. Calculating estimated glomerular filtration rate without the race correction factor: observations at a large academic medical system. Clin Chim Acta 2021;520:16–22. https://doi.org/10.1016/j.cca.2021.05.022.Search in Google Scholar PubMed PubMed Central

15. 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 2011;6:906–12. https://doi.org/10.2215/cjn.10931210.Search in Google Scholar PubMed

16. Bukabau, JB, Yayo, E, Gnionsahé, A, Monnet, D, Pottel, H, Cavalier, E, et al.. Performance of creatinine- or cystatin C–based equations to estimate glomerular filtration rate in sub-Saharan African populations. Kidney Int 2019;95:1181–9. https://doi.org/10.1016/j.kint.2018.11.045.Search in Google Scholar PubMed

17. Flamant, M, Vidal-Petiot, E, Metzger, M, Haymann, J-P, Letavernier, E, Delatour, V, et al.. Performance of GFR estimating equations in African Europeans: basis for a lower race-ethnicity factor than in African Americans. Am J Kidney Dis 2013;62:182–4. https://doi.org/10.1053/j.ajkd.2013.03.015.Search in Google Scholar PubMed

18. van Deventer, HE, George, JA, Paiker, JE, Becker, PJ, Katz, IJ. Estimating glomerular filtration rate in black South Africans by use of the modification of diet in renal disease and Cockcroft-Gault equations. Clin Chem 2008;54:1197–202. https://doi.org/10.1373/clinchem.2007.099085.Search in Google Scholar PubMed

19. Rocha, AD, Garcia, S, Santos, AB, Eduardo, JCC, Mesquita, CT, Lugon, JR, et al.. No race-ethnicity adjustment in CKD-EPI equations is required for estimating glomerular filtration rate in the Brazilian population. Internet J Nephrol 2020;2020:2141038. https://doi.org/10.1155/2020/2141038.Search in Google Scholar PubMed PubMed Central

20. Zanocco, JA, Nishida, SK, Passos, MT, Pereira, AR, Silva, MS, Pereira, AB, et al.. Race adjustment for estimating glomerular filtration rate is not always necessary. Nephron Extra 2012;2:293–302. https://doi.org/10.1159/000343899.Search in Google Scholar PubMed PubMed Central

21. Eneanya, ND, Yang, W, Reese, PP. Reconsidering the consequences of using race to estimate kidney function. JAMA 2019;322:113–4. https://doi.org/10.1001/jama.2019.5774.Search in Google Scholar PubMed

22. Delgado, C, Baweja, M, Crews, DC, Eneanya, ND, Gadegbeku, CA, Inker, LA, et al.. A unifying approach for GFR estimation: recommendations of the NKF-ASN Task force on reassessing the inclusion of race in diagnosing kidney disease. J Am Soc Nephrol 2021;32:2994–3015. https://doi.org/10.1681/asn.2021070988.Search in Google Scholar

23. Inker, LA, Eneanya, ND, Coresh, J, Tighiouart, H, Wang, D, Sang, Y, et al.. New creatinine- and cystatin C–based equations to estimate GFR without race. N Engl J Med 2021;385:1737–49. https://doi.org/10.1056/nejmoa2102953.Search in Google Scholar

24. Walther, CP, Winkelmayer, WC, Navaneethan, SD. Black race coefficient in GFR estimation and diabetes medications in CKD: national estimates. J Am Soc Nephrol 2021;32:1319–21. https://doi.org/10.1681/asn.2020121724.Search in Google Scholar PubMed PubMed Central

25. Walther, CP, Winkelmayer, WC, Navaneethan, SD. Updated US prevalence estimates for chronic kidney disease stage and complications using the new race-free equation to estimate glomerular filtration rate. JAMA Netw Open 2022;5:e220460. https://doi.org/10.1001/jamanetworkopen.2022.0460.Search in Google Scholar PubMed PubMed Central

26. Diao, JA, Powe, NR, Manrai, AK. Race-free equations for eGFR: comparing effects on CKD classification. J Am Soc Nephrol 2021;32:1868–70. https://doi.org/10.1681/asn.2021020224.Search in Google Scholar PubMed PubMed Central

27. Vestergaard, SV, Heide-Jørgensen, U, Birn, H, Christiansen, CF. Effect of the refitted race-free eGFR formula on the CKD prevalence and mortality in the Danish population. Clin J Am Soc Nephrol 2022;17:426–8. https://doi.org/10.2215/cjn.14491121.Search in Google Scholar

28. Delanaye, P, Masson, I, Maillard, N, Pottel, H, Mariat, C. The new 2021 CKD-EPI equation without race in an European cohort of renal transplanted patients. Transplantation 2022. https://doi.org/10.1097/TP.0000000000004234.Search in Google Scholar PubMed

29. Fu, EL, Coresh, J, Grams, ME, Clase, CM, Elinder, C-G, Paik, Paik, et al.. Removing race from the CKD-EPI equation and its impact on prognosis in a predominantly White European population. Nephrol Dial Transplant 2022. https://doi.org/10.1093/ndt/gfac197.Search in Google Scholar PubMed

30. Gansevoort, RT, Anders, H-J, Cozzolino, M, Filser, D, Fouque, D, Ortiz, A, et al.. What should European nephrology do with the new CKD-EPI equation? Nephrol Dial Transplant 2022. https://doi.org/10.1093/ndt/gfac254.Search in Google Scholar PubMed

31. Grubb, A, Nyman, U, Björk, J, Lindström, V, Rippe, B, Sterner, B, 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;51:1420–31. https://doi.org/10.1373/clinchem.2005.051557.Search in Google Scholar PubMed

32. Ottosson Frost, C, Gille-Johnson, P, Blomstrand, E, St-Aubin, V, Leion, F, Grubb, A. Cystatin C-based equations for estimating glomerular filtration rate do not require race or sex coefficients. Scand J Clin Lab Invest 2022;82:162–6. https://doi.org/10.1080/00365513.2022.2031279.Search in Google Scholar PubMed

33. Shafi, T, Zhu, X, Lirette, ST, Rule, AD, Mosley, T, Butler, KR, et al.. Quantifying individual-level inaccuracy in glomerular filtration rate estimation. A cross-sectional study. Ann Intern Med 2022;175:1073–82. https://doi.org/10.7326/m22-0610.Search in Google Scholar

34. Luis-Lima, S, Escamilla-Cabrera, B, Negrín-Mena, N, Estupinan, S, Delgado-Mallen, P, Cobo-Caso, MA, et al.. CKD staging with cystatin C or creatinine-based formulas: flipping the coin. Nephrol Dial Transplant 2019;34:287–94. https://doi.org/10.1093/ndt/gfy086.Search in Google Scholar PubMed

35. Porrini, E, Ruggenenti, P, Luis-Lima, S, Carrara, F, Jiménez, A, de Vries, APJ, et al.. Estimated GFR: time for a critical appraisal. Nat Rev Nephrol 2019;15:177–90. https://doi.org/10.1038/s41581-018-0080-9.Search in Google Scholar PubMed

36. Miller, WG. Perspective on new equations for estimating glomerular filtration rate. Clin Chem 2021;67:820–2. https://doi.org/10.1093/clinchem/hvab029.Search in Google Scholar PubMed

37. Agarwal, R, Delanaye, P. Glomerular filtration rate: when to measure and in which patients? Nephrol Dial Transplant 2019;34:2001–7. https://doi.org/10.1093/ndt/gfy363.Search in Google Scholar PubMed

38. Ebert, N, Bevc, S, Bökenkamp, A, Gaillard, F, Hornum, M, Jager, KJ, et al.. Assessment of kidney function: clinical indications for measured GFR. Clin Kidney J 2021;14:1861–70. https://doi.org/10.1093/ckj/sfab042.Search in Google Scholar PubMed PubMed Central

39. Delanaye, P, Ebert, N, Melsom, T, Gaspari, F, Mariat, C, Cavalier, E, et al.. Iohexol plasma clearance for measuring glomerular filtration rate in clinical practice and research: a review. Part 1: how to measure glomerular filtration rate with iohexol. Clin Kidney J 2016;9:682–99. https://doi.org/10.1093/ckj/sfw070.Search in Google Scholar PubMed PubMed Central

40. Nyssen, L, Delanaye, P, Le Goff, C, Peeters, S, Cavalier, E. A simple LC-MS method for the determination of iohexol and iothalamate in serum, using ioversol as an internal standard. Clin Chim Acta 2016;463:96–102. https://doi.org/10.1016/j.cca.2016.10.021.Search in Google Scholar PubMed

Received: 2022-09-17
Accepted: 2022-09-20
Published Online: 2022-10-24
Published in Print: 2023-01-27

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