Evidence map›Paper›PMID 39746332›Full record

ArticleAmerican journal of nephrology2025

Performance of New Race-Free eGFR Equations for Predicting Complications in Chronic Kidney Disease: From the KNOW-CKD Study.

Jaehee Koh, Jihyun Yang, Kyu-Beck Lee, Jayoun Kim, Jong Cheol Jeong, Yaeni Kim, Tae-Hyun Yoo, Kook-Hwan Oh, Young Youl Hyun

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Article in American journal of nephrology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

9 authors.

Jaehee KohWang Internal Medicine Clinic, Gwangmyeong, Republic of Korea.
Jihyun YangDivision of Nephrology, Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Kyu-Beck LeeDivision of Nephrology, Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Jayoun KimMedical Research Collaborating Center, Seoul National University Hospital, Seoul, Republic of Korea.
Jong Cheol JeongDepartment of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Yaeni KimDepartment of Internal Medicine, Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Tae-Hyun YooDivision of Nephrology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
Kook-Hwan OhDepartment of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Young Youl HyunDivision of Nephrology, Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe National Kidney Foundation (NKF) and the American Society of Nephrology (ASN) developed new race-free eGFR equations and recommended using these new equations in 2021. However, clinical implication of these new equations is not determined in Korean adults. Thus, this study aimed to evaluate performances of these new race-free eGFR equations in predicting complications in Korean chronic kidney disease (CKD) patients.

methodsThis study analyzed 1,727 participants from the KNOW-CKD cohort. We selected anemia, hyperkalemia, acidosis, hyperphosphatemia, and hyperparathyroidism as five complications of CKD. We determined cross-sectional associations between complications and four eGFR equations. These eGFRs were calculated from 2009 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) creatinine equation (2009 eGFRCr), 2012 CKD-EPI Creatinine-Cystatin C equation (2012 eGFRCrCys), 2021 CKD-EPI Creatinine equation (2021 eGFRCr), and 2021 CKD-EPI Creatinine-Cystatin C equation (2021 eGFRCrCys).

resultsAll associations between complications as continuous variables and eGFR by four equations were similar. All associations between complications as dichotomous variable and eGFR values form four equations were similar. For example, C-statistics (95% confidence interval) of the logistic model for anemia and eGFRs were 0.826 (0.806-0.845), 0.827 (0.806-0.846), 0.838 (0.819-0.857), and 0.839 (0.820-0.858) for 2009 eGFRCr, 2012 eGFRCrCys, 2021 eGFRCr, and 2021 eGFRCrCys, respectively. In addition, cross-validated areas under the curve for ROC analysis after predictive modeling for all complications were not significant different according to different eGFR equations.

conclusionNew race-free eGFR equations showed similar performances to existing equations for predicting complications in Korean patients with CKD.

Indexed as

Glomerular Filtration RateRenal Insufficiency, ChronicAcidosisAdultAgedAnemiaCreatinineCross-Sectional StudiesFemaleHumansHyperkalemiaHyperphosphatemiaMaleMiddle AgedPredictive Value of TestsRepublic of KoreaCreatinineComplicationEstimating glomerular filtration rateRace-free

Identifiers

PMID39746332
PMCPMC12169797

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