Keywords
kidney function tests/trends
diagnostic techniques and procedures
young adult
creatinine
CKD-EPI-09
CKD-EPI-21
EKFC
How to Cite
Abstract
Background: In 2012, it was recommended to estimate the glomerular filtration rate (eGFR) using CKD-EPI (CKD-EPI-09). Currently replaced by CKD-EPI-21, without a term referring to race and with new coefficients. In 2020 the EKFC equation was proposed.
Purpose: To evaluate the behavior of CKD-EPI-09, CKD-EPI-21 and EKFC in young people, differences and agreement in the assignment to G categories of TFG.
Methodology: Analytical study approved by the Ethics Committee. Sample: 189 volunteers, 18-37 years old. Caucasians. Creatininemia: kinetic Jaffé method traceable to Isotopic Dilution Mass Spectroscopy. Program: MedCalc.
Results: The GFR estimated by CKD-EPI-21 was higher than that by CKD-EPI-09 and EKFC. Mean of the differences (mL/min/1.73m2): (CKD-EPI-09– CKD-EPI-21)= -2.28; (EKFC– CKD-EPI-21)= -12.54; (EKFC– CKD-EPI-09)= -10.25. Assignment to G categories: the best kappa index in assignment to category G corresponded to CKD-EPI-09 vs. CKD-EPI-21. Recategorization: from G2 by CKD-EPI-09 to G1 by CKD-EPI-21 and EKFC: 4.2% and 21.2% respectively; from G2 by EKFC to G1 by CKD-EPI-09: 16.9%; by sex: in the same sense.
Conclusions: The differences by CKD-EPI-21 are exclusively due to the change in coefficients in age, sex and creatininemia. The eGFR with CKD-EPI-21 slightly increased that estimated by CKD-EPI-09, its difference with EKFC being the greatest. Agreement in assignment to category G: considerable-almost perfect between both CKD-EPI and lower between the other estimators. The CKD-EPI-21 vs. CKD-EPI-09 was 4%, from G2 to G1. Higher percentages in G2 by EKFC recategorized G1 by both CKD-EPI. It would be important to validate the equations against a reference method to select the most appropriate one for clinical use.
References
Kidney Disease Improving Global Outcomes. KDIGO 2012: Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int Suppl. 2013;3(1). https://kdigo.org/wp-content/uploads/2017/02/KDIGO_2012_CKD_GL.pdf
Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro 3rd AF, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604-12. https://doi.org/10.7326/0003-4819-150-9-200905050-00006
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. 2020;174(2):183-91. https://doi.org/10.7326/m20-4366
Kidney Disease Improving Global Outcomes. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. 2024;105(supl. 4S):S117-314. https://kdigo.org/wp-content/uploads/2024/03/KDIGO-2024-CKD-Guideline.pdf
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(19):1737-49. https://doi.org/10.1056/nejmoa2102953
Schmidt IM, Waikar SS. Separate and unequal: race-based algorithms and implications for nephrology. J Am Soc Nephrol. 2021;32(3):529-33. https://doi.org/10.1681/asn.2020081175
Vyas DA, Eisenstein LG, Jones DS. Hidden in plain sight - reconsidering the use of race correction in clinical algorithms. N Engl J Med. 2020;383(9):874-82. https://doi.org/10.1056/nejmms2004740
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(12):2994-3015. https://doi.org/10.1681/asn.2021070988
Levey AS, Eckardt KU, Dorman NM, Christiansen SL, Hoorn EJ, Ingelnger JR, et al. Nomenclature for kidney function and disease: report of a kidney disease: improving global outcomes (KDIGO) consensus conference. Kidney Int. 2020;97(6):1117-29. https://doi.org/10.1016/j.kint.2020.02.010
Landis J, Koch G. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159-74.
Brissón CM, Cuestas VI, Denner SG, Fernández VG, Prono Minella PC, Bonifacino Belzarena R, et al. Estimación de la tasa de filtración glomerular: concordancia de la ecuación del European Kidney Function Consortium de 2020 con CKD-EPI y FAS en adultos jóvenes. Rev Colomb Nefrol. 2022;9(1):e556. https://doi.org/10.22265/acnef.9.1.556
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. 2023;38(1):106-18. https://doi.org/10.1093/ndt/gfac241
Kim H, Hur M, Lee S, Lee GH, Moon HW, Yun YM. European Kidney Function Consortium Equation vs. Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) Refit Equations for Estimating Glomerular Filtration Rate: Comparison with CKD-EPI Equations in the Korean Population. J Clin Med. 2022;11(15):4323. https://doi.org/10.3390/jcm11154323
Betzler BK, Sultana R, He F, Tham YC, Lim CC, Wang YX, et al. Impact of Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) GFR Estimating Equations on CKD Prevalence and Classification Among Asians. Front. Med. 2022;9:957437. https://doi.org/10.3389/fmed.2022.957437
Shen Y, Wu H, Liu X, Zhu J, Shao W, Wang B, et al. Comparison of the 2021 and 2009 chronic kidney disease epidemiology collaboration creatinine equation for estimated glomerular filtration rate in a Chinese population. Clin Biochem. 2023;116:59-64. https://doi.org/10.1016/j.clinbiochem.2023.03.011
Husain SA, King KL, Mohan S. Differences between race-based and race-free estimated glomerular filtration rate among living kidney donors. Am J Transplant. 2022;22(5):1504-5. https://doi.org/10.1111/ajt.16962
Delanaye P, Rule AD, Schaeffner E, Cavalier E, Shi J, Hoofnagle AN, et al. Performance of the European Kidney Function Consortium (EKFC) creatinine-based equation in United States cohorts. Kidney Int. 2024;105(3):629-37. https://doi.org/10.1016/j.kint.2023.11.024
Inker LA, Tighiouart H, Adingwupu OM, Shlipak MG, Doria A, Estrella MM, et al. CKD-EPI and EKFC GFR Estimating Equations: Performance and Other Considerations for Selecting Equations for Implementation in Adults. J Am Soc Nephrol. 2023;34(12):1953-64. https://doi.org/10.1681/asn.0000000000000227
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.