Abstract 15701: Relationship Between Cardiac Biomarkers and Major Adverse Cardiovascular Events in DECLARE-TIMI 58
Bibliographic record
Abstract
Introduction: Biomarkers of hemodynamic stress and myocardial injury are associated with the risk of CV death & heart failure in patients with atherosclerotic vascular disease (ASCVD). Here we explore the association between cardiac biomarkers and ASCVD outcomes in patients with type 2 diabetes (T2DM). Methods: This was a nested biomarker study in DECLARETIMI 58, a randomized, blinded, placebo-controlled trial of dapagliflozin in T2DM and either multiple risk factors (MRF, ~60%) or established ASCVD (~40%). The relationship between baseline NT-proBNP and hsTnT levels (TIMI Biomarker Laboratory, n=14,565) and the composite of myocardial infarction, ischemic stroke, and CV death (MACE), was modeled within the placebo arm using Cox models adjusted for age, sex, race, smoking, baseline eGFR, BMI, T2DM duration, insulin use, history of CAD, MI, ischemic stroke, PAD, HF, dyslipidemia & hypertension. Interaction testing was applied to assess the effect of dapagliflozin according to baseline biomarker value. Results: NT-proBNP and hsTnT were significantly associated with MACE (Adjusted hazard ratio (aHR) per 1-SD in log-transformed biomarker, NT-proBNP: aHR 1.62; hsTnT aHR 1.59). The magnitude of the relationship was similar in patients with ASCVD (NT-proBNP aHR 1.60; hsTnT aHR 1.62) and MRF (NT-proBNP aHR 1.62; hsTnT: aHR 1.51) [Fig A] . Moreover, both biomarkers remained independently associated with MACE when combined in the multivariable model (NT-proBNP aHR 1.46, hsTnT aHR 1.39). The risk of MACE by baseline biomarker level and stratified by treatment arm is shown in Fig B. Conclusions: In patients with T2DM both with and without ASCVD, higher baseline NT-proBNP or hsTnT levels identified patients at increased risk of MACE. The difference in MACE rates between dapagliflozin and placebo tended to be more pronounced in ASCVD patients with higher baseline or NT-proBNP or hsTnT levels.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".