Relationship between Metabolic Acidosis and Chronic Kidney Disease Progression across Racial and Ethnic Groups: An Observational, Retrospective Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Metabolic acidosis is associated with chronic kidney disease (CKD) progression and mortality, but the association of race/ethnicity with incident metabolic acidosis and/or its adverse outcomes in patients with CKD is unknown. METHODS: We used deidentified medical records data (2007-2019) to generate a cohort of 136,067 patients with nondialysis-dependent CKD stages 3-5 and ≥2 years' postindex data or death within 2 years. We grouped participants into those with and without metabolic acidosis (serum bicarbonate 12 to <22 mEq/L vs. 22 to <30 mEq/L) as Asian, Black, Hispanic, non-Hispanic White individuals, or unknown. Cox proportional hazards models examined factors associated with (1) incident metabolic acidosis; and (2) time to the composite outcome of death, dialysis, transplant, or ≥40% decline from baseline eGFR (DD40) within each race/ethnic group. RESULTS: Metabolic acidosis incidence was higher for Asian, Black, and Hispanic versus non-Hispanic White individuals (p values for adjusted hazard ratios [HR] all <0.001), but this higher hazard was mitigated in all groups with increasing community education. During the median follow-up of 4.2 years, 47,032 of 136,607 (34.6%) experienced a DD40 event. There was an independent association of metabolic acidosis with DD40 within each race/ethnic group. Adjusted HRs (95% confidence interval) for DD40 were 1.806 (1.312, 2.486), 1.420 (1.313, 1.536), 1.409 (1.211, 1.641), and 1.561 (1.498, 1.626) in Asian, Black, Hispanic, and non-Hispanic White groups, respectively (all p < 0.0001), for metabolic acidosis versus normal serum bicarbonate. DISCUSSION/CONCLUSION: The higher incidence of metabolic acidosis observed in Asian, Black, and Hispanic individuals was mitigated by residing in higher education zip codes. Once established, metabolic acidosis was independently associated with DD40 in patients with CKD in all racial/ethnic groups examined.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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 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".