• Chi-Yuan Hsu, Wei Yang, Rishi V Parikh, Amanda H Anderson, Teresa K Chen, Debbie L Cohen, Jiang He, Madhumita J Mohanty, James P Lash, Katherine T Mills, Anthony N Muiru, Afshin Parsa, Milda R Saunders, Tariq Shafi, Raymond R Townsend, Sushrut S Waikar, Jianqiao Wang, Myles Wolf, Thida C Tan, Harold I Feldman, Alan S Go, and CRIC Study Investigators.
• From the Division of Nephrology, Department of Medicine (C.H., A.N.M., A.S.G.), and the Departments of Epidemiology and Biostatistics (A.S.G.), University of California, San Francisco, San Francisco, the Division of Research, Kaiser Permanente Northern California, Oakland (C.H., R.V.P., T.C.T., A.S.G.), the Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena (A.S.G.), and the Division of Nephrology, Department of Medicine, Stanford University School of Medicine, Palo Alto (A.S.G.) - all in California; the Department of Biostatistics, Epidemiology, and Informatics and the Center for Clinical Epidemiology and Biostatistics (W.Y., J.W., H.I.F.), the Division of Division of Renal-Electrolyte and Hypertension (D.L.C.), and the Department of Medicine (R.R.T.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia; the Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine (A.H.A., J.H., K.T.M.) and the Tulane University Translational Science Institute (A.H.A., J.H., K.T.M.), New Orleans; the Division of Nephrology, Department of Medicine, Johns Hopkins University School of Medicine, and the Welch Center for Prevention, Epidemiology, and Clinical Research, Baltimore (T.K.C.), and the Division of Kidney, Urologic, and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda (A.P.) - all in Maryland; the Division of Nephrology, Department of Internal Medicine, Wayne State University, Detroit (M.J.M.); the Division of Nephrology, Department of Medicine, University of Illinois at Chicago (J.P.L.), and the Section of General Internal Medicine, Department of Medicine, University of Chicago (M.R.S.) - both in Chicago; the Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson (T.S.); the Section of Nephrology, Boston University School of Medicine and Boston Medical Center, Boston (S.S.W.); and the Division of Nephrology, Department of Medicine, and Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC (M.W.).
• N. Engl. J. Med. 2021 Nov 4; 385 (19): 175017601750-1760.

BackgroundThe inclusion of race in equations to estimate the glomerular filtration rate (GFR) has become controversial. Alternative equations that can be used to achieve similar accuracy without the use of race are needed.MethodsIn a large national study involving adults with chronic kidney disease, we conducted cross-sectional analyses of baseline data from 1248 participants for whom data, including the following, had been collected: race as reported by the participant, genetic ancestry markers, and the serum creatinine, serum cystatin C, and 24-hour urinary creatinine levels.ResultsUsing current formulations of GFR estimating equations, we found that in participants who identified as Black, a model that omitted race resulted in more underestimation of the GFR (median difference between measured and estimated GFR, 3.99 ml per minute per 1.73 m2 of body-surface area; 95% confidence interval [CI], 2.17 to 5.62) and lower accuracy (percent of estimated GFR within 10% of measured GFR [P10], 31%; 95% CI, 24 to 39) than models that included race (median difference, 1.11 ml per minute per 1.73 m2; 95% CI, -0.29 to 2.54; P10, 42%; 95% CI, 34 to 50). The incorporation of genetic ancestry data instead of race resulted in similar estimates of the GFR (median difference, 1.33 ml per minute per 1.73 m2; 95% CI, -0.12 to 2.33; P10, 42%; 95% CI, 34 to 50). The inclusion of non-GFR determinants of the serum creatinine level (e.g., body-composition metrics and urinary excretion of creatinine) that differed according to race reported by the participants and genetic ancestry did not eliminate the misclassification introduced by removing race (or ancestry) from serum creatinine-based GFR estimating equations. In contrast, the incorporation of race or ancestry was not necessary to achieve similarly statistically unbiased (median difference, 0.33 ml per minute per 1.73 m2; 95% CI, -1.43 to 1.92) and accurate (P10, 41%; 95% CI, 34 to 49) estimates in Black participants when GFR was estimated with the use of cystatin C.ConclusionsThe use of the serum creatinine level to estimate the GFR without race (or genetic ancestry) introduced systematic misclassification that could not be eliminated even when numerous non-GFR determinants of the serum creatinine level were accounted for. The estimation of GFR with the use of cystatin C generated similar results while eliminating the negative consequences of the current race-based approaches. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases and others.).Copyright © 2021 Massachusetts Medical Society.

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