Metabolic Acidosis and Cardiovascular Disease in CKD
Bibliographic record
Abstract
RATIONALE & OBJECTIVE: Metabolic acidosis related to chronic kidney disease (CKD) is associated with an accelerated decline in glomerular filtration rate (GFR) and the development of end-stage kidney disease. Whether metabolic acidosis is associated with cardiovascular (CV) events in patients with CKD is unclear. STUDY DESIGN: Retrospective cohort study. SETTING & PARTICIPANTS: . Patients with metabolic acidosis (serum bicarbonate 12 to <22 mEq/L) or normal serum bicarbonate (22‒29 mEq/L) at baseline were identified by 2 consecutive measurements 28‒365 days apart. PREDICTOR: Serum bicarbonate as a continuous variable. OUTCOME: Primary outcome was a composite of major adverse cardiovascular events (MACE+). Secondary outcomes included individual components of the composite outcome. ANALYTICAL APPROACH: Cox proportional hazards models to evaluate the association between 1-mEq/L increments in serum bicarbonate and MACE+. RESULTS: A total of 51,558 patients were evaluated, 34% had metabolic acidosis. The median follow-up period was 3.9-4.5 years, depending on the outcome assessed. The adjusted hazard ratio (HR) for MACE+ was 0.964 (95% CI, 0.961-0.968). For the individual components of incident heart failure (HF), stroke, myocardial infarction (MI), and CV death, HRs were 0.98 (95% CI, 0.97-0.98), 0.98 (95% CI, 0.97-0.99), 0.96 (95% CI, 0.96-0.97), and 0.94 (95% CI, 0.93-0.94), respectively, for every 1-mEq/L increase in serum bicarbonate. LIMITATIONS: Possible residual confounding. CONCLUSIONS: Metabolic acidosis in CKD is associated with an increased risk of MACE+ as well as the individual components of incident HF, stroke, MI, and CV death. Randomized controlled trials evaluating treatments for the correction of metabolic acidosis in CKD to prevent CV events are needed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".