Apolipoprotein A-I infusions and cardiovascular outcomes in acute myocardial infarction according to baseline LDL-cholesterol levels: the AEGIS-II trial
Bibliographic record
Abstract
BACKGROUND AND AIMS: In the AEGIS-II trial (NCT03473223), CSL112, a human apolipoprotein A1 derived from plasma that increases cholesterol efflux capacity, did not significantly reduce the risk of the primary endpoint through 90 days vs. placebo after acute myocardial infarction (MI). Nevertheless, given the well-established relationship between higher low-density lipoprotein cholesterol (LDL-C) and plaque burden, as well as greater risk reductions seen with PCSK9 inhibitors in patients with baseline LDL-C ≥ 100 mg/dL on statin therapy, the efficacy of CSL112 may be influenced by baseline LDL-C. METHODS: Overall, 18 219 patients with acute MI, multivessel coronary artery disease, and additional risk factors were randomized to either four weekly infusions of 6 g CSL112 or placebo. This exploratory post-hoc analysis evaluated cardiovascular outcomes by baseline LDL-C in patients prescribed guideline-directed statin therapy at the time of randomization (n = 15 731). RESULTS: As baseline LDL-C increased, the risk of the primary endpoint at 90 days lowered in those treated with CSL112 compared with placebo. In patients with LDL-C ≥ 100 mg/dL at randomization, there was a significant risk reduction of cardiovascular death, MI, or stroke in the CSL112 vs. placebo group at 90, 180, and 365 days [hazard ratio .69 (.53-.90), .71 (.57-.88), and .78 (.65-.93)]. In contrast, there was no difference between treatment groups among those with LDL-C < 100 mg/dL at baseline. CONCLUSIONS: In this population, treatment with CSL112 compared to placebo was associated with a significantly lower risk of recurrent cardiovascular events among patients with a baseline LDL-C ≥ 100 mg/dL. Further studies need to confirm that CSL112 efficacy is influenced by baseline LDL-C.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".