Abstract 3739: The Influence of Albuminuria on Mortality in Patients with Stable Coronary Artery Disease
Bibliographic record
Abstract
Background: Patients with chronic kidney disease are at increased risk for cardiovascular morbidity and mortality. We assessed the association between albuminuria and death or cardiovascular events among patients with stable coronary disease. Methods: We studied patients enrolled in the Prevention of Events with an ACE Inhibitor (PEACE) trial, in which patients with chronic stable coronary disease and preserved systolic function were randomized to trandolapril or placebo and followed for a median of 4.8 years. The urinary albumin to creatinine ratio (ACR) assessed in a core laboratory in 2977 patients at baseline and in 1339 patients at follow-up (mean 34 months) was related to estimated glomerular filtration rate (eGFR) and outcomes. Results: The majority of patients (73%) had a baseline albumin/creatinine ratio within the normal range. Independent of the eGFR and other baseline covariates, a higher albumin/creatinine ratio even within the normal range was associated with increased risks for all-cause mortality (p < 0.001) and cardiovascular death (p = 0.01). The effect of trandolapril therapy on outcomes was not significantly modified by the level of albuminuria. Nevertheless, trandolapril therapy was associated with a significantly lower mean follow-up ACR (12.5 ug/mg vs 14.6 ug/mg, p = 0.0002), after adjusting for baseline ACR, time between collections and other covariates. An increase in ACR over time was associated with increased risk of cardiovascular death (HR per log ACR 1.74, 95% confidence intervals 1.08–2.82). Conclusions: Albuminuria, even in low levels within the normal range, is an independent predictor of cardiovascular and all-cause mortality.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".