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Figure.  Changes in Reported eGFR for Black Adults Following Removal of Race From eGFRcr
Changes in Reported eGFR for Black Adults Following Removal of Race From eGFRcr

All Black adults would experience a decrease in reported estimated glomerular filtration rate (eGFR) of 13.7% (computed from the Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI]) race coefficient as 1 − 1/1.159). Median eGFR was 102.9 mL/min/1.73 m2 with race and 88.8 mL/min/1.73 m2 without race; the median eGFR change was −14.1 mL/min/1.73 m2. The 25th percentile for the change in eGFR was −16.5 mL/min/1.73 m2, and the 75th percentile for the change in eGFR was −11.5 mL/min/1.73 m2. Data are from the 2001-2018 National Health and Nutrition Examination Survey (NHANES). eGFRcr indicates estimated glomerular filtration rate from serum creatinine.

Table.  Potential Implications for Black Adults in the US Following Removal of Race From eGFRcra
Potential Implications for Black Adults in the US Following Removal of Race From eGFRcra
1.
Eneanya  ND, Yang  W, Reese  PP.  Reconsidering the consequences of using race to estimate kidney function.   JAMA. 2019;322(2):113-114.PubMedGoogle ScholarCrossref
2.
Vyas  DA, Eisenstein  LG, Jones  DS.  Hidden in plain sight—reconsidering the use of race correction in clinical algorithms.   N Engl J Med. 2020;383(9):874-882.PubMedGoogle ScholarCrossref
3.
Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group.  KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease: summary of recommendation statements.   Kidney Int Suppl (2011). 2013;3(1):5-14. doi:10.1038/kisup.2012.77PubMedGoogle ScholarCrossref
4.
Code of Federal Regulations. Supplementary Medical Insurance (SMI) Benefits. 42 CFR §410. Accessed October 6, 2020. https://www.law.cornell.edu/cfr/text/42/part-410
5.
Levey  AS, Titan  SM, Powe  NR, Coresh  J, Inker  LA.  Kidney disease, race, and GFR estimation.   Clin J Am Soc Nephrol. 2020;15(8):1203-1212. doi:10.2215/CJN.12791019PubMedGoogle ScholarCrossref
6.
Sehgal  AR.  Race and the false precision of glomerular filtration rate estimates.   Ann Intern Med. 2020;173(12):1008-1009. doi:10.7326/M20-4951PubMedGoogle ScholarCrossref
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    Research Letter
    December 2, 2020

    Clinical Implications of Removing Race From Estimates of Kidney Function

    Author Affiliations
    • 1Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
    • 2Cardiovascular Research Institute, Morehouse Medical School, Atlanta, Georgia
    • 3Division of Renal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
    • 4Department of Medicine, Zuckerberg San Francisco General Hospital, San Francisco, California
    • 5Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
    JAMA. 2021;325(2):184-186. doi:10.1001/jama.2020.22124

    Over the past year, medical centers across the US have removed race adjustment from estimated glomerular filtration rate from serum creatinine (eGFRcr), with many now reporting the “White/other” value for all patients. These changes follow calls to reconsider the use of race in estimating kidney function1 and in medicine broadly.2 We analyzed potential changes in recommended care using eGFRcr with and without race among Black individuals in the US (individuals who are not Black would not be affected).

    Methods

    We used data from the National Health and Nutrition Examination Survey (NHANES) characterizing a cross-sectional sample of the noninstitutionalized US population from 2001 to 2018, with response rates from 48.8% to 79.6%. Laboratory measurements, including serum creatinine, were collected in mobile examination centers, and demographic variables, including race/ethnicity, were self-reported in personal interviews. Participants provided written consent using a protocol approved by the National Center for Health Statistics Research Ethics Review Board.

    We computed eGFRcr using the Chronic Kidney Disease (CKD) Epidemiology Collaboration (CKD-EPI) equation3 with and without its race coefficient for Black individuals. We then estimated the number of Black adults whose care could change as recommended by Kidney Disease: Improving Global Outcomes (KDIGO)3 and the Medicare Part B benefit policy.4 Evaluated changes include CKD diagnoses, CKD stage reclassifications and related drug recommendations, nephrologist referrals, Medicare coverage, kidney donation, and kidney transplantation. Outcomes are defined in the eAppendix in the Supplement. Analyses were performed using R version 4.0.0.

    Results

    The study cohort comprised 9522 nonpregnant, non-Hispanic Black adults; 50.5% self-identified as women and the median age was 45 years. After removal of race, median eGFR decreased from 102.9 mL/min/1.73 m2 to 88.8 mL/min/1.73 m2; the median change was −14.1 mL/min/1.73 m2 (Figure).

    Removing race may increase the prevalence of CKD among Black adults from 14.9% to 18.4% (difference, 3.5% [95% CI, 3.2%-3.9%]) (Table). Concurrently, 29.1% (95% CI, 26.4%-32.0%) of Black adults with existing CKD may be reclassified to more severe stages of disease, with significant clinical and pharmacologic implications. Affected drugs include angiotensin-converting enzyme inhibitors, β-blockers, warfarin, cisplatin, metformin, and sodium-glucose cotransporter-2 inhibitors. The prevalence of CKD stage 3b or higher may change from 2.3% to 3.5%, affecting 1.2% (95% CI, 1.0%-1.5%) of Black adults. Similarly, the prevalence of CKD stage 4 or higher may change from 1.0% to 1.3%, affecting 0.29% (95% CI, 0.18%-0.43%) of Black adults.

    Following race removal, 3.4% of Black adults may be referred to specialist care instead of 3.2% (difference, 0.22% [95% CI, 0.13%-0.35%]). Medicare would cover 5.5% of Black adults for medical nutrition therapy instead of 5.0% (difference, 0.47% [95% CI, 0.37%-0.60%]). Similarly, 0.36% of Black adults would be covered for kidney disease education instead of 0.22% (difference, 0.14% [95% CI, 0.07%-0.25%]).

    Computing eGFRcr without race may raise the proportion of Black adults eligible for the kidney transplant wait list from 0.66% to 0.71%, newly qualifying 0.051% (95% CI, 0.02%-0.10%) of Black adults for transplantation. Conversely, the proportion of kidney donor candidates deemed “not acceptable” would change from 38.5% to 40.6%, newly disqualifying 2.1% (95% CI, 1.8%-2.4%) of Black adults from kidney donation.

    Discussion

    Removal of race adjustment may increase CKD diagnoses among Black adults and enhance access to specialist care, medical nutrition therapy, kidney disease education, and kidney transplantation, while potentially excluding kidney donors and prompting drug contraindications or dose reductions for individuals reclassified to advanced stages of CKD. This potential for benefits and harms must be interpreted in light of persistent disparities in care,3 documented biases of eGFRcr without race,5 and the historical misuse of race as a biological variable to further racism.2

    This study had several limitations. First, many institutions use the Modification of Diet in Renal Disease (MDRD) equation. Removal of the larger race coefficient in the MDRD (1.212 vs 1.159 in CKD-EPI) would lead to larger decreases in eGFR and more individuals crossing relevant thresholds. Second, some institutions have removed race using methods other than universalizing the “White/other” equation. Third, eGFR does not determine care for all patients. Clinical judgment,6 unbiased confirmatory tests to corroborate eGFRcr, and varying adherence to guidelines may all influence how changes materialize.

    Section Editor: Jody W. Zylke, MD, Deputy Editor.
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    Article Information

    Corresponding Author: Arjun K. Manrai, PhD, Computational Health Informatics Program, Boston Children’s Hospital, Department of Biomedical Informatics, Harvard Medical School, 401 Park Dr, Boston, MA 02115 (arjun_manrai@hms.harvard.edu).

    Accepted for Publication: October 22, 2020.

    Published Online: December 2, 2020. doi:10.1001/jama.2020.22124

    Author Contributions: Mr Diao and Dr Manrai had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Diao, Wu, Taylor, Powe, Manrai.

    Acquisition, analysis, or interpretation of data: Diao, Wu, Tucker, Powe, Kohane, Manrai.

    Drafting of the manuscript: Diao, Powe, Manrai.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Diao, Wu, Powe, Manrai.

    Obtained funding: Manrai.

    Supervision: Kohane, Manrai.

    Conflict of Interest Disclosures: Dr Taylor reported receipt of personal fees for consulting services from Novartis, Pfizer, and UnitedHealth Group. No other disclosures were reported.

    Funding/Support: Dr Manrai was supported by National Institutes of Health (NIH) award 5K01HL138259.

    Role of the Funder/Sponsor: The NIH had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Additional Contributions: We thank Andrew S. Levey, MD, and Lesley Inker, MD, MPH, Division of Nephrology, Tufts Medical Center, and Jason K. Wang, MA, Computational Health Informatics Program, Boston Children’s Hospital, for helpful comments on the manuscript. None received compensation for their contributions.

    References
    1.
    Eneanya  ND, Yang  W, Reese  PP.  Reconsidering the consequences of using race to estimate kidney function.   JAMA. 2019;322(2):113-114.PubMedGoogle ScholarCrossref
    2.
    Vyas  DA, Eisenstein  LG, Jones  DS.  Hidden in plain sight—reconsidering the use of race correction in clinical algorithms.   N Engl J Med. 2020;383(9):874-882.PubMedGoogle ScholarCrossref
    3.
    Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group.  KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease: summary of recommendation statements.   Kidney Int Suppl (2011). 2013;3(1):5-14. doi:10.1038/kisup.2012.77PubMedGoogle ScholarCrossref
    4.
    Code of Federal Regulations. Supplementary Medical Insurance (SMI) Benefits. 42 CFR §410. Accessed October 6, 2020. https://www.law.cornell.edu/cfr/text/42/part-410
    5.
    Levey  AS, Titan  SM, Powe  NR, Coresh  J, Inker  LA.  Kidney disease, race, and GFR estimation.   Clin J Am Soc Nephrol. 2020;15(8):1203-1212. doi:10.2215/CJN.12791019PubMedGoogle ScholarCrossref
    6.
    Sehgal  AR.  Race and the false precision of glomerular filtration rate estimates.   Ann Intern Med. 2020;173(12):1008-1009. doi:10.7326/M20-4951PubMedGoogle ScholarCrossref
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