Apabetalone and hospitalization for heart failure in patients following an acute coronary syndrome: a prespecified analysis of the BETonMACE study
Bibliographic record
Abstract
BACKGROUND: Patients with diabetes and acute coronary syndrome (ACS) are at high risk for subsequent heart failure. Apabetalone is a selective inhibitor of bromodomain and extra-terminal (BET) proteins, epigenetic regulators of gene expression. Preclinical data suggest that apabetalone exerts favorable effects on pathways related to myocardial structure and function and therefore could impact subsequent heart failure events. The effect of apabetalone on heart failure events after an ACS is not currently known. METHODS: The phase 3 BETonMACE trial was a double-blind, randomized comparison of apabetalone versus placebo on the incidence of major adverse cardiovascular events (MACE) in 2425 patients with a recent ACS and diabetes. This prespecified secondary analysis investigated the impact of apabetalone on hospitalization for congestive heart failure, not previously studied. RESULTS: Patients (age 62 years, 74.4% males, 90% high-intensity statin use, LDL-C 70.3 mg/dL, HDL-C 33.3 mg/dL and HbA1c 7.3%) were followed for an average 26 months. Apabetalone treated patients experienced the nominal finding of a lower rate of first hospitalization for heart failure (2.4% vs. 4.0%, HR 0.59 [95%CI 0.38-0.94], P = 0.03), total number of hospitalizations for heart failure (35 vs. 70, HR 0.47 [95%CI 0.27-0.83], P = 0.01) and the combination of cardiovascular death or hospitalization for heart failure (5.7% vs. 7.8%, HR 0.72 [95%CI 0.53-0.98], P = 0.04). CONCLUSION: Apabetalone treatment was associated with fewer hospitalizations for heart failure in patients with type 2 diabetes and recent ACS. Future studies are warranted to define the potential for BET inhibition with apabetalone to prevent heart failure in patients with diabetes and ACS.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".