Algorithmic Changes Are Not Enough: Evaluating the Removal of Race Adjustment from the eGFR Equation
Marika M. Cusick, Glenn M. Chertow, Douglas K. Owens, Michelle Y., Williams, Sherri Rose

TL;DR
Removing race adjustment from eGFR equations alone does not significantly change nephrology referral rates among Black patients, indicating that structural inequities require broader interventions beyond algorithm modifications.
Contribution
This study empirically evaluates the clinical impact of removing race adjustment from eGFR equations on healthcare disparities in CKD management.
Findings
No significant change in nephrology referral rates after removing race adjustment
Estimated referral rates remained nearly identical with or without race adjustment
Structural inequities persist beyond algorithmic changes in CKD care
Abstract
Changing clinical algorithms to remove race adjustment has been proposed and implemented for multiple health conditions. Removing race adjustment from estimated glomerular filtration rate (eGFR) equations may reduce disparities in chronic kidney disease (CKD), but has not been studied in clinical practice after implementation. Here, we assessed whether implementing an eGFR equation (CKD-EPI 2021) without adjustment for Black or African American race modified quarterly rates of nephrology referrals and visits within a single healthcare system, Stanford Health Care (SHC). Our cohort study analyzed 547,194 adult patients aged 21 and older who had at least one recorded serum creatinine or serum cystatin C between January 1, 2019 and September 1, 2023. During the study period, implementation of CKD-EPI 2021 did not modify rates of quarterly nephrology referrals in those documented as Black…
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Taxonomy
TopicsAdvanced Causal Inference Techniques · Radiomics and Machine Learning in Medical Imaging
