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Definition, staging and measurement

TL;DR — CKD requires persistence for more than three months; classify it by cause, GFR category G1–G5 and albuminuria category A1–A3 (KDIGO CKD Work Group 2024, PMID 38490803). eGFR is an estimate with biological and analytical error, while uACR has within-person variability; confirmation is therefore part of classification. Creatinine and cystatin C carry different non-GFR determinants, and their combined race-free equation is generally more accurate than either alone (Inker 2021, PMID 34554658). Risk is continuous across both axes, so stage labels are shorthand rather than biological discontinuities (CKD Prognosis Consortium 2023, PMID 37787795).

Definition and chronicity

A single low eGFR may reflect acute illness, laboratory variation or medication haemodynamics. CKD requires duration, prior values or another convincing marker of chronicity (KDIGO CKD Work Group 2024, PMID 38490803).

GFR categories

G1 is at least 90, G2 60–89, G3a 45–59, G3b 30–44, G4 15–29 and G5 below 15 mL/min/1.73 m². G1 and G2 require another damage marker (KDIGO CKD Work Group 2024, PMID 38490803).

Albuminuria categories

A1 is uACR below 30 mg/g, A2 30–300, and A3 above 300. Risk rises within categories and repeated testing reduces misclassification (CKD Prognosis Consortium 2023, PMID 37787795).

Estimation limits

Creatinine varies with muscle mass, diet and secretion; cystatin C varies with inflammation, thyroid state, adiposity and corticosteroid exposure. Discordance is information, not a reason to choose whichever value is convenient (Inker 2021, PMID 34554658).

Heat-map interpretation

The KDIGO heat map combines population risk strata, not individual destiny. Cause, trajectory, age, competing death, treatment and measurement uncertainty still matter (Tangri 2016, PMID 26757465).

Drug dosing

Indexed eGFR and unindexed clearance answer different questions. Medication labels may have been developed with older creatinine-clearance methods; a dosing decision should name the equation and body-size convention used (KDIGO CKD Work Group 2024, PMID 38490803).

Classification tables

These are the KDIGO 2024 categories; prognosis remains continuous within them (KDIGO CKD Work Group 2024, PMID 38490803).

G category eGFR (mL/min/1.73 m²) Interpretation
G1 ≥90 CKD only if another marker of kidney damage is present
G2 60–89 CKD only if another marker of kidney damage is present
G3a 45–59 Mild-to-moderate reduction, persistent for >3 months
G3b 30–44 Moderate-to-severe reduction, persistent for >3 months
G4 15–29 Severe reduction
G5 <15 Kidney failure category; treatment is not determined by eGFR alone
A category uACR (mg/g) Conventional label
A1 <30 Normal to mildly increased
A2 30–300 Moderately increased
A3 >300 Severely increased

How much change is real change

Short-term within-person variability sets the floor below which a "change" is noise. In 50 clinically stable CKD outpatients (median eGFR 40 mL/min/1.73 m², median uACR 173 mg/g) sampled three times within four weeks, the within-person coefficient of variation was 5.4% for serum creatinine, 4.1% for cystatin C, 7.4% for beta-trace protein and 5.6% for β2-microglobulin — but 29.7% for random-spot uACR, 32.5% for first-morning uACR, and 50.6% for random-spot urine albumin concentration alone (Waikar 2018, PMID 30031564). Translated into reference change values, a single first-morning uACR must rise by about 141% or fall by about 58% before it exceeds analytical and biological noise, whereas a creatinine-based eGFR needs roughly a 14–16% change (Waikar 2018, PMID 30031564). This is the arithmetic behind the KDIGO instruction to confirm albuminuria before assigning an A category, and it is why single-value albuminuria "responses" in observational data are unreliable.

The practical asymmetry is worth stating plainly: the filtration axis is measured precisely and interpreted loosely (because eGFR is a biased estimate of mGFR), while the damage axis is measured imprecisely and interpreted tightly (because A-category boundaries are sharp).

Creatinine–cystatin C discordance

Discordance between the two filtration markers is common and prognostic rather than merely technical. In an individual-participant meta-analysis of 821,327 outpatients from 23 cohorts and 39,639 inpatients from 2 cohorts, 11% of outpatients (cohort range 3–50%) and 35% of inpatients had an eGFRcys at least 30% lower than their eGFRcr (Estrella 2025, PMID 41202182). Over mean 11 years, that large negative eGFR difference carried higher all-cause mortality (28.4 vs 16.8 per 1,000 person-years; HR 1.69, 95% CI 1.57–1.82), cardiovascular mortality (HR 1.61, 1.48–1.76), heart failure (HR 1.54, 1.40–1.68), atherosclerotic cardiovascular disease (HR 1.35, 1.27–1.44) and kidney failure with replacement therapy (HR 1.29, 1.13–1.47) (Estrella 2025, PMID 41202182). Whether the excess risk reflects unmeasured true GFR loss, sarcopenia and low creatinine generation, or the inflammatory and adiposity determinants of cystatin C is unresolved; a CRIC analysis of 1,290 participants with iothalamate-measured GFR found that putative determinants — muscle-mass markers, middle-molecule clearance, obesity, inflammation — explained only 36% of the variance in eGFR difference and did not materially attenuate its associations with death or heart-failure hospitalisation (McCoy 2025, PMID 40373998).

Which equation to trust when they disagree has an empirical answer. Among 9,404 paired creatinine, cystatin C and iohexol-clearance measurements in Stockholm, the three estimates performed equivalently in the 45% of samples where eGFRcr and eGFRcys agreed within 20%. In the 47% of samples where eGFRcys was at least 20% lower, median bias was +15.0 for eGFRcr, −8.5 for eGFRcys and +0.8 mL/min/1.73 m² for the combined equation; P30 was 50%, 73% and 84%; correct GFR-category classification was 38%, 45% and 62% (Fu 2023, PMID 36995139). The combined equation is therefore not a tie-breaker of convenience — it is the accurate estimate precisely in the situation where clinicians are most tempted to pick a favourite.

Competing equations: CKD-EPI 2021 versus EKFC

The 2021 CKD-EPI race-free equations are not the only race-free option, and the alternatives were built on a different principle. The European Kidney Function Consortium (EKFC) equation rescales serum creatinine by Q, the median normal creatinine for age and sex, giving one continuous equation from age 2 to 90; in pooled development (11,251 participants) and external validation (8,378 participants) it had median bias −0.9 mL/min/1.73 m² in adults and 3.1% of estimates in error by more than 30%, outperforming CKD-EPI and CKiD at the paediatric–adult transition (Pottel 2021, PMID 33166224). A cystatin C version using a rescaling factor that varies with age but not race or sex was derived from 227,643 Swedish patients and validated in 11,231 European, 1,093 US and 508 African participants, where it was more accurate than the KDIGO-recommended CKD-EPI cystatin C equation, and the arithmetic mean of the two EKFC estimates was better than either alone (Pottel 2023, PMID 36720134).

Head-to-head in US cohorts the margin is small but consistent: across 12,854 participants with measured GFR, P30 was 79.2% (95% CI 78.5–79.9) for CKD-EPI 2021, 80.1% (79.4–80.7) for race-free EKFC and 81.1% (80.5–81.8) for EKFC with population-specific Q-values; only the population-specific version had a median bias statistically indistinguishable from zero (0.14, −0.07 to 0.35 mL/min/1.73 m²) versus 1.22 (0.99–1.47) for CKD-EPI 2021 (Delanaye 2024, PMID 38101514). The unresolved question is whether the population-specific Q re-imports by another name the population-level adjustment that the race-free reform removed, or whether a laboratory-derived normal value is categorically different from a self-reported social category — the two literatures have not been argued against each other in the same paper.

Equation Basis Reported accuracy Boundary
CKD-EPI 2021 cr Race-free refit of 2009 equation P30 79.2% in pooled US mGFR cohorts (Delanaye 2024, PMID 38101514) KDIGO-recommended default; overestimates mGFR by ~1.2 mL/min/1.73 m²
CKD-EPI 2021 cr-cys Both markers Most accurate of the CKD-EPI family (Inker 2021, PMID 34554658) Requires standardized cystatin C
EKFC cr Creatinine rescaled by age/sex-specific Q Adult bias −0.9; 3.1% of estimates off by >30% (Pottel 2021, PMID 33166224) Developed without Black participants
EKFC cys Cystatin C rescaled, no race or sex term More accurate than CKD-EPI eGFRcys across Europe, US and Africa (Pottel 2023, PMID 36720134) Rescaling factor still age-dependent
Combined cr-cys (any family) Both markers P30 84% vs 50%/73% when markers are discordant (Fu 2023, PMID 36995139) The discordance case is where it matters

The age-adaptation controversy

KDIGO retains a single eGFR threshold of 60 mL/min/1.73 m² at every age (KDIGO CKD Work Group 2024, PMID 38490803). The strongest empirical challenge comes from Alberta, where the same population was classified by a fixed threshold and by age-adapted thresholds of 75, 60 and 45 mL/min/1.73 m² for ages <40, 40–64 and ≥65. Fixed criteria produced 537 new cases per 100,000 person-years against 343 for age-adapted criteria, and the fixed cohort had lower risk (5-year kidney failure 1.7% vs 3.0%; death 21.9% vs 25.4%) because it was diluted by low-risk older people. Of the 72,703 people captured only by fixed criteria, 54,342 (75%) were aged 65 or over with eGFR 45–59 and A1 albuminuria; their 5-year kidney-failure risk was ≤0.12%, indistinguishable from non-CKD controls, and their risk of death exceeded their risk of kidney failure by 69-, 122-, 279- and 935-fold across successive age bands from 65–69 to ≥80 (Liu 2021, PMID 34459844).

The symmetric argument is that a fixed threshold under-diagnoses young adults, in whom the eGFR level at which mortality, cardiovascular and kidney-failure risk begins to rise is higher than 60 (Hundemer 2024, PMID 38411155). The counter-case for keeping one threshold rests on simplicity, on the fact that albuminuria — which is already in the definition — captures much of the risk that age-adaptation is trying to add, and on the observation that risk in the KDIGO heat map is continuous rather than dichotomous, so any threshold is administrative (CKD Prognosis Consortium 2023, PMID 37787795). This is genuinely unsettled; no trial has randomized a diagnostic threshold, and the outcome that would settle it — net harm from labelling versus net benefit from earlier protective therapy — has not been measured directly.

When albuminuria is not what was measured

Much of the world's kidney data is urine protein or dipstick, not albumin. Conversion equations derived from 919,383 adults with same-day paired measurements in 12 research and 21 clinical cohorts perform acceptably above a PCR of about 50 mg/g but are unreliable below it: sensitivity/specificity for screening at uACR ≥30 mg/g was 91%/87%, for stage A2 75%/89% and for stage A3 87%/98% (Sumida 2020, PMID 32658569). Dipstick is worse at the threshold that matters most: a category of trace-or-greater detected uACR ≥30 mg/g with sensitivity 62% and specificity 88%, and detected A2 with sensitivity 36% (Sumida 2020, PMID 32658569). Community data agree — in 10,944 Australian adults, dipstick ≥1+ had 57.8% sensitivity (95% CI 54.1–61.4) and 95.4% specificity for uACR ≥30 mg/g, but 98.9% sensitivity and 92.6% specificity for uACR ≥300 mg/g (White 2011, PMID 21411199). Dipstick therefore functions as a reasonable rule-in test for A3 and a poor rule-out test for A2, which is exactly the stratum where risk-based treatment decisions have moved.

Reassuringly for risk stratification specifically, a 2-year 4-variable kidney failure risk equation using uACR predicted from PCR discriminated similarly to one using observed uACR (Sumida 2020, PMID 32658569).

What these measurements can carry as trial endpoints

Both axes have been formally evaluated as surrogates, with different strength. For GFR: in 3,758,551 participants with baseline eGFR ≥60 and 122,664 with eGFR <60 across 14 cohorts, a slower eGFR decline by 0.75 mL/min/1.73 m²/year sustained over 2 years was associated with adjusted HR 0.70 (95% CI 0.68–0.72) for ESKD above 60 and 0.71 (0.68–0.74) below 60 — but the absolute translation depends entirely on baseline risk, reducing 5-year ESKD risk by 1.6% in a rapidly progressing population (8.3% baseline) and by only 0.13% in a low-risk one (0.58% baseline) (Grams 2019, PMID 31292199).

For albuminuria: treatment effects on 6-month albuminuria change predicted treatment effects on the clinical endpoint across 41 randomized comparisons in 29,979 participants (Heerspink 2019, PMID 30635226), with the parallel observational association established in the CKD Prognosis Consortium (Coresh 2019, PMID 30635225). The 2026 update expanded this to 48 trials and 85,681 participants: each 30% relative reduction in geometric-mean uACR corresponded to a 19% lower hazard of kidney failure or doubled creatinine (95% Bayesian credible interval 5–30%), with median R² 0.66 — but a credible interval on R² of 0.06 to 0.98, and no clear variation by CKD aetiology (Heerspink 2026, PMID 41198855). That R² interval is the honest summary of the surrogate's status: the central estimate supports use, the uncertainty band does not exclude a weak relationship.

The gap below CKD: acute kidney disease

Abnormality of kidney function or structure lasting three months or less meets neither AKI nor CKD criteria. KDIGO's 2021 consensus conference defined acute kidney disease (AKD) to fill that space — abnormalities with health implications of duration ≤3 months, which may include AKI but also captures milder or slower functional change developing over more than seven days (Lameire 2021, PMID 34252450). The construct matters for this page because the "persistence for >3 months" clause in the CKD definition creates an interval during which a person is measurably abnormal and formally unclassified, and because AKD is the state in which most CKD is actually first detected.

The dedicated monitoring study: cystatin C helps at diagnosis, not for tracking change

Most equation-comparison studies are cross-sectional. eGFR-C was designed prospectively to answer the monitoring question, in UK primary, secondary and tertiary care: 1,167 adults with stage 3 CKD for baseline accuracy against measured GFR, 875 followed for the ability to detect change over three years, 278 with additional measurements for progression modelling, and 20 studied to quantify GFR variability components, with a measurement-model analysis comparing monitoring-strategy costs in the 875 (Lamb 2024, PMID 39056437; design in Lamb 2014, PMID 24423077).

Two features of this design matter beyond its specific results. First, cystatin C costs more than creatinine, so the NHS-facing question is not "is it more accurate?" but "does the accuracy gain change a decision often enough to justify the cost?" — a question no cross-sectional P30 comparison can answer. Second, quantifying the reference change value for a significant GFR change is the monitoring analogue of the within-person variability work above (Waikar 2018, PMID 30031564): a monitoring strategy that cannot distinguish a real change from measurement noise generates false progression signals regardless of which equation it uses.

Decision and interpretation matrix

Dimension Question Guardrail
Diagnostic axis Cause + G category + A category Avoid treating eGFR as the diagnosis
Time axis Chronicity and trajectory Separate acute change from persistent disease
Risk axis Kidney failure + cardiovascular events + death Show competing events
Treatment axis Eligibility, absolute benefit, harm, burden Do not rank drugs by relative effect alone
Measurement axis Assay, equation, repeatability State what was actually measured
Equity axis Testing, referral, access, affordability Audit downstream care, not labels only
Patient axis Symptoms, function, life participation Include outcomes patients prioritize
Evidence axis RCT, cohort, model, guideline Do not collapse designs

Evidence ledger

This ledger makes the page’s evidentiary mix inspectable. It does not imply that every source answers every question.

PMID Record used Role and boundary
38490803 KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. (KDIGO CKD Work Group 2024, PMID 38490803) Guideline or commentary; recommendation evidence depends on its review.
34554658 New Creatinine- and Cystatin C-Based Equations to Estimate GFR without Race. (Inker 2021, PMID 34554658) Observational or conceptual evidence; association is not treatment effect.
37787795 Estimated GFR, Albuminuria, and Adverse Outcomes: individual-participant data meta-analysis. (CKD Prognosis Consortium 2023, PMID 37787795) Synthesis; heterogeneity and included-study definitions constrain transport.
26757465 Multinational assessment of equations predicting kidney failure. (Tangri 2016, PMID 26757465) Synthesis; heterogeneity and included-study definitions constrain transport.
38519239 Executive summary of the KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease: known knowns and known unknowns. (Levin 2024, PMID 38519239) Guideline or commentary; recommendation evidence depends on its review.
32061315 Global, regional, and national burden of chronic kidney disease, 1990-2017. (GBD CKD Collaboration 2020, PMID 32061315) Modelled projection; the estimate follows from the model inputs and assumptions, not from observed randomized follow-up.
22038337 A population-based approach for the definition of chronic kidney disease: CKD Prognosis Consortium. (Cirillo 2012, PMID 22038337) Synthesis; heterogeneity and included-study definitions constrain transport.
23243116 Cohort profile: the chronic kidney disease prognosis consortium. (Matsushita 2013, PMID 23243116) Observational or conceptual evidence; association is not treatment effect.
30348535 Relationship of Estimated GFR and Albuminuria to Concurrent Laboratory Abnormalities. (Inker 2019, PMID 30348535) Synthesis; heterogeneity and included-study definitions constrain transport.
34563581 A Unifying Approach for GFR Estimation: Recommendations of the NKF-ASN Task Force on Reassessing the Inclusion of Race in Diagnosing Kidney Disease. (Delgado 2022, PMID 34563581) Guideline or commentary; recommendation evidence depends on its review.
36857500 Kidney Failure Risk Equation evaluation with novel inputs in 59 cohorts. (Grams 2023, PMID 36857500) Observational or conceptual evidence; association is not treatment effect.
26028594 eGFR and albuminuria for prediction of cardiovascular outcomes: individual-participant meta-analysis. (Matsushita 2015, PMID 26028594) Synthesis; heterogeneity and included-study definitions constrain transport.
32970396 Dapagliflozin in Patients with Chronic Kidney Disease. (Heerspink 2020, PMID 32970396) Intervention study; eligibility, comparator, endpoint and follow-up bound inference.
36331190 Empagliflozin in Patients with Chronic Kidney Disease. (EMPA-KIDNEY Collaborative Group 2023, PMID 36331190) Intervention study; eligibility, comparator, endpoint and follow-up bound inference.
30990260 Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy. (Perkovic 2019, PMID 30990260) Intervention study; eligibility, comparator, endpoint and follow-up bound inference.
41205219 Chronic Kidney Disease Prevalence and Awareness Among US Adults. (Gong 2026, PMID 41205219) Observational or conceptual evidence; association is not treatment effect.
38213490 Cost-effectiveness of screening for chronic kidney disease: evidence and gaps. (van Mil 2024, PMID 38213490) Guideline or commentary; recommendation evidence depends on its review.
39137037 Screening for chronic kidney disease: change of perspective and novel developments. (van Mil 2024, PMID 39137037) Observational or conceptual evidence; association is not treatment effect.
38186904 Cost-effectiveness of screening for CKD in the general adult population: systematic review. (Yeo 2024, PMID 38186904) Synthesis; heterogeneity and included-study definitions constrain transport.
40227684 Balancing Efficiency and Equity in Population-Wide CKD Screening. (Cusick 2025, PMID 40227684) Modelled projection; the estimate follows from the model inputs and assumptions, not from observed randomized follow-up.
37403003 Chronic kidney disease of unknown aetiology: a global review. (Rao 2023, PMID 37403003) Observational or conceptual evidence; association is not treatment effect.
33116757 Mesoamerican Nephropathy: What We Know so Far. (Sanchez Polo 2020, PMID 33116757) Observational or conceptual evidence; association is not treatment effect.
18161745 Cellular and molecular mechanisms of fibrosis. (Wynn 2008, PMID 18161745) Observational or conceptual evidence; association is not treatment effect.
7246778 Hyperfiltration in remnant nephrons: a potentially adverse response to renal ablation. (Hostetter 1981, PMID 7246778) Observational or conceptual evidence; association is not treatment effect.
24522492 Relative risks of CKD for mortality and end-stage renal disease across races are similar. (Wen 2014, PMID 24522492) Observational or conceptual evidence; association is not treatment effect.

What can and cannot be concluded

  • Risk associations do not by themselves establish that changing the marker changes risk.
  • A relative effect must be paired with baseline risk, follow-up and the exact endpoint.
  • Albuminuria, acute eGFR change, chronic eGFR slope and kidney failure are not interchangeable.
  • Subgroup consistency is not evidence that every subgroup had adequate power.
  • Guideline recommendations combine evidence with values, feasibility, cost and service capacity.
  • Older adults require competing-mortality and treatment-burden framing.
  • Dialysis and transplantation comparisons are vulnerable to eligibility and immortal-time bias.
  • Modelled lifetime benefit is not a randomized observed benefit.
  • A biochemical response without a patient-important outcome remains a surrogate result.
  • This page is research synthesis, not individualized medical advice.

Research-design checklist

  • Define CKD cause, G category, A category and chronicity at baseline.
  • Report the creatinine or cystatin C equation and laboratory calibration.
  • Prespecify acute and chronic eGFR slopes when haemodynamic effects are expected.
  • Keep sustained GFR decline, kidney failure and replacement therapy separable.
  • Report absolute event risks, follow-up and confidence intervals with relative effects.
  • Treat death as a competing event where it can preclude kidney failure.
  • Measure hyperkalaemia, acute kidney injury and treatment discontinuation consistently.
  • Include symptoms, function, life participation and treatment burden.
  • Describe background RAS, SGLT2, MRA and GLP-1 therapy explicitly.
  • Prespecify albuminuria and cause strata without over-reading underpowered interactions.
  • Record screening, prescribing, persistence and monitoring as separate implementation steps.
  • Report representation, access and affordability variables needed for equity analysis.

Open questions

  • How many repeat measurements, at what interval, minimise misclassification at each G–A cell without imposing impractical testing burden? KDIGO requires persistence over three months but does not specify a sampling design (KDIGO CKD Work Group 2024, PMID 38490803).
  • Which creatinine–cystatin C discordance phenotypes carry information beyond the variables already in the equation? The combined equation is more accurate overall, but discordance has not been shown to change dosing or prognosis independently (Inker 2021, PMID 34554658). → OQ-11
  • Should drug labels be re-derived against indexed eGFR rather than the creatinine-clearance methods many were developed with? No trial has compared dosing conventions on clinical outcomes.

  • Do the G-category boundaries mark anything biological, or only convenient risk strata? Risk is continuous across both axes (CKD Prognosis Consortium 2023, PMID 37787795).

  • Should the eGFR threshold defining CKD vary with age? Age-adapted criteria removed 72,703 mostly older people whose 5-year kidney-failure risk was ≤0.12% and whose death risk exceeded it 69–935-fold (Liu 2021, PMID 34459844), yet no trial has randomized a diagnostic threshold and the label's net harm has never been measured against the benefit of earlier protective therapy (Hundemer 2024, PMID 38411155).

  • Does the EKFC population-specific Q-value reintroduce a population adjustment of the kind race-free reform removed, or is a laboratory-derived median normal creatinine categorically different (Delanaye 2024, PMID 38101514)?
  • What causes the mortality excess of large creatinine–cystatin C discordance — unmeasured GFR loss, low muscle mass, or the non-GFR determinants of cystatin C (Estrella 2025, PMID 41202182) (McCoy 2025, PMID 40373998)?
  • Is albuminuria change a strong enough surrogate to license regulatory approval on its own? The pooled R² of 0.66 has a credible interval of 0.06–0.98 across 48 trials (Heerspink 2026, PMID 41198855).

References

  1. KDIGO CKD Work Group et al. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. 2024;105(4S):S117-S314. PMID 38490803
  2. Inker et al. New Creatinine- and Cystatin C-Based Equations to Estimate GFR without Race. N Engl J Med. 2021;385(19):1737-1749. PMID 34554658
  3. CKD Prognosis Consortium et al. Estimated GFR, Albuminuria, and Adverse Outcomes: individual-participant data meta-analysis. JAMA. 2023;330(13):1266-1277. PMID 37787795
  4. Tangri et al. Multinational assessment of equations predicting kidney failure. JAMA. 2016;315(2):164-174. PMID 26757465
  5. Levin et al. Executive summary of the KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease: known knowns and known unknowns. Kidney Int. 2024;105(4):684-701. PMID 38519239
  6. GBD CKD Collaboration et al. Global, regional, and national burden of chronic kidney disease, 1990-2017. Lancet. 2020;395(10225):709-733. PMID 32061315
  7. Cirillo et al. A population-based approach for the definition of chronic kidney disease: CKD Prognosis Consortium. J Nephrol. 2012;25(1):7-12. PMID 22038337
  8. Matsushita et al. Cohort profile: the chronic kidney disease prognosis consortium. Int J Epidemiol. 2013;42(6):1660-1668. PMID 23243116
  9. Inker et al. Relationship of Estimated GFR and Albuminuria to Concurrent Laboratory Abnormalities. Am J Kidney Dis. 2019;73(2):206-217. PMID 30348535
  10. Delgado et al. A Unifying Approach for GFR Estimation: Recommendations of the NKF-ASN Task Force on Reassessing the Inclusion of Race in Diagnosing Kidney Disease. Am J Kidney Dis. 2022;79(2):268-288.e1. PMID 34563581
  11. Grams et al. Kidney Failure Risk Equation evaluation with novel inputs in 59 cohorts. J Am Soc Nephrol. 2023;34(3):482-494. PMID 36857500
  12. Matsushita et al. eGFR and albuminuria for prediction of cardiovascular outcomes: individual-participant meta-analysis. Lancet Diabetes Endocrinol. 2015;3(7):514-525. PMID 26028594
  13. Heerspink et al. Dapagliflozin in Patients with Chronic Kidney Disease. N Engl J Med. 2020;383(15):1436-1446. PMID 32970396
  14. EMPA-KIDNEY Collaborative Group et al. Empagliflozin in Patients with Chronic Kidney Disease. N Engl J Med. 2023;388(2):117-127. PMID 36331190
  15. Perkovic et al. Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy. N Engl J Med. 2019;380(24):2295-2306. PMID 30990260
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