FC078: Impact of Removing Race from the CKD-EPI Equation: Analysis of 1.6 Million Swedish Adults

Abstract

Abstract BACKGROUND AND AIMS The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) recently developed a novel creatinine-based eGFR equation without a race coefficient. While American nephrology societies recommend using this novel equation, its implications are unknown. METHOD We included 1.6 million adult individuals with routine outpatient serum creatinine testing during 2007–2018 in Stockholm, Sweden. First, we calculated reclassification across KDIGO eGFR categories when changing from the 2009 to 2021 CKD-EPI equation. Second, for both equations, the association between eGFR and (1) kidney failure with replacement therapy (KFRT), (2) all-cause mortality, (3) cardiovascular mortality and (4) major adverse cardiovascular events was estimated with Cox regression. Third, prognostic accuracy of both eGFR equations within the Kidney Failure Risk Equation was assessed with discrimination and calibration. RESULTS Compared with the 2009 equation, the 2021 equation yielded a higher eGFR by a median (IQR) of 3.9 (2.9–4.8) mL/min/1.73 m2, decreasing prevalence of CKD G3–G5 from 5.1 to 3.8%. The 2021 equation reclassified 9.9% of the total population and 36.2% of the CKD G3–G5 population to a less severe eGFR category. Individuals who were reclassified to less severe eGFR categories were older and therefore exhibited higher crude risks of all-cause/cardiovascular death and major adverse cardiovascular events, and lower risk of kidney replacement therapy compared with nonreclassified participants of similar eGFR. eGFR by both equations strongly predicted study outcomes, with similar discrimination and calibration for the Kidney Failure Risk Equation. CONCLUSION Implementing the 2021 CKD-EPI equation in predominantly white European populations raises eGFR by a modest amount (larger at older age and men) and shifts a major proportion of CKD patients to a higher eGFR category, with eGFR by both equations strongly predicting outcomes.