Potential Implications of 2021 CKD-EPI Equation in Patients with CKD from British Columbia, Canada
Bibliographic record
Abstract
Background: One in 10 British Columbians has kidney disease. The implications of implementing 2021 CKD-EPI equation in British Columbia (BC) is unknown. This was investigated in a population-level cohort of CKD patients from BC, Canada. Methods: CKD patients aged ≥19 years and registered in the “Provincial Renal Program” on March 31, 2023 (index date) were included. Patients needed to have ≥1 serum creatinine recorded within 1 year before index date. We excluded patients who received transplantation before index date. We calculated eGFR using CKD-EPI 2009 and 2021 equations, and estimated the mean difference in eGFRs and corresponding Kidney Failure Risk Equation (KFRE) 2-year risks by age and sex. We assessed the implications in two clinical aspects: (1) reclassification between eGFR categories (G1-G5) (2) reclassification between KDIGO risk categories (low, moderately increased, high and very high risk). Finally, we investigated patient characteristics among those who were reclassified in eGFR categories (switcher; yes/no). Results: Study sample included 16,037 patients, median age 74 years, 54% male. Compared to 2009 equation, eGFR calculated using 2021 equation was on average 1.80-2.60 ml/min higher in women and 2.86-3.33 ml/min higher in men. The 2021 equation downgraded the CKD severity with highest % of patients downgraded in G5 category (Fig.1). In KDIGO risk categorization, ˜4% of patients in the very high risk group were reclassified to a lower risk group. The switchers appeared to be older male, majority (˜43%) were in eGFR category G4 followed by 27% in G3b. KFRE 2-year risk score calculated using eGFR from 2021 equation was lower compared to that of estimated using eGFR from 2009 equation, median (IQR) in difference was -0.854 (-2.516, -0.258). Difference was larger in males. Conclusions: The eGFR calculated using CKD-EPI 2021 was higher compared to 2009 equation. A large number (˜17%) of patients currently under the care of nephrologists in BC would have categorically less severe CKD. The implications of this on resource utilization, care plans and outcomes are unknown. - CKD patients registered in Provincial Renal Program # Pts CKD-EPI 2021: eGFR category G1 G2 G3a G3b G4 G5 Total CKD-EPI 2009: eGFR category G1 754 0 0 0 0 0 754 G2 172 (14%) 1023 0 0 0 0 1195 G3a 0 310 (17%) 1492 0 0 0 1802 G3b 0 0 776 (16%) 3980 0 0 4756 G4 0 0 0 1166 (19%) 5124 0 6290 G5 0 0 0 0 278 (22%) 962 1240 Total 926 1333 2268 5146 5402 962 16037
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".