FC078: Impact of Removing Race from the CKD-EPI Equation: Analysis of 1.6 Million Swedish Adults
Bibliographic record
Abstract
Abstract BACKGROUND AND AIMS The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) recently developed a novel creatinine-based eGFR equation without a race coefficient. While American nephrology societies recommend using this novel equation, its implications are unknown. METHOD We included 1.6 million adult individuals with routine outpatient serum creatinine testing during 2007–2018 in Stockholm, Sweden. First, we calculated reclassification across KDIGO eGFR categories when changing from the 2009 to 2021 CKD-EPI equation. Second, for both equations, the association between eGFR and (1) kidney failure with replacement therapy (KFRT), (2) all-cause mortality, (3) cardiovascular mortality and (4) major adverse cardiovascular events was estimated with Cox regression. Third, prognostic accuracy of both eGFR equations within the Kidney Failure Risk Equation was assessed with discrimination and calibration. RESULTS Compared with the 2009 equation, the 2021 equation yielded a higher eGFR by a median (IQR) of 3.9 (2.9–4.8) mL/min/1.73 m2, decreasing prevalence of CKD G3–G5 from 5.1 to 3.8%. The 2021 equation reclassified 9.9% of the total population and 36.2% of the CKD G3–G5 population to a less severe eGFR category. Individuals who were reclassified to less severe eGFR categories were older and therefore exhibited higher crude risks of all-cause/cardiovascular death and major adverse cardiovascular events, and lower risk of kidney replacement therapy compared with nonreclassified participants of similar eGFR. eGFR by both equations strongly predicted study outcomes, with similar discrimination and calibration for the Kidney Failure Risk Equation. CONCLUSION Implementing the 2021 CKD-EPI equation in predominantly white European populations raises eGFR by a modest amount (larger at older age and men) and shifts a major proportion of CKD patients to a higher eGFR category, with eGFR by both equations strongly predicting outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".