Evidence map›Paper›PMID 35689668›Full record

ArticleNephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association2023

Removing race from the CKD-EPI equation and its impact on prognosis in a predominantly White European population.

Edouard L Fu, Josef Coresh, Morgan E Grams, Catherine M Clase, Carl-Gustaf Elinder, Julie Paik, Chava L Ramspek, Lesley A Inker, Andrew S Levey, Friedo W Dekker and 1 more

Open access · bronzeAbstract read
In one paragraph

Article in Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed, 2 pooled it
9.5field-weighted citation impact, top 1% of its field
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

51 citing papers in PubMed, 2 syntheses or guidelines pooled it, 67 citations in OpenAlex.

  1. Pooled it
  2. Guideline
  3. Article
  4. Article
  5. A Microsimulation-Based Approach for Mitigating Societal Bias in Chronic Kidney Disease Data.Medical decision making : an international journal of the Society for Medical Decision Making · 2026
    Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. A microsimulation-based framework for mitigating societal bias in primary care data.medRxiv : the preprint server for health sciences · 2025
    Article
  11. Article
  12. Observational
  13. Article
  14. Article
  15. Article
  16. Comparison of estimated glomerular filtration rate equations on prediction of mortality, kidney failure and acute kidney injury.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2025
    Article
  17. Article
  18. Article
  19. Article
  20. Observational
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 7 institutions in 4 countries.

Edouard L FuDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Josef CoreshDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Morgan E GramsDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Catherine M ClaseDepartments of Medicine and Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada.
Carl-Gustaf ElinderDivision of Renal Medicine, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden.
Julie PaikDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Chava L RamspekDepartment of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
Lesley A InkerDivision of Nephrology, Tufts Medical Center, Boston, MA, USA.
Andrew S LeveyDivision of Nephrology, Tufts Medical Center, Boston, MA, USA.
Friedo W DekkerDepartment of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
Juan J CarreroDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Johns Hopkins University · USKarolinska Institutet · SELeiden University Medical Center · NLTufts Medical Center · USBrigham and Women's Hospital · USHarvard University · USImpact · CA

Funding

Prevention and Treatment of Cardiovascular Disease in Patients with Chronic Kidney Disease: Patient-Oriented Research and Mentoring - RenewalK24HL155861 · NHLBI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Morgan Erika Grams · 2021 to 2026
$732k
Dutch Kidney Foundation 20OK016NIH HHS K24 HL155861NWOSwedish Heart and Lung FoundationSwedish Research Council #2019-01059Westman Foundation
6 · The paper itself

Abstract

backgroundWhile American nephrology societies recommend using the 2021 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) estimated glomerular filtration rate (eGFR) equation without a Black race coefficient, it is unknown how this would impact disease distribution, prognosis and kidney failure risk prediction in predominantly White non-US populations.

methodsWe studied 1.6 million Stockholm adults with serum/plasma creatinine measurements between 2007 and 2019. We calculated changes in eGFR and reclassification across KDIGO GFR categories when changing from the 2009 to 2021 CKD-EPI equation; estimated associations between eGFR and the clinical outcomes kidney failure with replacement therapy (KFRT), (cardiovascular) mortality and major adverse cardiovascular events using Cox regression; and investigated prognostic accuracy (discrimination and calibration) of both equations within the Kidney Failure Risk Equation.

resultsCompared with the 2009 equation, the 2021 equation yielded a higher eGFR by a median [interquartile range (IQR)] of 3.9 (2.9-4.8) mL/min/1.73 m2, which was larger at older age and for men. Consequently, 9.9% of the total population and 36.2% of the population with CKD G3a-G5 was reclassified to a higher eGFR category. Reclassified individuals exhibited a lower risk of KFRT, but higher risks of all-cause/cardiovascular death and major adverse cardiovascular events, compared with non-reclassified participants of similar eGFR. eGFR by both equations strongly predicted study outcomes, with equal discrimination and calibration for the Kidney Failure Risk Equation.

conclusionsImplementing the 2021 CKD-EPI equation in predominantly White European populations would raise eGFR by a modest amount (larger at older age and in men) and shift a major proportion of CKD patients to a higher eGFR category. eGFR by both equations strongly predicted outcomes.

Indexed as

Cardiovascular DiseasesRenal InsufficiencyRenal Insufficiency, ChronicAdultCreatinineGlomerular Filtration RateHumansMalePrognosisWhiteCreatinineCKD-EPIcreatinineepidemiologyglomerular filtration ratekidney diseases

Identifiers

PMID35689668
PMCPMC9869854
OpenAlexW4282931008

What OpenQuestion holds

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LicenceCC BY-NC
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.