Racial Bias in eGFR Kidney Function Algorithm Delays Treatment for Black Patients

The estimated glomerular filtration rate (eGFR) equation, developed by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) in 2009 and its predecessor equations dating back to 1999, incorporated a race coefficient that increased calculated kidney function for Black patients by approximately 16%. This adjustment was based on assumptions about higher average muscle mass in Black individuals, which affects creatinine levels used in the calculation. The algorithm became standard practice across virtually all U.S. healthcare systems and was embedded in electronic health records and laboratory reporting systems. The racial adjustment had profound clinical consequences, as eGFR scores directly determine when patients qualify for kidney transplant waiting lists, specialist referrals, and certain medications. Black patients needed significantly worse actual kidney function before the algorithm would indicate severe disease, systematically delaying their access to life-saving interventions. Research published in 2020 and 2021 demonstrated that removing the race coefficient would have made an estimated 3 million Black Americans immediately eligible for earlier nephrology referrals and hundreds of thousands eligible for transplant listing. The bias was first systematically challenged in 2020 when nephrologists at the University of Washington and other institutions published studies showing the discriminatory impact. Dr. Vanessa Grubbs and colleagues demonstrated that the race-based equation perpetuated health disparities and was not scientifically justified. The National Kidney Foundation and American Society of Nephrology formed a joint task force to address the issue, leading to new race-neutral equations being recommended in 2021. Major healthcare systems including Vanderbilt University Medical Center, Beth Israel Deaconess Medical Center, and the University of California system began removing race from their eGFR calculations in 2021. However, implementation has been uneven, with some systems slower to adopt changes. The incident highlights how algorithmic bias can become entrenched in medical practice for decades, affecting millions of patients before being recognized and addressed. The financial impact on affected patients includes delayed treatment costs, progression to more expensive dialysis, and reduced quality of life from delayed transplants.