Kidney function and kidney failure prediction in a large multiethnic population

Adir Sommer; Arjun K. Manrai; Ben Reis; James A. Diao; Keren Cohen; Marinka Zitnik; Maya Makov-Assif; Morgan Sanchez; Noa Dagan; Ran D. Balicer

REVIEW 3 major objections 6 minor 14 references

Kidney function and kidney failure prediction in a large multiethnic population

T0 review · 3 major / 6 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read The choice of eGFR equation has little effect on predicting who develops kidney failure, but it substantially changes how many people are labeled with chronic kidney disease and at what stage.

desk verdict Largest head-to-head of four eGFR equations to date; the prevalence and calibration results are likely robust, but the predictive ranking rests on a sicker, survival-conditioned UACR subpopulation and needs a sensitivity analysis. read the letter →

arxiv 2607.22504 v1 pith:OB333LQ2 submitted 2026-07-24 q-bio.QM

James A. Diao , Morgan Sanchez , Adir Sommer , Keren Cohen , Maya Makov-Assif , Marinka Zitnik , Ben Reis , Ran D. Balicer

show 2 more authors

Noa Dagan Arjun K. Manrai

This is my paper · ORCID

classification q-bio.QM

keywords eGFRchronickidneydiseasefailurepredictionMDRDCKD-EPIEKFCrace-neutralequationsCKDprevalence

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether the switch to race-neutral equations for estimating kidney function changes clinical predictions or only reshuffles labels. Using 1.9 million adults followed for up to 10 years, it finds that the oldest equation (2006 MDRD) best discriminates 5-year kidney failure, while the newest European equation (2021 EKFC) performs worst, yet the absolute differences are small. The bigger effect is on classification: equations disagree enough to move people between CKD stages and to shift age-adjusted CKD prevalence from 8.5% to 10.8% depending on the equation. Across regions of birth, prevalence varies even more, from 8.9% to 15.3%. The paper concludes that equation choice matters more for epidemiology and clinical thresholds than for predicting which individual will progress to kidney failure.

What carries the argument

The central objects are the four eGFR equations: 2006 MDRD, 2009 CKD-EPI, 2021 CKD-EPI, and 2021 EKFC. These are compared using three mechanisms: cross-tabulation of CKD stage reclassification against measured creatinine clearance, survival curves with death as a competing risk, and AUROC/AUPRC for predicting 5-year kidney failure (initiation of dialysis or transplant), both as standalone eGFR and as an input to the Kidney Failure Risk Equation (KFRE). The work also uses age-standardized prevalence to compare CKD burden across regions of birth. The KFRE, which adds age, sex, and urinary albumin-to-creatinine ratio, improves AUROC by roughly 4.4 percentage points on average, far more than the

What would settle it

A prospective study in a general primary-care population that systematically measures UACR in everyone, follows all participants for 5 years without informative dropout, and compares the four equations' AUROC for kidney failure would settle whether the paper's ranking (2006 MDRD best, 2021 EKFC worst) is generalizable; if the AUROC differences shrink to zero or reverse, the central predictive claim would be falsified.

Watch

Extended reading notes

Core claim

The paper claims that among four creatinine-based eGFR equations—2006 MDRD, 2009 CKD-EPI, 2021 CKD-EPI, and 2021 EKFC—the choice of equation barely changes the prediction of incident kidney failure, but it significantly changes the correspondence between CKD stage and failure risk, and it changes the measured prevalence of CKD across populations. Concretely, 2006 MDRD yields the highest AUROC for 5-year kidney failure (0.862) and 2021 EKFC the lowest (0.846), a difference of only 0.016. Meanwhile, 2021 EKFC produces lower GFR estimates and 2021 CKD-EPI higher ones relative to prior equations, causing net reclassification to more severe stages with EKFC and less severe stages with 2021 CKD-EP

Load-bearing premise

The ranking of eGFR equations by discrimination is based on a subpopulation that has both UACR measurements and complete follow-up, and this subpopulation is much sicker than the full 1.9 million-person cohort, so the ranking may not hold if missing UACR or dropout is not random.

Editorial extensions

If this is right

If the central claim holds, health systems can choose between race-neutral eGFR equations without meaningfully changing which patients progress to kidney failure; prediction accuracy is nearly equation-independent.
The choice of equation will change CKD prevalence estimates by up to 2.3 percentage points nationally, and this difference is comparable to or larger than differences between regions of birth.
Stage reclassification from equation choice could affect clinical actions tied to GFR thresholds, including specialist referral, transplant listing, and dosing of renally cleared medications.
The KFRE's larger gains suggest that adding albuminuria and other predictors matters more than refining the creatinine-based equation itself.
The plateau in discrimination among eGFR equations points to serum creatinine as a limiting factor, motivating further study of alternative filtration markers.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the predictive plateau is real, future gains in kidney failure risk prediction will likely come from biomarkers such as cystatin C or from directly incorporating albuminuria, not from further tweaks to creatinine-based equations.
The equal predictive performance of race-stratified and race-neutral equations in this large multiethnic population suggests that removing race from eGFR equations does not sacrifice prognostic accuracy, a point the paper's data support but which is not the paper's central claim.
The UACR-available subpopulation used for discrimination analyses is markedly sicker than the full cohort, so the ranking of equations (MDRD highest, EKFC lowest) may not generalize to general primary-care populations if UACR missingness is informative; the paper acknowledges this as selection bias.
Prevalence differences across regions of birth of similar magnitude to equation differences imply that regional kidney disease burden estimates are sensitive to equation choice, which has implications for global health resource allocation.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short version: this is the largest head-to-head comparison of 2006 MDRD, 2009/2021 CKD-EPI, and 2021 EKFC in one cohort, and it gives the cleanest evidence yet that equation choice changes CKD prevalence and stage distributions far more than it changes outcome prediction. The prevalence numbers are probably right. The predictive ranking is less certain: it rests on a sicker UACR-tested subpopulation conditioned on five-year survival, and the authors don't do enough to show that selection is ignorable.

What's new: prior comparisons only pitted 2009 against 2021 CKD-EPI in Swedish and US cohorts. Adding MDRD and EKFC in ~1.9M adults with region-of-birth stratification is a real contribution. The decision to use default EKFC Q values rather than refitting to Clalit is the right call for fairness. Fine-Gray models, direct age standardization, and a clear tabulation of the subcohorts are all solid. The acknowledgment that UACR-based analysis may be selected is present, but it's only qualitative.

Where it's soft: Figure 3's AUROC/KFRE comparisons use 182,034 patients with CKD, UACR, and no death/disenrollment for 5 years. Table 1 shows that group has 65.9% diabetes and 4.81% dialysis initiation vs 19.0% and 0.77% in the full cohort. If UACR testing or survival is correlated with severity and with the eGFR values produced by each equation, the observed ordering—MDRD best, EKFC worst—may not hold in the full CKD population or in primary care. The authors never run a sensitivity analysis in the full CKD cohort, and the AUC differences are small (≤0.016), so the ranking is not robustly established. This doesn't kill the central claim—"little effect on prediction" still has support—but it weakens the stronger version that MDRD is genuinely more discriminative. Minor: prevalence estimates are reported without CIs; the abstract says 1,909,042 while the text says 1,908,042; no code or data are available.

Bottom line: This deserves a serious referee. The prevalence and calibration results are likely to hold, and the subpopulation concern is addressable. I'd ask for a sensitivity analysis in the full CKD cohort or an explicit selection model, CIs for prevalence, and a fix for the count typo. I'd bring it to reading group; I'd cite the prevalence numbers.

Referee Report

3 major / 6 minor

Summary. This retrospective cohort study uses Clalit Health Services data on approximately 1.9 million adults to compare four creatinine-based eGFR equations—2006 MDRD, 2009 CKD-EPI, 2021 CKD-EPI, and 2021 EKFC—in terms of calibration, discrimination of 5-year kidney failure, and age-adjusted CKD prevalence. The authors report that 2021 EKFC yields lower GFR estimates and 2021 CKD-EPI higher GFR estimates than earlier equations; that 2006 MDRD has the highest AUROC (0.862) and 2021 EKFC the lowest (0.846) for predicting kidney failure; that KFRE improves prediction substantially over eGFR alone; and that age-adjusted CKD prevalence varies by equation (8.5% to 10.8%) and by region of birth (8.9% to 15.3%). The central conclusion is that equation choice has little effect on incident kidney outcome prediction but large effects on CKD staging, prevalence, and clinical classification.

Significance. If the findings are robust, this is a valuable head-to-head comparison in a large, multiethnic, population-based cohort with long follow-up. The study's design avoids circularity: the equations are external, and the authors deliberately use default EKFC Q values rather than recalibrating to the Clalit population. The use of Fine-Gray subdistribution hazards for stage-specific outcomes and the inclusion of KFRE alongside eGFR alone are strengths. The prevalence differences across equations and birth regions have direct implications for health-system decisions and health equity. The main limitation is that the predictive comparisons in Figure 3 are restricted to a UACR-available, 5-year-survivor subpopulation that is substantially sicker than the full cohort, so the generalizability of the AUROC ranking and the 'little effect on prediction' claim needs additional support.

major comments (3)

[Figure 3 and Table 1] The AUROC/KFRE comparisons that support the 'little effect on prediction' claim are computed in 182,034 patients with available UACR and with at least 5 years of follow-up without death or disenrollment. Table 1 shows this subpopulation is much sicker than the full cohort: CKD 100% vs 15.1%, diabetes 65.9% vs 19.0%, dialysis during follow-up 4.81% vs 0.77%, and death 46.5% vs 13.9%. If UACR availability or 5-year survival is informative, the relative ranking (MDRD highest, EKFC lowest) may not generalize to the full cohort or to primary-care populations. The Discussion acknowledges selection bias only qualitatively (second limitation). Because eGFR-only discrimination does not require UACR, an AUROC analysis in the full CKD cohort (n=284,399) and an inverse-probability-weighted or multiple-imputation sensitivity analysis for UACR availability are needed to support the general claim.
[Figure 3 and Methods (Statistical analysis)] The handling of death is inconsistent across analyses. Figures 1 and 2 use the Fine-Gray subdistribution hazard with death as a competing risk, but Figure 3 excludes deaths and disenrollments within 5 years entirely. This may bias discrimination estimates, especially in a subpopulation with 46.5% mortality. A competing-risk-aware discrimination measure (e.g., inverse-probability-of-censoring-weighted AUC) or a sensitivity analysis treating death as a composite outcome would clarify whether the small AUROC differences (≤0.016) are robust to how the competing event is handled.
[Methods, Outcome definitions; Figure 4] The prevalence analyses assume that individuals without UACR data have no albuminuria: 'Individuals without data on either urinary albumin or urinary creatinine were assumed to not have albuminuria.' Only 27.6% of the full cohort has UACR recorded, and 7.5% have UACR>30 mg/g. If missingness is informative with respect to region of birth or eGFR level, the age-adjusted prevalence differences across equations and regions (Figure 4) will be affected. A sensitivity analysis using an eGFR-only definition of CKD or imputation of missing UACR would strengthen the prevalence claim.

minor comments (6)

[Results, first paragraph] The text states the study population comprised 1,908,042 adults, while the Abstract and Table 1 report 1,909,042. Please correct the inconsistency.
[Figure 3 caption] The caption gives n=182,034, while Table 1 reports 191,370 for 'CKD and Recorded UACR.' The additional exclusions (5-year follow-up, no death/disenrollment, complete covariate data) should be stated explicitly so readers can reconcile the numbers.
[Methods, Outcome definitions] The CKD definition requires 'two or more consecutive measures spanning at least three months' for eGFR, but for UACR the text says 'any time frame prior to the index date' without specifying a second measurement. Clarify whether a single UACR≥30 suffices and how this interacts with the 'assumed no albuminuria' rule for missing UACR.
[Discussion] The sentence 'the age-adjusted prevalence of kidney disease in the adult Clalit population was comparable to the global all-age crude prevalence of 9.1%' compares an age-adjusted adult prevalence to a crude all-age global prevalence. This comparison is not age-standardized and should be rephrased or removed.
[Figure 2] The sentence describing stage-specific risk as 'divergent for 2006 MDRD and 2021 EKFC (higher and lower rates of kidney failure respectively)' is ambiguous. Please specify which equation corresponds to higher versus lower rates within each CKD stage.
[Figure 1] The cross-tabulations in Figure 1 are based on 18,202 participants with measured creatinine clearance, a subgroup with high dialysis (8.9%) and death (40.7%) rates. Reporting cell counts and confidence intervals for the 5-year risk estimates would improve transparency.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the study evaluates externally published eGFR equations against new outcome data without fitting equation parameters.

full rationale

The paper does not derive or fit any of the four eGFR equations; each equation (2006 MDRD, 2009 CKD-EPI, 2021 CKD-EPI, 2021 EKFC) is an externally published formula applied to the Clalit cohort. The authors explicitly avoid recalibrating EKFC to the study population, stating: “we used the default Q values for EKFC of 0.62 for men and 0.80 for women rather than the observed median serum creatinine values of 0.66 and 0.89.” The primary comparisons—AUROC/AUPRC for 5-year kidney failure, CKD stage reclassification, and age-adjusted CKD prevalence—are all evaluations of fixed external formulas against observed outcomes, not predictions inferred from the same fitted quantities. The paper’s own self-citations (refs 3 and 26) are background context on race in GFR estimation and clinical implications, and are not load-bearing inputs to the numeric results. The AUROC analyses are restricted to the UACR-available CKD subpopulation, and the authors acknowledge potential selection bias in their limitations: “analyses performed on subpopulations defined by the availability of laboratory measurements may be subject to selection bias.” This is an external-validity concern, not a circularity concern. No step in the derivation chain reduces to its own inputs by construction, and no fitted parameter is renamed as a prediction. Therefore the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No new free parameters are fitted; the four eGFR equations and KFRE are fixed external models, and EKFC uses published default Q values (0.62 men, 0.80 women). The core assumptions concern missing-data handling, US race-label transfer, surrogate outcome validity, and competing-risk modeling.

assumptions (4)

domain assumption Participants without recorded UACR are treated as not having albuminuria (missing UACR implies UACR<30).
Used in the definition of CKD and prevalence estimates. Biases prevalence downward if missingness depends on albuminuria. Stated in Methods: 'Individuals without data on either urinary albumin or urinary creatinine were assumed to not have albuminuria.'
domain assumption The US 'non-Black' race label can be applied to all Clalit participants for the race-stratified equations.
Figure 4 caption: 'We used "non-Black" race as the input for all race-stratified equations.' If Israeli/immigrant populations are not well represented by US race bins, race-stratified equation results are biased.
domain assumption Creatinine clearance is an acceptable directly measured surrogate for GFR for reclassification comparisons.
Used in Figure 1B. The authors acknowledge it overestimates GFR due to tubular secretion (Methods, ref 12), so discordance with eGFR may partly reflect this bias.
standard math Fine-Gray subdistribution model with death as competing risk is a valid characterization of kidney failure risk.
Used for survival curves and reclassification risk tables. Standard in competing-risk settings but depends on correct censoring assumptions.

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Cite this review

Pith. "Pith review of Kidney function and kidney failure prediction in a large multiethnic population." pith.science (2026). https://pith.science/paper/OB333LQ2

@misc{pith2026260722504,
  author       = {Pith},
  title        = {Pith review of: Kidney function and kidney failure prediction in a large multiethnic population},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OB333LQ2}},
  note         = {Machine review of arXiv:2607.22504}
}

read the original abstract

Background: Patients with chronic kidney disease (CKD) experience worsening kidney function and develop subsequent kidney failure at different rates. Accurate estimates of a patient's current and future kidney function are needed for optimal clinical decision-making. Methods: To compare current and previously recommended equations for estimating kidney function and risk of kidney failure across a large multiethnic population, we conducted a retrospective multicenter cohort study of primary care, acute care, and hospital settings. Our study population comprised 1,909,042 adults with at least one recorded serum creatinine measurement during 2012-2014, with follow-up until January 2025. Our primary outcomes were the area under the receiver operating characteristic curve and prevalence of CKD by stage across regions of origin. Findings: GFR estimates from the two race-stratified equations (2006 MDRD and 2009 CKD-EPI) were similarly calibrated. GFR estimates from the two race-neutral equations diverged, with 2021 EKFC producing lower GFR estimates and 2021 CKD-EPI producing higher GFR estimates compared to prior equations. The oldest equation, 2006 MDRD, was the most discriminative of kidney failure within 5 years while the newer European equation, 2021 EKFC, was the least discriminative, with AUROCs of 0.862 (95% CI, 0.855-0.869) and 0.846 (95% CI, 0.838-0.853), respectively. The age-adjusted prevalence of CKD varied by the choice of eGFR equation (ranging from 8.5% for 2021 CKD-EPI to 10.8% for EKFC) and across regions of birth (ranging from 8.9% for East Sub-Saharan Africa to 15.3% for South Asia). Interpretation: Using 10 years of follow-up for nearly 2 million individuals, our study demonstrates how newly developed US and European equations diverge from previously recommended equations with broad clinical and epidemiological implications.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

14 extracted references · 2 canonical work pages

[1]

Diao, M.D., M.Phil.a,b,c *, Morgan Sanchez, B.S.a,b *, Adir Sommer, M.D.d, Keren Cohen, B.S.d, Maya Makov-Assif, M.D.d, Marinka Zitnik, Ph.D.a,b,e,f, Ben Reis, Ph.D.a,g, Ran D

Kidney function and kidney failure prediction in a large multiethnic population James A. Diao, M.D., M.Phil.a,b,c *, Morgan Sanchez, B.S.a,b *, Adir Sommer, M.D.d, Keren Cohen, B.S.d, Maya Makov-Assif, M.D.d, Marinka Zitnik, Ph.D.a,b,e,f, Ben Reis, Ph.D.a,g, Ran D. Balicer, M.D., M.P.H.a,d,h **, Noa Dagan, M.D., Ph.D.a,d,i **, Arjun K. Manrai, Ph.D.a,b **...

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[2]

2006 MDRD B

Rates of Kidney Failure by CKD Stage and GFR Estimating Equation among Participants with Chronic Kidney Disease A. 2006 MDRD B. 2009 CKD-EPI C. 2021 CKD-EPI D. 2021 EKFC A. Survival curves for each of six chronic kidney disease (CKD) stages as calculated using the 2006 Modification of Diet in Renal Disease (MDRD) equation among 284,399 participants with c...

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[3]

18 Nyman U, Grubb A, Sterner G, Björk J

DOI:10.1016/S0140-6736(24)00367-2. 18 Nyman U, Grubb A, Sterner G, Björk J. The CKD-EPI and MDRD equations to estimate GFR. Validation in the Swedish Lund-Malmö Study cohort. Scand J Clin Lab Invest 2011; 71: 129–38. 19 Chen L-I, Guh J-Y, Wu K-D, et al. Modification of diet in renal disease (MDRD) study and CKD epidemiology collaboration (CKD-EPI) equatio...

work page doi:10.1016/s0140-6736(24)00367-2 2011
[4]

non-Black

Estimated Age-Adjusted Prevalence of CKD by eGFR Equation and Region of Birth Age-adjusted prevalence of chronic kidney disease (CKD) by Global Burden of Disease (GBD) super region of birth. Chronic kidney disease is defined as two or more consecutive measurements of eGFR < 60 ml/min/1.73 m2 spanning at least three months or most recent urine albumin:crea...

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[11]

Demographic, clinical, and laboratory characteristics in the study population Variable Total study population Population with CKD CKD and Recorded UACR Recorded creatinine clearance Data Count–no. 1,909,042 284,399 191,370 18,202 Median Follow-up–years 9.8 9.5 9.5 9.5 Age, years Aged 25-44–% 37.5 3.5 4.3 13.4 Aged 45-64–% 35.1 21.8 25.4 27.5 Aged 65-74–% ...

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Reclassification from 2009 CKD-EPI to alternate eGFR equations B

Discordance in CKD Stage Classification and Kidney Failure Risk A. Reclassification from 2009 CKD-EPI to alternate eGFR equations B. Kidney failure risk of alternate eGFR equations against creatinine clearance A. Classification of chronic kidney disease (CKD) stages when GFR is estimated using the 2009 Chronic Kidney Disease Epidemiology Collaboration (CK...

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DOI:10.1007/s11606-020-06280-5. 6 Inker LA, Eneanya ND, Coresh J, et al. New Creatinine- and Cystatin C-Based Equations to Estimate GFR without Race. N Engl J Med 2021; 385: 1737–49. 7 Delgado C, Baweja M, Crews DC, et al. A Unifying Approach for GFR Estimation: Recommendations of the NKF-ASN Task Force on Reassessing the Inclusion of Race in Diagnosing K...

work page doi:10.1007/s11606-020-06280-5 2021
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3 Diao JA, Inker LA, Levey AS, Tighiouart H, Powe NR, Manrai AK

DOI:10.1056/NEJMms2004740. 3 Diao JA, Inker LA, Levey AS, Tighiouart H, Powe NR, Manrai AK. In search of a better equation - performance and equity in estimates of kidney function. N Engl J Med 2021; 384: 396–9. 4 Powe NR. Black Kidney Function Matters: Use or Misuse of Race? JAMA 2020; 324: 737–8. 5 Ahmed S, Nutt CT, Eneanya ND, et al. Examining the Pote...

doi:10.1056/nejmms2004740 2021

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15 Tangri N, Stevens LA, Griffith J, et al. A predictive model for progression of chronic kidney disease to kidney failure. JAMA 2011; 305: 1553–9. 15 16 Tangri N, Grams ME, Levey AS, et al. Multinational Assessment of Accuracy of Equations for Predicting Risk of Kidney Failur...

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The collection date of the latest such serum creatinine measurement was defined as the individual’s index date, before which covariates are collected and after which follow-up is assessed. As of the index date, participants were required to be aged 25-90 years, have no history...

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Lancet 2024; published online April

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9 Gansevoort RT, Anders H-J, Cozzolino M, et al

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Our primary outcomes were the area under the receiver operating characteristic curve and prevalence of CKD by stage across regions of origin. Findings: GFR estimates from the two race-stratified equations—2006 Modification of Diet in Renal Disease (MDRD) and 2009 CKD Epidemiol...

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Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.

[1] [1]

Diao, M.D., M.Phil.a,b,c *, Morgan Sanchez, B.S.a,b *, Adir Sommer, M.D.d, Keren Cohen, B.S.d, Maya Makov-Assif, M.D.d, Marinka Zitnik, Ph.D.a,b,e,f, Ben Reis, Ph.D.a,g, Ran D

Kidney function and kidney failure prediction in a large multiethnic population James A. Diao, M.D., M.Phil.a,b,c *, Morgan Sanchez, B.S.a,b *, Adir Sommer, M.D.d, Keren Cohen, B.S.d, Maya Makov-Assif, M.D.d, Marinka Zitnik, Ph.D.a,b,e,f, Ben Reis, Ph.D.a,g, Ran D. Balicer, M.D., M.P.H.a,d,h **, Noa Dagan, M.D., Ph.D.a,d,i **, Arjun K. Manrai, Ph.D.a,b **...

2012

[2] [2]

2006 MDRD B

Rates of Kidney Failure by CKD Stage and GFR Estimating Equation among Participants with Chronic Kidney Disease A. 2006 MDRD B. 2009 CKD-EPI C. 2021 CKD-EPI D. 2021 EKFC A. Survival curves for each of six chronic kidney disease (CKD) stages as calculated using the 2006 Modification of Diet in Renal Disease (MDRD) equation among 284,399 participants with c...

2006

[3] [3]

18 Nyman U, Grubb A, Sterner G, Björk J

DOI:10.1016/S0140-6736(24)00367-2. 18 Nyman U, Grubb A, Sterner G, Björk J. The CKD-EPI and MDRD equations to estimate GFR. Validation in the Swedish Lund-Malmö Study cohort. Scand J Clin Lab Invest 2011; 71: 129–38. 19 Chen L-I, Guh J-Y, Wu K-D, et al. Modification of diet in renal disease (MDRD) study and CKD epidemiology collaboration (CKD-EPI) equatio...

work page doi:10.1016/s0140-6736(24)00367-2 2011

[4] [4]

non-Black

Estimated Age-Adjusted Prevalence of CKD by eGFR Equation and Region of Birth Age-adjusted prevalence of chronic kidney disease (CKD) by Global Burden of Disease (GBD) super region of birth. Chronic kidney disease is defined as two or more consecutive measurements of eGFR < 60 ml/min/1.73 m2 spanning at least three months or most recent urine albumin:crea...

2009

[5] [11]

Demographic, clinical, and laboratory characteristics in the study population Variable Total study population Population with CKD CKD and Recorded UACR Recorded creatinine clearance Data Count–no. 1,909,042 284,399 191,370 18,202 Median Follow-up–years 9.8 9.5 9.5 9.5 Age, years Aged 25-44–% 37.5 3.5 4.3 13.4 Aged 45-64–% 35.1 21.8 25.4 27.5 Aged 65-74–% ...

2012

[6] [12]

Reclassification from 2009 CKD-EPI to alternate eGFR equations B

Discordance in CKD Stage Classification and Kidney Failure Risk A. Reclassification from 2009 CKD-EPI to alternate eGFR equations B. Kidney failure risk of alternate eGFR equations against creatinine clearance A. Classification of chronic kidney disease (CKD) stages when GFR is estimated using the 2009 Chronic Kidney Disease Epidemiology Collaboration (CK...

2009

[7] [15]

6 Inker LA, Eneanya ND, Coresh J, et al

DOI:10.1007/s11606-020-06280-5. 6 Inker LA, Eneanya ND, Coresh J, et al. New Creatinine- and Cystatin C-Based Equations to Estimate GFR without Race. N Engl J Med 2021; 385: 1737–49. 7 Delgado C, Baweja M, Crews DC, et al. A Unifying Approach for GFR Estimation: Recommendations of the NKF-ASN Task Force on Reassessing the Inclusion of Race in Diagnosing K...

work page doi:10.1007/s11606-020-06280-5 2021

[8] [17]

3 Diao JA, Inker LA, Levey AS, Tighiouart H, Powe NR, Manrai AK

DOI:10.1056/NEJMms2004740. 3 Diao JA, Inker LA, Levey AS, Tighiouart H, Powe NR, Manrai AK. In search of a better equation - performance and equity in estimates of kidney function. N Engl J Med 2021; 384: 396–9. 4 Powe NR. Black Kidney Function Matters: Use or Misuse of Race? JAMA 2020; 324: 737–8. 5 Ahmed S, Nutt CT, Eneanya ND, et al. Examining the Pote...

doi:10.1056/nejmms2004740 2021