Trade-off of myocardial infarction vs. bleeding types on mortality after acute coronary syndrome: lessons from the Thrombin Receptor Antagonist for Clinical Event Reduction in Acute Coronary Syndrome (TRACER) randomized trial
Bibliographic record
Abstract
AIMS: Dual antiplatelet therapy reduces non-fatal ischaemic events after acute coronary syndrome (ACS) but increases bleeding to a similar extent. We sought to determine the prognostic impact of myocardial infarction (MI) vs. bleeding during an extended follow-up period to gain insight into the trade-off between efficacy and safety among patients after ACS. METHODS AND RESULTS: In 12 944 patients with non-ST-segment elevation ACS from the Thrombin Receptor Antagonist for Clinical Event Reduction in Acute Coronary Syndrome (TRACER) trial, we investigated the relative impact of MI and bleeding occurring >30 days post-ACS and subsequent all-cause mortality. Bleeding was graded according to Bleeding Academic Research Consortium (BARC) criteria. MI was associated with a five-fold increase in mortality. BARC type 2 and 3, but not type 1, bleeding had a significant impact on mortality. MI was associated with a greater risk of mortality compared with BARC 2 [relative risk (RR) 3.5; 95% confidence interval (CI) 2.08-4.77; P < 0.001] and BARC 3a bleeding (RR 2.23; 95% CI 1.36-3.64; P = 0.001), and a risk similar to BARC 3b bleeding (RR 1.37; 95% CI 0.81-2.30; P = 0.242). Risk of death after MI was significantly lower than after BARC 3c bleeding (RR 0.22; 95% CI 0.13-0.36; P < 0.001). MI and bleeding had similar time-associations with mortality, which remained significant for several months, still being higher early after the event. CONCLUSION: In patients treated with antiplatelet therapy after ACS, both MI and bleeding significantly impacted mortality with similar time-dependency. Although BARC 2 and 3a bleeding were less prognostic for death than MI, the risk of mortality was equivalent between BARC 3b bleeding and MI, and was higher following BARC 3c bleeding.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".