Estimated GFR in the Korean and US Asian Populations Using the 2021 Creatinine-Based GFR Estimating Equation Without Race
Bibliographic record
Abstract
Rationale & Objective In 2021, the new Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) updated the creatinine-based estimated glomerular filtration rate (eGFR) equation and removed the coefficient for race. The development and validation of this equation involved binarizing race into African American and non-African American, involving few Asian participants. This study aimed to examine the difference between the 2021 equation and the previous 2009 equation on CKD prevalence estimates in 2 Asian populations. Study Design Observational study using 2 national surveys. Setting & Participants Participants from the 2019 Korea National Health and Nutrition Survey and participants self-reported as Asian from the 2011-2020 US National Health and Nutrition Survey. Exposure eGFR using 2009 and 2021 CKD-EPI creatinine equation. Outcomes Prevalence of CKD (eGFR<60mL/min/1.73m 2 or urine albumin-creatinine ratio≥30mg/g). Analytical Approach Sampling-weighted prevalence estimated using the 2009 and 2021 equations as well as the percentage of individuals with CKD G3+using the 2009 equation being reclassified as not having CKD G3+using the 2021 equation. Results The prevalence of CKD estimated using the 2021 equation was 9.75% (95% confidence intervals [CI], 8.80-10.80%) in Koreans and 11.60% (95% CI, 10.23-13.13%) in US Asians. The prevalence of CKD estimated using the 2021 equation was slightly lower than that using the 2009 equation in both Korean and US Asian populations by 0.63% (95% CI, 0.44-0.90%) and 0.84% (95% CI, 0.52-1.34%), respectively. Furthermore, 32.8% and 30.2% of Koreans and US Asians with CKD G3-5, respectively, estimated using the 2009 equation were reclassified as not having CKD G3-5 when the eGFR was calculated using the 2021 equation. Limitations Measured GFR was not available. Conclusions Use of the 2021 CKD-EPI creatinine equation leads to a small decrease in CKD prevalence in both Korean and US Asian populations, and of similar magnitude, resulting in significant reclassification among those originally classified as having CKD G3+. Plain-Language Summary The 2009 serum creatinine-based kidney function estimating equation used demographic information including race. Because race is a social construct, race was eliminated in the new equation developed in 2021. As race was categorized into African American and non-African American during its development, this study examined the impact of the 2021 equation in 2 distinct Asian populations (Koreans and US Asians) using 2 national datasets. We found that the prevalence of chronic kidney disease (CKD) estimated using the 2021 equation was slightly lower that estimated using the 2009 equation in both Koreans and US Asians. Approximately one-third of people with CKD estimated using the 2009 equation were reclassified as not having CKD estimated using the 2021 equation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".