Evidence map›Paper›PMID 42724472›Full record

ArticleClinical kidney journal2026

Which equation and which biomarker should be used in the Kidney Failure Risk Equation? A comparative analysis in the CKD-REIN cohort.

Antoine Lanot, Pierre Delanaye, Marie Metzger, Aghiles Hamroun, Natalia Alencar de Pinho, Christophe Mariat

Abstract read
In one paragraph

Article in Clinical kidney journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Antoine LanotNormandie Univ, UNICAEN, CHU de Caen Normandie, Néphrologie, Caen, France.ORCID https://orcid.org/0000-0003-0178-7389
Pierre DelanayeDepartment of Nephrology-Dialysis-Transplantation, University of Liège, CHU Sart Tilman, Liège, Belgium.ORCID https://orcid.org/0000-0002-1480-5761
Marie MetzgerParis Saclay University, Paris-Sud Univ, UVSQ, CESP, INSERM, Renal and Cardiovascular Epidemiology Team, Villejuif, France.
Aghiles HamrounDepartment 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.
Natalia Alencar de PinhoParis Saclay University, Paris-Sud Univ, UVSQ, CESP, INSERM, Renal and Cardiovascular Epidemiology Team, Villejuif, France.ORCID https://orcid.org/0000-0002-0438-6487
Christophe MariatService de Nephrologie, Dialyse et Transplantation Renale, Hopital Nord, CHU de Saint-Etienne, 25 Boulevard Pasteur, Saint-Etienne Cedex 2, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

chronic kidney diseaseeGFREKFCKFREkidney failure

Identifiers

PMID42724472
PMCPMC13559190

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.