Alirocumab after acute coronary syndrome in patients with a history of heart failure
Bibliographic record
Abstract
AIMS: Patients with heart failure (HF) have not been shown to benefit from statins. In a post hoc analysis, we evaluated outcomes in ODYSSEY OUTCOMES in patients with vs. without a history of HF randomized to the proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor alirocumab or placebo. METHODS AND RESULTS: Among 18 924 patients with recent acute coronary syndrome (ACS) receiving intensive or maximum-tolerated statin treatment, the primary outcome of major adverse cardiovascular events (MACE) was compared in patients with or without a history of HF. The pre-specified secondary outcome of hospitalization for HF was also analysed. Overall, 2815 (14.9%) patients had a history of HF. Alirocumab reduced low-density lipoprotein cholesterol and lipoprotein(a) similarly in patients with or without HF. Overall, alirocumab reduced MACE compared with placebo [hazard ratio (HR): 0.85; 95% confidence interval (CI): 0.78-0.93; P = 0.0001]. This effect was observed among patients without a history of HF (HR: 0.78; 95% CI: 0.70-0.86; P < 0.0001), but not in those with a history of HF (HR: 1.17; 95% CI: 0.97-1.40; P = 0.10) (Pinteraction = 0.0001). Alirocumab did not reduce hospitalization for HF, overall or in patients with or without prior HF. CONCLUSION: Alirocumab reduced MACE in patients without a history of HF but not in patients with a history of HF. Alirocumab did not reduce hospitalizations for HF in either group. Patients with a history of HF are a high-risk group that does not appear to benefit from PCSK9 inhibition after ACS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".