Future myocardial infarction after an acute coronary syndrome and pharmacogenetic response to dalcetrapib
Bibliographic record
Abstract
BACKGROUND: Acute coronary syndrome (ACS) survivors have heightened risk for subsequent cardiovascular events. METHODS: All baseline characteristics collected in both the Dal-Outcomes and Dal-GenE trials were considered as potential risk markers. A prediction index for subsequent fatal and non-fatal myocardial infarction (MI) following ACS was developed using Cox proportional hazards modeling on data from Dal-Outcomes placebo patients (n=7086). This prediction index was then applied in all Dal-GenE participants (n=5989) to determine whether the reduction in MI observed with dalcetrapib (versus placebo) in patients with the AA genotype at rs1967309 in the ADCY9 gene remained significant, independent of the other markers integrated into the prediction index. RESULTS: Of the 36 baseline variables considered as potential risk markers, 18 contributed to the prediction index with a Harrell's C-index of 0.72 (95% CI, 0.69-0.75) in Dal-Outcomes placebo patients. Prior history of coronary events, LDL-C, blood pressure, A1c, hs-CRP, smoking and age were contributors. The prediction index was strongly predictive when applied to the 5989 AA genotype patients from Dal-GenE, with a HR for MI of 1.92 (95%CI: 1.78-2.08) for each SD increase in score. When adjusting for the prediction index, the HR for dalcetrapib versus placebo was 0.77 (95% CI, 0.63-0.94) in Dal-GenE. CONCLUSION: Despite guideline directed therapy following ACS, history of prior coronary events and on-treatment LDL-C, A1c, hs-CRP and blood pressure remain determinants of future MI. In the Dal-GenE AA genotype patients, dalcetrapib reduced the rate of MI, independently of those variables. The Dal-GenE 2 trial is designed to confirm this pharmacogenetic hypothesis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".