Prognosis in patients with prior myocardial infarction and PEGASUS-TIMI 54 criteria in the CLARIFY registry
Bibliographic record
Abstract
Abstract Background/Introduction The PEGASUS-TIMI 54 trial showed that prolonged treatment with ticagrelor reduces the cumulative occurrence of ischemic adverse events. CLARIFY is the biggest real life registry on chronic coronary syndrome. Purpose - To evaluate the percentage of patients eligible for long-term ticagrelor therapy in the CLARIFY registry. – To compare the outcome of this subgroup of patients with those with PEGASUS exclusion criteria or without PEGASUS inclusion criteria. Methods Within the CLARIFY population, we selected post MI patients and we excluded those with missing info (post MI evaluable population). Then, we divided patients into 3 groups: excluded (meeting PEGASUS exclusion criteria, namely use of P2Y12 receptor antagonists or chronic oral anticoagulant, any stroke, coronary-artery bypass grafting in the past 5 years); eligible (meeting PEGASUS high-risk inclusion criteria, namely age≥65 years; diabetes; multivessel disease; creatinine clearance <60 ml/min) and ineligible (not meeting PEGASUS high-risk inclusion criteria). We therefore compared the ischemic (CV death, MI and stroke) and bleeding (major bleeding) outcome of the 3 groups adjusting for age, sex, smoking and geographical region. Results Among the 11811 post-MI evaluable patients, 4706 (39.8%) were included in the eligible group, 5715 (48.4%) in the excluded group, and 1390 in the ineligible group (11.8%). Both the ischemic and bleeding endpoints were significantly different among the 3 groups with the excluded patients with the worst and ineligible patients with the best outcome (see table). The same trend was shown for CV death, while the occurrence of MI was not significantly different among the 3 groups. In the eligible group, the ratio between ischemic and bleeding events was 6:1, whereas between CV death and major bleeding was 3.5:1. Conclusions Around 40% of CLARIFY post-MI patients could benefit from prolonged ticagrelor therapy. In this group of patients, ischemic risk seems to be higher than the bleeding one. Ischemic & bleeding risk in the 3 groups Funding Acknowledgement Type of funding source: Private company. Main funding source(s): CLARIFY registry was funded by Servier
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".