Abstract 16820: Albuminuria and Outcomes in Patients With Non-ST-segment Elevation Acute Coronary Syndromes: Results From the TRACER Trial
Bibliographic record
Abstract
Introduction: Patients with acute coronary syndromes (ACS) and kidney dysfunction are at increased risk of recurrent cardiovascular (CV) adverse events. Urinary albumin excretion (albuminuria) has been independently associated with CV outcomes. However, in a high-risk population of patients with ACS, the additional prognostic information of albuminuria to estimated glomerular filtration rate (eGFR) is less clear. Hypothesis: We studied the relationship between albuminuria and CV death and myocardial infarction (MI) in 12,944 patients with non-ST-segment elevation (NSTE)-ACS. Methods: Albuminuria was collected routinely at baseline by dipsticks and stratified into no/trace albuminuria, microalbuminuria (≥30mg/dL), and macroalbuminuria (≥300mg/dL). Baseline serum creatinine was obtained. Kaplan-Meier event rates for CV death, and the combination of CV death or MI were calculated. Multivariable adjusted Cox regression models, with baseline characteristics and biomarkers, and the addition of eGFR (Chronic Kidney Disease - Epidemiology (CKD-EPI) equation), were assessed. Results: Levels of albuminuria were available in 9736 patients (75.2%), and both serum creatinine and albuminuria measurements in 9473 (73.2%) patients. More patients with macroalbuminuria, compared with patients with no albuminuria, had diabetes (66% vs. 27%) or hypertension (86% vs. 68%) There was a significant increased risk in CV events with macroalbuminuria, which was significant in the adjusted model but did not remain significant when eGFR was added to the model (Table). Conclusions: High-risk patients with NSTE-ACS and albuminuria at presentation have an increased risk of adverse CV outcomes. However, in the present cohort, albuminuria did not provide additional independent prognostic value to eGFR.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".