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Figure.  Difference Between Estimated and Measured Glomerular Filtration Rate (GFR) With and Without the African American (AA) Coefficient Across a Range of Estimated GFR Values
Difference Between Estimated and Measured Glomerular Filtration Rate (GFR) With and Without the African American (AA) Coefficient Across a Range of Estimated GFR Values

Data from 2601 AA participants from the Chronic Kidney Disease Epidemiology Collaboration development and internal validation sample were analyzed. Measured GFR (mGFR) values were obtained using urinary clearance of iothalamate. Estimated GFR based on serum creatinine (eGFRcr) values were computed using serum creatinine traceable to an international reference standard. We computed eGFRcr values with (gray) and without (orange) the application of the AA coefficient and removed values less than the 2.5 percentile and greater than the 97.5 percentile of these distributions, leaving 2463 participants for the analysis. Model plotted represents the generalized additive models for eGFRcr (mL/min/1.73 m2) on the difference between mGFR and eGFRcr (mL/min/1.73 m2) (excluding an additional 190 participants from the plot). The colored area along the lines represents 95% CIs of the estimate. The dashed line and darker orange shading indicate extrapolation of the model beyond the available data.

Table.  Performance of the 2009 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) Creatinine Equation With and Without Specification of African American (AA) Race and With and Without Height and Weight
Performance of the 2009 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) Creatinine Equation With and Without Specification of African American (AA) Race and With and Without Height and Weight
1.
Kidney Disease: Improving Global Outcomes (KDIGO).  KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease.   Kidney Int Suppl. 2013;3(1):1-150.Google ScholarCrossref
2.
Miller  WG, Jones  GRD.  Estimated glomerular filtration rate; laboratory implementation and current global status.   Adv Chronic Kidney Dis. 2018;25(1):7-13. doi:10.1053/j.ackd.2017.09.013PubMedGoogle ScholarCrossref
3.
Levey  AS, Stevens  LA, Schmid  CH,  et al; CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration).  A new equation to estimate glomerular filtration rate.   Ann Intern Med. 2009;150(9):604-612. doi:10.7326/0003-4819-150-9-200905050-00006PubMedGoogle ScholarCrossref
4.
Institute of Medicine.  Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care. National Academies Press; 2003.
5.
Eneanya  ND, Yang  W, Reese  PP.  Reconsidering the consequences of using race to estimate kidney function.   JAMA. 2019;322(2):113-114. doi:10.1001/jama.2019.5774PubMedGoogle ScholarCrossref
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    Research Letter
    March 16, 2020

    Estimation of Glomerular Filtration Rate With vs Without Including Patient Race

    Author Affiliations
    • 1Division of Nephrology, Tufts Medical Center, Boston, Massachusetts
    • 2Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts
    • 3Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
    JAMA Intern Med. 2020;180(5):793-795. doi:10.1001/jamainternmed.2020.0045

    Glomerular filtration rate (GFR) is critically important for determining drug dosing as well as prognosis and treatment in patients with kidney disease. Despite its importance, we rarely measure it directly. Instead, we use serum creatinine level to estimate GFR (eGFRcr). Because serum creatinine is determined by diet and muscle mass as well as GFR, we use age, sex, race (African American vs non–African American), height, or weight to adjust the estimation of GFR.1-3

    Using race in the equation to estimate GFR is problematic because race is a social rather than a biological construct.4 People self-define their race in different ways, and many people are of mixed race, making any single category flawed. We sought to compare estimated GFR with vs without including patient race in the analysis using a data set that had been previously used to develop the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation,3 the guideline-recommended GFR-estimating equation for adults, and to determine whether height and weight might substitute for race.

    Methods

    The CKD-EPI equation was developed using a pooled data set from 10 studies of people with and without chronic kidney disease, all of whom had measured GFR values using urinary clearance of iothalamate and serum creatinine traceable to an international reference standard.3 Race was classified as African American or other and assigned by the study participants or the investigator. Performance of the equation was evaluated using root mean square error (RMSE) and bias. RMSE was computed for the regression of measured GFR (mGFR) on eGFRcr on a logarithmic scale. Bias was computed as the median value for the difference between eGFRcr and mGFR (eGFRcr − mGFR). We compared median bias and RMSE between equations using Wilcoxon signed rank tests. Analyses were performed from May through June 2019 using SAS software version 9.4M6 (SAS Institute). The Tufts Health Sciences Institutional Review Board deemed the study exempt from review owing to the use of deidentified data.

    Results

    Among the 8254 participants in the development data set, 2601 (31.5%) were African American, 3606 (43.6%) were women, and the mean (SD) age and mGFR were 47 (15) years and 68 (40) mL/min/1.73 m2, respectively. As shown in the Figure, eliminating the race coefficient in the CKD-EPI equation was associated with a systematic error in the evaluation of African American individuals, an underestimation of mGFR throughout the range of the eGFRcr values. As shown in the Table, a new equation without race was associated with worse performance, more so in African American individuals than in non–African American individuals (equation 2). Inclusion of height and weight in addition to race did not meaningfully decrease the association of race with eGFRcr (coefficient, 1.15 vs 1.16) nor meaningfully improve performance (equation 3). Even when height and weight were included, eliminating race was associated with worse equation performance, more so in African American individuals than in non–African American individuals (equation 4).

    Discussion

    The study results show that a strategy of eliminating the African American coefficient from the CKD-EPI equation was associated with a systematic bias toward underestimation of mGFR in African American individuals, which was not overcome by substituting height and weight. In particular, the equation with the African American coefficient was much more accurate at eGFRcr values less than approximately 75 mL/min/1.73 m2. We are concerned that the strategy of eliminating race from the equation as suggested by Eneanya et al5 may have unintended consequences in African American individuals, such as inappropriate early transplant or dialysis initiation, overdiagnosis of CKD, overestimation of the association of the risk of adverse outcomes with reduced GFR, inadequate dosing of drugs excreted by glomerular filtration (eg, some antibiotics and cancer chemotherapy), and limited access to tests (eg, some imaging procedures) and treatments that require a higher level of GFR (eg, metformin, sodium-glucose cotransporter 2 inhibitors, bisphosphonates), including living kidney donation. Better methods are needed to improve the accuracy of GFR assessment without requiring specification of race.

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    Article Information

    Accepted for Publication: January 4, 2020.

    Corresponding Author: Andrew S. Levey, MD, Division of Nephrology, Tufts Medical Center, 800 Washington St, PO Box 391, Boston, MA 02111 (alevey@tuftsmedicalcenter.org).

    Published Online: March 16, 2020. doi:10.1001/jamainternmed.2020.0045

    Author Contributions: Drs Tighiouart and Inker 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.

    Study concept and design: Levey, Inker.

    Acquisition, analysis, or interpretation of data: Levey, Tighiouart, Titan.

    Drafting of the manuscript: Levey.

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

    Statistical analysis: Tighiouart, Titan, Inker.

    Obtained funding: Levey.

    Administrative, technical, or material support: Inker.

    Study supervision: Levey.

    Conflict of Interest Disclosures: Dr Levey reported receiving grants and contracts from the National Institutes of Health and the National Kidney Foundation to Tufts Medical Center and a clinical trial contract with AstraZeneca outside the submitted work. Dr Inker reported receiving funding from the National Institutes of Health, the National Kidney Foundation, Retrophin, Omeros, Dialysis Clinic, Inc, and Reata Pharmaceuticals for research and contracts to Tufts Medical Center and having consulting agreements with Tricida and Omeros outside the submitted work. No other disclosures were reported.

    Funding/Support: The development and validation of the Chronic Kidney Disease Epidemiology Collaboration was supported by grants UO1 DK 053869, UO1 DK 067651, and UO1 DK 35073 from the National Institute of Diabetes and Digestive and Kidney Diseases as part of a cooperative agreement.

    Role of the Funder/Sponsor: The funder 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 acknowledge the assistance of Juhi Chaudhari, MPH, Division of Nephrology, Tufts Medical Center, Boston, Massachusetts, in manuscript preparation. She was not compensated for this contribution.

    References
    1.
    Kidney Disease: Improving Global Outcomes (KDIGO).  KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease.   Kidney Int Suppl. 2013;3(1):1-150.Google ScholarCrossref
    2.
    Miller  WG, Jones  GRD.  Estimated glomerular filtration rate; laboratory implementation and current global status.   Adv Chronic Kidney Dis. 2018;25(1):7-13. doi:10.1053/j.ackd.2017.09.013PubMedGoogle ScholarCrossref
    3.
    Levey  AS, Stevens  LA, Schmid  CH,  et al; CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration).  A new equation to estimate glomerular filtration rate.   Ann Intern Med. 2009;150(9):604-612. doi:10.7326/0003-4819-150-9-200905050-00006PubMedGoogle ScholarCrossref
    4.
    Institute of Medicine.  Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care. National Academies Press; 2003.
    5.
    Eneanya  ND, Yang  W, Reese  PP.  Reconsidering the consequences of using race to estimate kidney function.   JAMA. 2019;322(2):113-114. doi:10.1001/jama.2019.5774PubMedGoogle ScholarCrossref
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