Abstract 11594: Reduction in Subtypes and Sizes of Myocardial Infarction With Ticagrelor in PEGASUS-TIMI 54
Bibliographic record
Abstract
Objective: Ticagrelor reduced the risk of CV death, MI or stroke (MACE) in stable patients with prior MI in PEGASUS-TIMI 54. We investigated the efficacy of ticagrelor in reducing different subtypes and sizes of MI. Methods: MIs were adjudicated by the blinded TIMI Clinical Events Committee with definitions consistent with the Third Universal Definition of MI. Each MI was categorized by subtype and fold elevation of peak troponin (Tn) over the upper limit of normal (ULN). Results for both doses of ticagrelor were pooled in comparison to placebo. Results: A total of 1042 MIs occurred in the 21,162 randomized patients over a median follow up of 33 months. The majority (76%) of the MIs were spontaneous (Type 1), with demand MI (Type 2) and PCI-related (Type 4) accounting for 13% and 10% respectively (Figure Left); sudden death MI (Type 3) and CABG-related MI (Type 5) each accounted for <1%. Using fold elevation in Tn, half of MIs (520, 50%) had a peak Tn ≥10x ULN and 21% of MIs (220) had a peak Tn ≥100x ULN (Figure Right). A total of 21% (224) were STEMI. Overall ticagrelor reduced MI at 3 yrs (4.47% vs 5.25%, HR 0.83, 95% CI 0.72 - 0.95, p=0.0055). The benefit was highly consistent among the different subtypes of MI (Figure left) and with increasing size of MI by fold elevation of Tn (Figure right) and for STEMI (HR 0.60, 95% CI 0.46 - 0.78, p=0.0002). Conclusion: In stable outpatients with a history of MI the majority of recurrent MI events are spontaneous and associated with a high biomarker elevation. Ticagrelor significantly reduces the incidence of MI consistently among different subtypes and biomarker sizes, with the greatest absolute reduction in spontaneous MIs, large MIs, and STEMI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.006 | 0.001 |
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".