Effect of Reconstituted Human Apolipoprotein A-I on Recurrent Ischemic Events in Survivors of Acute MI
Bibliographic record
Abstract
BACKGROUND: The AEGIS-II trial hypothesized that CSL112, an intravenous formulation of human apoA-I, would lower the risk of plaque disruption, decreasing the risk of recurrent events such as myocardial infarction (MI) among high-risk patients with MI. OBJECTIVES: This exploratory analysis evaluates the effect of CSL112 therapy on the incidence of cardiovascular (CV) death and recurrent MI. METHODS: The AEGIS-II trial was an international, multicenter, randomized, double-blind, placebo-controlled trial that randomized 18,219 high-risk acute MI patients to 4 weekly infusions of apoA-I (6 g CSL112) or placebo. RESULTS: The incidence of the composite of CV death and type 1 MI was 11% to 16% lower in the CSL112 group over the study period (HR: 0.84; 95% CI: 0.7-1.0; P = 0.056 at day 90; HR: 0.86; 95% CI: 0.74-0.99; P = 0.048 at day 180; and HR: 0.89; 95% CI: 0.79-1.01; P = 0.07 at day 365). Similarly, the incidence of CV death or any MI was numerically lower in CSL112-treated patients throughout the follow-up period (HR: 0.92; 95% CI: 0.80-1.05 at day 90, HR: 0.89; 95% CI: 0.79-0.996 at day 180, HR: 0.91; 95% CI: 0.83-1.01 at day 365). The effect of CSL112 treatment on MI was predominantly observed for type 1 MI and type 4b (MI due to stent thrombosis). CONCLUSIONS: Although CSL112 did not significantly reduce the occurrence of the primary study endpoints, patients treated with CSL112 infusions had numerically lower rates of CV death and MI, type-1 MI, and stent thrombosis-related MI compared with placebo. These findings could suggest a role of apoA-I in reducing subsequent plaque disruption events via enhanced cholesterol efflux. Further prospective data would be needed to confirm these observations.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".