[en] [en] BACKGROUND: The Kidney Failure Risk Equation (KFRE) is a widely used tool to estimate the 2- and 5-year risk of progression to kidney failure in patients with chronic kidney disease (CKD). The choice of estimated glomerular filtration rate (eGFR) equation and biomarker may influence its prognostic performance. We aimed at comparing the predictive performance of the KFRE when used with different eGFR equations [CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) vs. EKFC (European Kidney Function Consortium)] and biomarkers (creatinine, cystatin C, or both) in a large French CKD cohort.
METHODS: We analyzed 2168 patients with CKD stages G3-G5 from the prospective Chronic Kidney Disease Renal Epidemiology and Information Network cohort. Eight eGFR equations were evaluated in combination with both the four- and eight-variable KFRE models. Prognostic performance was assessed at 2 and 5 years using the time-dependent area under the receiver operating characteristic curve (AUROC), Brier scores, calibration plots, and decision curve analysis. Subgroup analyses were performed by age and sex.
RESULTS: Discrimination was uniformly high (AUROC > 0.89), but calibration patterns and decision curve analyses differed between equations. The creatinine-based CKD-EPI 2009 equation demonstrated robust and consistent performance across models and time horizons. EKFC equations-especially when using creatinine alone-showed slightly improved clinical utility at the 40% decision threshold. Cystatin C-based equations did not improve KFRE predictions compared to creatinine-based ones.
CONCLUSIONS: In this French CKD cohort, both CKD-EPI 2009 and EKFC creatinine-based equations supported accurate risk prediction using the KFRE. These findings support the use of EKFC in European clinical practice and reinforce the importance of aligning eGFR equations with the population context when applying prognostic models.
Disciplines :
Urology & nephrology
Author, co-author :
Lanot, Antoine ; Normandie Univ, UNICAEN, CHU de Caen Normandie, Néphrologie, Caen, France ; Normandie université, Unicaen, UFR de médecine, 2 rue des rochambelles, Caen, France ; "ANTICIPE" U1086 INSERM-UCN, Centre François Baclesse, Caen, France
Delanaye, Pierre ; Université de Liège - ULiège > Département des sciences cliniques > Néphrologie ; Department of Nephrology-Dialysis-Apheresis, Hôpital Universitaire Carémeau, Université de Montpellier, Nîmes, France
Metzger, Marie; Paris Saclay University, Paris-Sud Univ, UVSQ, CESP, INSERM, Renal and Cardiovascular Epidemiology Team, Villejuif, France
Hamroun, Aghiles; Department of Public Health-Epidemiology, Lille University Hospital Center, Lille University, Maison Régionale de la Recherche Clinique, CHU Lille, 6 Rue du Professeur Laguesse, Lille, France ; INSERM, UMR1167, Risk Factors and Molecular Determinants of Aging-Related Disease (RID-AGE), Pasteur Institute of Lille, Lille University, Lille, France
Alencar de Pinho, Natalia ; Paris Saclay University, Paris-Sud Univ, UVSQ, CESP, INSERM, Renal and Cardiovascular Epidemiology Team, Villejuif, France
Mariat, Christophe; Service de Nephrologie, Dialyse et Transplantation Renale, Hopital Nord, CHU de Saint-Etienne, 25 Boulevard Pasteur, Saint-Etienne Cedex 2, France
Language :
English
Title :
Which equation and which biomarker should be used in the Kidney Failure Risk Equation? A comparative analysis in the CKD-REIN cohort.
GSK - GlaxoSmithKline Boehringer Ingelheim NNF - Novo Nordisk Fonden
Funding text :
N.A.P. coordinates the CKD-REIN cohort study and has received funding from Boehringer Ingelheim France, Novo Nordisk, GlaxoSmithKline (GSK), and Fresenius Medical Care. All grants have been made to the Paris-Saclay University.CKD-REIN was funded by the \u201CCohortes-Investissements d\u2019Avenir\u201D program (ANR-IA-COH-2012/3731), a subsidy from the Ministry of Higher Education and Research for public service charges (No. 2023-1322 of 29 December 2023 of the Finance Ministry\u2019s budget for 2024), and by the national 2010 Hospital Clinical Research Program (PHRC). CKD-REIN is also supported through a public\u2013private partnership with GlaxoSmithKline (GSK) since 2012, Boehringer Ingelheim France since 2022, Novo Nordisk since 2024, Fresenius Medical Care from 2012 to 2024, Vifor France from 2018 to 2023, Sanofi-Genzyme from 2012 to 2015, Baxter and Merck Sharp & Dohme-Chibret (MSD France) from 2012 to 2017, Amgen from 2012 to 2020, Lilly France from 2013 to 2018, Otsuka Pharmaceutical from 2015 to 2020, and AstraZeneca from 2018 to 2021. Inserm Transfert set up and has managed this partnership since 2011. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Cohortes-Investissements d’Avenir
Tangri N, Stevens LA, Griffith J et al. A predictive model for progression of chronic kidney disease to kidney failure. JAMA. 2011; 305: 1553–9. 10.1001/jama.2011.451
Tangri N, Grams ME, Levey AS et al. CKD Prognosis Consortium. Multinational assessment of accuracy of equations for predicting risk of kidney failure: a meta-analysis. JAMA. 2016; 315: 164–74. 10.1001/jama.2015.18202
Astor BC, Matsushita K, Gansevoort RT et al. Lower estimated glomerular filtration rate and higher albuminuria are associated with mortality and end-stage renal disease. A collaborative meta-analysis of kidney disease population cohorts. Kidney Int. 2011; 79: 1331–40. 10.1038/ki.2010.550
Levey AS, Stevens LA, Schmid CH et al. CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration). A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009; 150: 604–12. 10.7326/0003-4819-150-9-200905050-00006
Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. 2024; 105: S117–314. 10.1016/j.kint.2023.10.018
Inker LA, Schmid CH, Tighiouart H et al. CKD-EPI Investigators. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012; 367: 20–29. 10.1056/NEJMoa1114248
Delanaye P, Vidal-Petiot E, Björk J 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: 106–18. 10.1093/ndt/gfac241
Pottel H, Björk J, Courbebaisse M 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. 10.7326/M20-4366
Pottel H, Björk J, Rule AD et al. Cystatin C-based equation to estimate GFR without the inclusion of race and sex. N Engl J Med. 2023; 388: 333–43. 10.1056/NEJMoa2203769
Cavalier E, Zima T, Datta P et al. EFLM Task Group on Chronic Kidney Disease. Recommendations for European laboratories based on the KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Clin Chem Lab Med. 2024; 63: 525–34. 10.1515/cclm-2024-1082
Shlipak MG, Matsushita K, Ärnlöv J et al. CKD Prognosis Consortium. Cystatin C versus creatinine in determining risk based on kidney function. N Engl J Med. 2013; 369: 932–43. 10.1056/NEJMoa1214234
Inker LA, Eneanya ND, Coresh J et al. New creatinine- and cystatin C based equations to estimate GFR without race. N Engl J Med. 2021; 385: 1737–49. 10.1056/NEJMoa2102953
Stengel B, Combe C, Jacquelinet C et al. The French Chronic Kidney Disease-Renal Epidemiology and Information Network (CKD-REIN) cohort study. Nephrol Dial Transplant. 2014; 29: 1500–7. 10.1093/ndt/gft388
Sumida K, Nadkarni GN, Grams ME et al. Chronic Kidney Disease Prognosis Consortium. Conversion of urine protein-creatinine ratio or urine dipstick protein to urine albumin-creatinine ratio for use in chronic kidney disease screening and prognosis: an individual participant-based meta-analysis. Ann Intern Med. 2020; 173: 426–35. 10.7326/M20-0529
Delanaye P, Schaeffner E, Cozzolino M et al. 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). Clin Chem Lab Med. 2022; 61: 44–47. 10.1515/cclm-2022-0928
Gansevoort RT, Anders HJ, Cozzolino M et al. What should European nephrology do with the new CKD-EPI equation?. Nephrol Dial Transplant. 2023; 38: 1–6. 10.1093/ndt/gfac254
Fu EL, Coresh J, Grams ME et al. Removing race from the CKD-EPI equation and its impact on prognosis in a predominantly White European population. Nephrol Dial Transplant. 2023; 38: 119–28. 10.1093/ndt/gfac197
Delanaye P, Cavalier E, Pottel H et al. New and old GFR equations: a European perspective. Clin Kidney J. 2023; 16: 1375–83. 10.1093/ckj/sfad039
Royston P, Parmar MK. Restricted mean survival time: an alternative to the hazard ratio for the design and analysis of randomized trials with a time-to-event outcome. BMC Med Res Methodol. 2013; 13: 152. 10.1186/1471-2288-13-152
Blanche P, Dartigues JF, Jacqmin-Gadda H. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med. 2013; 32: 5381–97. 10.1002/sim.5958
Collins GS, Dhiman P, Ma J et al. Evaluation of clinical prediction models (part 1): from development to external validation. BMJ. 2024; 384: e074819. 10.1136/bmj-2023-074819
Steyerberg EW, Harrell FE Jr, Borsboom GJ et al. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol. 2001; 54: 774–81. 10.1016/S0895-4356(01)00341-9
Gerds TA, Schumacher M. Consistent estimation of the expected Brier score in general survival models with right-censored event times. Biometrical J. 2006; 48: 1029–40. 10.1002/bimj.200610301
Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006; 26: 565–74. 10.1177/0272989X06295361
Elm Ev, Altman DG, Egger M et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008; 61: 344–9. 10.1016/j.jclinepi.2007.11.008
Major RW, Shepherd D, Medcalf JF et al. The Kidney Failure Risk Equation for prediction of end stage renal disease in UK primary care: an external validation and clinical impact projection cohort study. PLoS Med. 2019; 16: e1002955. 10.1371/journal.pmed.1002955
Caldinelli A, FAUROCon AL, Sjölander A et al. Risk-based referral model to nephrologist-specialist care in Stockholm. Nephrol Dial Transplant. 2025; 41: 102–11. [In press]. 10.1093/ndt/gfaf128
Prouvot J, Ahmadpoor P, Clemmer E et al. Kidney Failure Risk Equation performance according to the etiology of chronic kidney disease in the CKD-CAREMEAU cohort. Clin Kidney J. 2025; 18: sfaf258. 10.1093/ckj/sfaf258
de Hond AAH, Steyerberg EW, van Calster B. Interpreting area under the receiver operating characteristic curve. Lancet Digital Health. 2022; 4: e853–5. 10.1016/S2589-7500(22)00188-1
Van Calster B, McLernon DJ, van Smeden M et al. Calibration: the Achilles heel of predictive analytics. BMC Med. 2019; 17: 230. 10.1186/s12916-019-1466-7
Alba AC, Agoritsas T, Walsh M et al. Discrimination and calibration of clinical prediction models: users’ guides to the medical literature. JAMA. 2017; 318: 1377–84. 10.1001/jama.2017.12126
Grams ME, Brunskill NJ, Ballew SH et al. The Kidney Failure Risk Equation: evaluation of novel input variables including eGFR estimated using the CKD-EPI 2021 equation in 59 cohorts. J Am Soc Nephrol. 2023; 34: 482–94. 10.1681/ASN.0000000000000050
Atiquzzaman M, Er L, Djurdjev O et al. Implications of implementing the 2021 CKD-EPI equation without race on managing patients with kidney disease in British Columbia. Kidney Int Rep. 2024; 9: 830–42. 10.1016/j.ekir.2024.01.039
Delanaye P, Flamant M, Vidal-Petiot E et al. Discordant results between creatinine- and cystatin C-based equations for estimating GFR. Kidney Int Rep. 2025; 10: 1248–59. 10.1016/j.ekir.2025.01.030
