Visual Abstract
Keywords: kidney transplantation, disparity
Abstract
Background and objectives
Wait time for kidney transplantation can accrue when GFR is ≤20 ml/min. We examined whether using the race-free 2021 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations to guide preemptive waitlisting could attenuate racial differences in accruable preemptive wait time.
Design, setting, participants, & measurements
Our retrospective cohort study included Black or White Chronic Renal Insufficiency Cohort (CRIC) participants who were theoretically eligible for waitlist registration. We used Weibull accelerated failure time models to determine the association between race (Black or White) and time to kidney failure from the qualifying visit when the eGFR by creatinine or creatinine-cystatin C 2021 CKD-EPI equations fell to ≤20 ml/min per 1.73 m2. We then tested for differences in the time ratios from models using the 2021 creatinine- or creatinine-cystatin C–based CKD-EPI equation through a bootstrapping approach.
Results
By the creatinine equation, 472 CRIC participants were theoretically eligible for waitlist registration, and potential preemptive wait time was similar for Black versus White participants (time ratio, 1.05; 95% confidence interval, 0.81 to 1.35). The median wait time by the creatinine equation that could be accrued for Black participants was 23 versus 22 months in White participants. By the creatinine-cystatin C equation, 441 CRIC participants were eligible, and potential wait time was 20% shorter (95% confidence interval, 0.62 to 1.02) for Black than White participants. The median wait time that could be accrued for Black participants was 21 versus 26 months for White participants when using the creatinine-cystatin C equation. Using bootstrapping, the ratio of the time ratio of the models using the creatinine versus creatinine-cystatin C equation was statistically significantly different (ratio of the time ratios = 1.31 with 95% confidence interval, 1.06 to 1.62).
Conclusions
Use of the 2021 creatinine-based CKD-EPI equation to determine preemptive waitlist eligibility reduced racial differences in preemptive wait time accrual more than use of the creatinine-cystatin C 2021 CKD-EPI equation within a theoretical context.
Introduction
GFR is the primary criterion for determining eligibility for registration on the kidney transplant waitlist in patients not yet treated with dialysis (1,2). According to current national policy, patients can begin to accrue wait time for transplantation when their GFR is ≤20 ml/min. Previously, GFR was commonly determined using creatinine-based estimating equations that adjust kidney function upward by 16% for Black individuals but not individuals of other racial or ethnic groups (3). Because race is a social rather than a biologic construct (4,5), there has been concern that the incorporation of a race term into equations that estimate GFR may introduce bias (2,4,5). Furthermore, because the Organ Procurement and Transplantation Network (OPTN) did not previously specify which GFR equation should be used when assessing eligibility for waitlist registration, understanding whether new equations to estimate GFR attenuate racial differences is important, especially because measuring GFR (which is considered the gold standard metric by many) is cumbersome, costly, and not always readily available in all practices (6).
Our objective in this study was to determine whether using the 2021 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations (7) (which are recommended for use by the National Kidney Foundation and the American Society of Nephrology and do not include race terms) (4,8) to estimate GFR and eligibility for preemptive waitlisting would associate with racial differences in access to preemptive wait time accrual in the Chronic Renal Insufficiency Cohort (CRIC) study within a theoretical modeled context.
Materials and Methods
Study Population
The CRIC study is a multicenter study of adults with CKD with eGFR of 20–70 ml/min per 1.73 m2 at enrollment. The details of this study have been previously described, and participants are followed annually for research visits (1,9,10). In this study, we included non-Hispanic White (henceforth referred to as White) and non-Hispanic Black (henceforth referred to as Black) CRIC participants given our focus on racial differences in preemptive waitlist eligibility that were noted in our prior study (1,11). We excluded visits in which a participant was ≥75 years of age given the lower likelihood that such individuals would be deemed eligible for kidney transplantation and registered on the waitlist.
We constructed two theoretical cohorts of individuals who were potentially eligible for preemptive wait time accrual using either the 2021 CKD-EPI creatinine (eGFRcr) equation or the 2021 CKD-EPI combined creatinine-cystatin C (eGFRcr-cys) equation to estimate GFR (7). We chose to use these two CKD-EPI equations on the basis of recommendations from the joint National Kidney Foundation and American Society of Nephrology Task Force on Reassessing the Inclusion of Race in Diagnosing Kidney Disease (8). We required participants to have at least one eGFR <25 ml/min per 1.73 m2 and a subsequent eGFR ≤20 ml/min per 1.73 m2 at any point during their participation in the CRIC study using the 2021 CKD-EPI equations. Requiring a downward eGFR trajectory for inclusion in our analysis was designed to ensure that participants had progressive loss of kidney function and would reasonably be considered for preemptive waitlisting on the basis of their declining eGFR. Because participants in this study were selected for inclusion on the basis of the 2021 CKD-EPI equations, the two cohorts in this study differed from the cohorts included in our prior work (1).
We used deidentified CRIC data from the National Institute of Diabetes and Digestive and Kidney Diseases Central Repository. The University of California, San Francisco Institutional Review Board considers this study exempt human subjects research. Analyses were conducted using Stata 16 (Stata Corp, College Station, TX).
Statistical Analyses
We examined the characteristics of participants included in each of the two cohorts at the time of entry into our study by race using chi-squared and Kruskal–Wallis tests as appropriate.
We then used Weibull accelerated failure time and cause-specific hazards models in unadjusted analyses as our primary models. Deaths in these models were treated as censoring events. CRIC participants were followed at annual visits and censored in our study if they were lost to follow-up.
The primary exposure was non-Hispanic Black or White race by self-report. The outcome was time to onset of the need for KRT (dialysis or transplant) in months starting from the CRIC visit when the eGFR fell to ≤20 ml/min per 1.73 m2 if the participant qualified for entry into our study. Of note, Weibull accelerated failure time models yield differences between groups in the amount of time to outcomes rather than hazard ratios (HRs), such that a time ratio of less than one when comparing Black individuals with White individuals would mean shorter time for Black individuals until onset of KRT. Dates of initiation of KRT and death were ascertained as previously described (1).
We considered our primary models to be unadjusted as only GFR is considered by OPTN for eligibility for waitlist registration, and shorter time between eGFR ≤20 ml/min and KRT leads to shorter potential wait time accrual, regardless of the mechanism. However, in sensitivity analyses, we also adjusted our analyses for age and sex (minimally adjusted models), and additionally, for sociodemographic and clinical characteristics such as diabetes, proteinuria, peripheral arterial and heart disease, and use of renin-angiotensin-aldosterone system blockers (fully adjusted models). Missing covariates at the qualifying visit were carried forward from the prior visits.
In additional analyses, we also examined the risk of KRT using Fine–Gray models and treated death as a competing risk in unadjusted and fully adjusted models.
Finally, we used nonparametric bootstrapping with 1000 repetitions to test the ratio of the time ratios and the HRs derived from the Weibull and Cox analyses using the 2021 CKD-EPI equations to compute eGFRcr and eGFRcr-cys. This nonparametric analysis was designed to compare whether there were differences in the theoretical access to waitlisting of Black versus White CRIC participants using the 2021 creatinine-based equation versus the 2021 creatinine-cystatin C–based equation.
Results
When we used the 2021 creatinine-based CKD-EPI equation to identify individuals who were theoretically eligible for waitlist registration before the onset of KRT on the basis of their eGFR, we found that 472 participants met our inclusion criteria. The median age of participants was 62 years. The median eGFR was 17.6 ml/min per 1.73 m2 for White individuals and 16.1 ml/min per 1.73 m2 for Black individuals at study entry (Table 1). The lower eGFR among Black patients likely reflects faster progression of CKD after an observed eGFR of 25 ml/min per 1.73 m2 and mimics what would occur in clinical practice (i.e., lower eGFR at the time of recognition of transplant eligibility). The median urine protein-creatinine ratio was similar for Black and White participants (Table 1).
Table 1.
Characteristics of the Chronic Renal Insufficiency Cohort at the time when participants were observed to have an eGFR ≤20 ml/min per 1.73 m2 by the race-free 2021 Chronic Kidney Disease Epidemiology Collaboration equations
| N (%) unless Otherwise Specified | eGFR by Creatinine Equation as Inclusion Criteria | eGFR by Creatinine-Cystatin C Equation as Inclusion Criteria | ||||
|---|---|---|---|---|---|---|
| Overall, n=472 | White, n=122 | Black, n=350 | Overall, n=441 | White, n=152 | Black, n=289 | |
| Median age [IQR], yr | 62 [54–68] | 63 [55–68] | 62 [54–68] | 62 [55–68] | 64 [57–69] | 62 [54–67] |
| Women | 229 (49) | 48 (39) | 181 (52) | 221 (50) | 66 (43) | 155 (54) |
| Median eGFR by CKD-EPI [IQR], ml/min per 1.73 m2 | 16.5 [13.6–18.6] | 17.6 [14.9–18.8] | 16.1 [13.0–18.3] | 16.9 [14.4–18.5] | 17.7 [15.4–18.8] | 16.4 [14.0–18.3] |
| Median urine protein-creatinine ratio [IQR], g/g | 1.24 [0.42–2.90] | 1.24 [0.42–2.84] | 1.23 [0.42–3.03] | 1.20 [0.41–2.87] | 1.07 [0.32–2.63] | 1.32 [0.45–2.95] |
| Median urine albumin-creatinine ratio [IQR], mg/g | 618 [170–1596] | 608 [162–1431] | 620 [174–1676] | 609 [158–1572] | 538 [117–1436] | 652 [190–1642] |
| Diabetes | 287 (61) | 66 (54) | 221 (63) | 276 (63) | 88 (58) | 188 (65) |
| Heart failure | 76 (16) | 10 (8) | 66 (19) | 81 (18) | 20 (13) | 61 (21) |
| Myocardial infarction | 128 (27) | 31 (25) | 97 (28) | 132 (30) | 49 (32) | 83 (29) |
| Peripheral vascular disease | 46 (10) | 10 (8) | 36 (10) | 51 (12) | 21 (14) | 30 (10) |
IQR, interquartile range; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration.
When we examined the time available between eGFRcr ≤20 ml/min per 1.73 m2 using the 2021 CKD-EPI creatinine-based equation and the onset of KRT, we found no statistically significant differences when we compared Black (median time of 23 months) versus White (median time of 22 months) (Table 2) participants using either unadjusted Weibull (time ratio, 1.05; 95% confidence interval [95% CI], 0.81 to 1.35) or Cox models (Table 3). Even with adjustment for additional covariates that are known to influence the risk for progression of CKD, findings remained similar in fully adjusted models (Table 3). When we treated death as a competing risk using unadjusted Fine–Gray models, findings were also similar (sub-HR, 1.02; 95% CI, 0.79 to 1.31 comparing Black versus White participants). Results were similar in fully adjusted Fine–Gray models (sub-HR, 1.04; 95% CI, 0.78 to 1.38).
Table 2.
Outcomes according to self-reported race and different Chronic Kidney Disease Epidemiology Collaboration estimating equations
| N (%) Unless Otherwise Specified | Overall | White | Black |
|---|---|---|---|
| By eGFRcr as inclusion criteria | |||
| n | 472 | 122 | 350 |
| Kidney failure | 329 (70) | 79 (65) | 250 (71) |
| Death | 37 (8) | 10 (8) | 27 (8) |
| Months between eGFR ≤20 ml/min per 1.73 m2 and kidney failure, median [IQR] | 23 [9–46] | 22 [9–44] | 23 [10–46] |
| By eGFRcr_cys as inclusion criteria | |||
| n | 441 | 152 | 289 |
| Kidney failure | 312 (71) | 98 (65) | 214 (74) |
| Death | 45 (10) | 17 (11) | 28 (10) |
| Months between eGFR ≤20 ml/min per 1.73 m2 and kidney failure, median [IQR] | 23 [9–46] | 26 [10–53] | 21 [8–43] |
eGFRcr, Chronic Kidney Disease Epidemiology Collaboration creatinine equation; IQR, interquartile range; eGFRcr_cys, Chronic Kidney Disease Epidemiology Collaboration combined creatinine-cystatin C equation.
Table 3.
Comparison of time between eGFR of 20 ml/min per 1.73 m2 and the onset of kidney failure
| Entry into the Cohort When eGFR ≤20 ml/min per 1.73 m2 until Kidney Failure | Weibull Model Time Ratio (95% Confidence Interval) | Cox Model Hazard Ratio (95% Confidence Interval) |
|---|---|---|
| Unadjusted model a | ||
| eGFRcr | 1.05 (0.81 to 1.35) | 0.95 (0.74 to 1.22) |
| eGFRcr-cys | 0.80 (0.62 to 1.02) | 1.22 (0.96 to 1.56) |
| Minimally adjusted model for age and sex | ||
| eGFRcr | 1.08 (0.84 to 1.39) | 0.92 (0.72 to 1.19) |
| eGFRcr-cys | 0.83 (0.65 to 1.07) | 1.19 (0.93 to 1.51) |
| Fully adjusted model all covariates b | ||
| eGFRcrc | 1.06 (0.85 to 1.33) | 0.93 (0.70 to 1.22) |
| eGFRcr-cysd | 0.84 (0.68 to 1.05) | 1.22 (0.94 to 1.60) |
eGFRcr, Chronic Kidney Disease Epidemiology Collaboration 2021 creatinine equation; eGFRcr_cys, Chronic Kidney Disease Epidemiology Collaboration 2021 combined creatinine-cystatin C equation.
Time and hazard ratios compare the time or risk between eGFR of 20 ml/min per 173 m2 and the onset of kidney failure in Black participants compared with White participants (reference group).
Adjusted for age, sex, income, education, insurance, logarithm protein-creatinine ratio, presence or absence of diabetes, peripheral vascular disease, myocardial infarction, heart failure, angiotensin-converting enzyme inhibitor, and angiotensin-receptor blocker use at the visit when patients entered the study.
There were two patients who had a missing urine protein-creatinine ratio in adjusted analysis.
There was one patient with a missing urine protein-creatinine ratio in adjusted analysis.
When we used the 2021 creatinine-cystatin C CKD-EPI equation to identify individuals who were theoretically eligible for accumulation of time on the waitlist before the onset of KRT, we found that 441 participants were eligible for inclusion in our analyses (which differs from the cohort above because fewer participants met our eGFR-based inclusion criteria when using the creatinine-cystatin C CKD-EPI equation). The median age was 62 years, and the median eGFR was 17.7 ml/min per 1.73 m2 among White participants and 16.4 ml/min per 1.73 m2 among Black participants (Table 1). The urine protein-creatinine ratio was similar for Black and White participants. The number of Black individuals who were eligible for inclusion was smaller using eGFRcr-cys than for eGFRcr.
When we examined time available between eGFRcr-cys ≤20 ml/min per 1.73 m2 using the 2021 CKD-EPI creatinine-cystatin C–based equation and the onset of the need for KRT, we also found no statistically significant differences in the time available for Black (median time of 21 months) versus White (median time of 26 months) (Table 2) participants in unadjusted Weibull (time ratio, 0.80; 95% CI, 0.62 to 1.02) and Cox models (Table 3). Even with adjustment for additional covariates that may influence the risk for progression of CKD, findings remained consistent in fully adjusted models (Table 3). When we treated death as a competing risk using unadjusted Fine–Gray models, the risk for progression to KRT was also not statistically significantly different when comparing Black versus White individuals (sub-HR, 1.24; 95% CI, 0.98 to 1.57 comparing Black versus White participants). In fully adjusted Fine–Gray models, the risk for progression to KRT was not statistically significantly different comparing Black versus White participants (sub-HR, 1.28; 95% CI, 0.97 to 1.69).
When we tested for differences in the time and HRs for the onset of KRT in our models on the basis of the eGFRcr and eGFRcr-cys equations, we found that the theoretical preemptive wait time that could be accrued differed more between Black and White CRIC participants when using the 2021 CKD-EPI eGFRcr-cys equation (21 versus 26 months; 5 months longer for White participants) than when using the 2021 CKD-EPI eGFRcr equation (23 versus 22 months; 1 month longer for Black participants). When using bootstrapping to test the statistical significance of differences between the time ratios or the HRs of models comparing the eGFRcr versus eGFRcr-cys in Weibull or Cox models, we found that these differences were statistically significant (ratio of the time ratios, 1.31; 95% CI, 1.06 to 1.62; P=0.01; ratio of the HRs, 0.78; 95% CI, 0.63 to 0.95; P=0.02).
Discussion
We previously demonstrated that using the 2012 CKD-EPI equations to estimate GFR by either the creatinine- or cystatin C–based equations led to shorter preemptive wait time that could theoretically be available before the onset of KRT for Black compared with White participants in the CRIC study (1). We now demonstrate that use of the 2021 CKD-EPI equations, which no longer include race terms, was associated with differences in accruable preemptive wait time by race when using the creatinine-cystatin C–based equation but not the creatinine-based equation. Using these revised estimating equations, we no longer observed a statistically significant difference in the time between eGFR of 20 ml/min per 1.73 m2 and the onset of the need for KRT by race. However, we found that the 2021 creatinine-based CKD-EPI equation was superior to the 2021 creatinine-cystatin C CKD-EPI equation in attenuating racial differences in accruable preemptive wait time within a theoretical context. Importantly, the difference in accruable preemptive wait time by race was still 5 months when using eGFRcr-cys (with shorter accruable wait time in Black participants) but only 1 month when using eGFRcr (with shorter accruable wait time in White participants). Our data suggest that use of the 2021 creatinine-based CKD-EPI equation to determine preemptive waitlist eligibility is the strategy that is most likely to reduce racial differences in access to preemptive wait time accrual.
We previously demonstrated that attenuation of racial differences in theoretical preemptive wait time accrual could be improved through elimination of the race coefficient in the 2012 CKD-EPI equations, although this approach may lead to inaccuracies in the estimates of GFR (1). This was confirmed in other studies that used electronic health record data to examine this issue (12,13). The inclusion of race in the estimation of GFR for preemptive waitlisting has been an issue of concern, and OPTN recently released a request for feedback on the use of GFR estimating equations because there was no requirement to use any particular metric of GFR to determine waitlist eligibility (14,15). In June 2022, the OPTN board of directors approved the requirement to use race-neutral equations when estimating GFR for waitlisting in the future, which took effect in July of 2022 (4,14,15). We believe that the results of our study may help inform the choice of which race-neutral equation to use. Thus, our study has implications for policies related to preemptive waitlisting and deserves further confirmation in larger cohort studies.
Our models suggest that incorporation of cystatin C into the estimation of GFR does not improve observed differences in preemptive wait time that can be accrued by race, at least within a theoretical context. This is consistent with the findings of our prior study using the 2012 CKD-EPI cystatin C–based equation, which does not include a race term but also did not attenuate the racial differences that we observed in theoretical wait time accrual (1). Further studies are needed to understand the potential reasons for these observations.
One of the reasons that Black patients had a lower median eGFR at the time of entry into our study compared with White patients was likely that the rate of the progression of their kidney disease was faster. Because the CRIC study obtained annual laboratory measurements as part of a rigorous research protocol, the lower median eGFR of Black participants at the time of eligibility for our study (and therefore, eligibility for waitlist registration) compared with White participants suggests that the faster rate of progression in Black participants contributes to differences in access to preemptive waitlisting. A similar phenomenon would be expected in clinical practice when practitioners typically see patients with CKD at relatively fixed intervals. Coupled with the known lower rates of referral for preemptive transplantation in Black patients (16), in a real-world context, such differences are likely to be even more exaggerated. As we previously noted, one potential option to improve the observed differences in preemptive waitlisting is to allow for waitlist registration at different thresholds of eGFR by race (rather than using the same threshold for all patients) (1). However, the current national policy uses a single threshold of GFR for waitlist registration, and under these current policies, our data suggest that the 2021 CKD-EPI creatinine-based equation is associated with greater attenuation of differences by race compared with the creatinine-cystatin C–based equation.
One of the strengths of our study was the availability of laboratory data ascertained routinely at regular intervals in a research setting, so that our results are less likely to be influenced by the frequency with which creatinine or cystatin C is measured in clinical practice settings or when using electronic health record data. We note that we did not include measured GFR in this study as a metric for comparison, as we believe it is rare for recipients with stage 4 or 5 CKD to undergo actual measurements of their GFR for the purpose of waitlist registration, and such tests are not widely available or commonly performed in this setting. We chose to estimate GFR in accordance with the recent recommendations released by the National Kidney Foundation and the American Society of Nephrology (4).
We do highlight a few limitations to our study. Our study was conducted within a theoretical context and does not address whether using the 2021 CKD-EPI equations would lead to actual improvements in transplant rates for Black individuals. In addition, estimation of GFR is important in many contexts outside of preemptive waitlist registration, and our study does not address whether the use of the 2021 CKD-EPI equations improves health disparities in other contexts, such as drug dosing, diagnosis of CKD, or determination of clinical trial eligibility (17–19). Because of the observational nature of our study, residual confounding may be present. Although the CRIC study is a national study conducted at diverse sites throughout the United States, it is possible that study participants differ from candidates for waitlisting or transplantation in the general population. It is also possible that the patients included for study may not all have met eligibility criteria for actual receipt of a kidney transplant or had contraindications to waitlisting. In addition, the sample size in this study was limited, and we may not have sufficient power to detect differences between risk groups that could be present within a broader and larger population. Lastly, our findings are theoretical within a modeled context and should be validated in other CKD cohorts, but our study was designed to highlight potential areas deserving further research.
In conclusion, although the difference in accruable preemptive wait time by race was not statistically significant, it was still 5 months when using eGFRcr-cys (with shorter accruable wait time in Black participants) but only 1 month when using eGFRcr (with shorter accruable wait time in White participants). We suggest that the use of the 2021 creatinine-based CKD-EPI equation for waitlist eligibility determination may reduce racial differences in preemptive wait time accrual more than use of the race-free creatinine-cystatin C equation. Further studies are needed to understand how to eliminate disparities in access to kidney transplantation.
Disclosures
D.B. Adey reports consultancy agreements with Natera and Otsuka; research funding from Allovir, Hansa Pharmaceuticals, and Natera; honoraria from the American Board of Internal Medicine, the Fresno-Madera Medical Society, and Otsuka; and advisory or leadership roles for the American Board of Internal Medicine Subspecialty Governance Board, the American Society of Transplantation Organ Procurement and Transplantation Network (OPTN)/UNOS Policy Committee Conflict of Interest Committee, and the Transplant International Journal Editorial Board. S. Amaral reports consultancy agreements with Bristol Myers Squibb (DSMB); advisory or leadership roles for Dialysis Patient Citizens (advisory council, education center, and ad hoc Multi-Organ Transplant committee) and Vascular Composite Allograft committees for United Network for Organ Sharing/OPTN; and National Institutes of Health (NIH) funding from the National Institute of Child Health and Human Development and the National Institute of Diabetes and Digestive and Kidney Diseases. K.L. Johansen reports employment with Hennepin Healthcare, consultancy agreements with Akebia and Vifor, grant funding from NIH, serving as a member of the steering committee for the GlaxoSmithKline prolyl hydroxylase inhibitor clinical trials program, serving in an advisory or leadership role for the Akebia Advisory Board, and serving as an associate editor of JASN. E. Ku reports ownership interest in Edison Company; research funding from CareDx, Natera, and NIH; and serving as an associate editor of American Journal of Kidney Diseases and an advisory role for the American Kidney Fund Health Equity Coalition. L. Li reports employment with the Public Health Institute and ownership interest in Elevance Health Inc and United Health Group Inc. The remaining author has nothing to disclose.
Funding
K.L. Johansen, E. Ku, and C.E. McCulloch were funded by National Institute of Diabetes and Digestive and Kidney Diseases grant R01 DK115629. S. Amaral, K.L. Johansen, E. Ku, and C.E. McCulloch were also funded by National Institute of Diabetes and Digestive and Kidney Diseases grant R01 DK120886.
Acknowledgments
The CRIC study was conducted by the CRIC investigators and supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK).
The data from the CRIC study reported here were supplied by the NIDDK Central Repositories.
This manuscript was not prepared in collaboration with the investigators of the CRIC study and does not necessarily reflect the opinions or views of the CRIC study, the NIDDK Central Repositories, or NIDDK.
Footnotes
Published online ahead of print. Publication date available at www.cjasn.org.
See related editorial, “Reducing Racial Disparities in Access to Transplant in the United States: One Step at a Time,” on pages 1439–1441.
Author Contributions
D.B. Adey, K.L. Johansen, and E. Ku conceptualized the study; D.B. Adey, S. Amaral, K.L. Johansen, E. Ku, L. Li, and C.E. McCulloch were responsible for investigation; K.L. Johansen, E. Ku, L. Li, and C.E. McCulloch were responsible for formal analysis; K.L. Johansen, E. Ku, L. Li, and C.E. McCulloch were responsible for methodology; S. Amaral and K.L. Johansen were responsible for resources; L. Li was responsible for validation; S. Amaral, K.L. Johansen, E. Ku, and C.E. McCulloch were responsible for funding acquisition; K.L. Johansen and C.E. McCulloch provided supervision; E. Ku wrote the original draft; and D.B. Adey, S. Amaral, K.L. Johansen, L. Li, and C.E. McCulloch reviewed and edited the manuscript.
Data Sharing Statement
All of the data used in this manuscript are accessible by the public with an appropriate data use agreement at no cost and were derived from the National Institute of Diabetes and Digestive and Kidney Diseases Central Repository.
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