Effect of alirocumab on incidence of atrial fibrillation after acute coronary syndromes: insights from ODYSSEY OUTCOMES
Bibliographic record
Abstract
Abstract Background Atrial fibrillation (AF) is a marker of risk in patients presenting with acute coronary syndromes (ACS). The potential effect of inhibiting proprotein convertase subtilisin/kexin type 9 (PCSK9) on the incidence of AF is unknown. Methods The ODYSSEY OUTCOMES trial compared randomized treatment with the PCSK9 inhibitor alirocumab or placebo in patients with recent ACS and residual dyslipidaemia despite optimal statin therapy. The current analysis determined: 1) whether alirocumab treatment influenced incident AF; 2) whether a history of AF influenced the risk of major adverse cardiovascular events (MACE); and 3) whether there was interaction between AF at baseline and randomized treatment on MACE. AF was determined from the medical history and investigator reports of adverse events. Results Of 18,924 participants, 662 (3.5%) had a history of AF at randomization and 18,262 (96.5%) had no history of AF. Of the latter category, 499 (2.7%) had incident AF. Older age, randomization in South America or Eastern Europe, history of heart failure or myocardial infarction, and higher body mass index were factors associated with incident AF. Treatment with alirocumab or placebo did not influence incident AF (2.2% vs 2.6%, respectively; hazard ratio 0.90, 95% confidence interval 0.75–1.08; Figure). Patients with a history of AF had a greater burden of comorbidities, including cerebrovascular disease, peripheral artery disease, hypertension and heart failure; they also had higher rates of MACE (Table). There was no significant interaction between AF and randomized treatment on risk of MACE (P interaction=0.78) Conclusions Although treatment with alirocumab did not significantly modify the risk of incident AF after ACS in this analysis, future studies with more sensitive and systematic methods of ascertainment may be warranted. History of AF is a strong predictor of risk of recurrent MACE after ACS. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Sanofi, Regeneron Pharmaceuticals, Inc
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".