Implications of Implementing the 2021 CKD-EPI Equation Without Race on Managing Patients With Kidney Disease in British Columbia, Canada
Bibliographic record
Abstract
Introduction: We investigated the implications of implementing race-free Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2021 equation among real-world patients with chronic kidney disease (CKD) from British Columbia (BC), Canada. Methods: This study included nondialysis-dependent patients with CKD aged ≥19 years who were registered in the Patient Records and Outcome Management Information System (PROMIS) as of March 31, 2016 (index date) with ≥1 serum creatinine measurement within 1 year before the index date. Patients with a history of kidney transplantation before the index date were excluded. CKD-EPI 2021 versus 2009 equation was the exposure variable. Difference in mean estimated glomerular filtration rate (eGFR) and number (%) of patients reclassified to a different eGFR category were estimated. We used Fine and Gray subdistribution hazard model to investigate the association between change in eGFR category and progression to kidney failure (incident maintenance dialysis or kidney transplantation) within 2 years. Results: higher. Variation was higher among males. Overall, ∼17% of the study sample were reclassified to a category with higher eGFR by 2021 equation (switchers). The highest proportion (28%) of patients were reclassified from G5 to G4. The risk of progressing to kidney failure was 22% less among switchers compared to nonswitchers; adjusted subdistribution hazard ratio (HR) (95% confidence interval [CI]) is 0.78 (0.65, 0.94). Conclusion: CKD-EPI 2021 equation appeared to provide higher eGFR compared to 2009 equation. This higher eGFR values appeared to be concordant with subsequent real-world CKD progression outcomes. Higher eGFR from the 2021 equation may have substantial clinical implications in both diagnosis as well as long-term care of patients with CKD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".