Factor V Leiden and the Risk of Bleeding in Patients With Acute Coronary Syndromes Treated With Antiplatelet Therapy: Pooled Analysis of 3 Randomized Clinical Trials
Bibliographic record
Abstract
Background Whether factor V Leiden is associated with lower bleeding risk in patients with acute coronary syndromes using (dual) antiplatelet therapy has yet to be investigated. Methods and Results We pooled data from 3 randomized clinical trials, conducted in patients with acute coronary syndromes, with adjudicated bleeding outcomes. Cox regression models were used to obtain overall and cause‐specific hazard ratios (HRs) to account for competing risk of atherothrombotic outcomes (ie, composite of ischemic stroke, myocardial infarction, and cardiovascular death) in each study. Estimates from the individual studies were pooled using fixed effect meta‐analysis. The 3 studies combined included 17 623 patients of whom 969 (5.5%) were either heterozygous or homozygous (n=23) carriers of factor V Leiden. During 1 year of follow‐up, a total of 1289 (7.3%) patients developed major (n=559) or minor bleeding. Factor V Leiden was associated with a lower risk of combined major and minor bleeding (adjusted cause‐specific HR, 0.75; 95% CI, 0.56–1.00; P =0.046; I 2 =0%) but a comparable risk of major bleeding (adjusted cause‐specific HR, 0.93; 95% CI, 0.62–1.39; P =0.73; I 2 =0%). Adjusted pooled cause‐specific HRs for the association of factor V Leiden with atherothrombotic events alone and in combination with bleeding events were 0.75 (95% CI, 0.55–1.02; P =0.06; I 2 =0%) and 0.75 (95% CI, 0.61–0.92; P =0.007; I 2 =0%), respectively. Conclusions Given that the lower risk of bleeding conferred by factor V Leiden was not counterbalanced by a higher risk of atherothrombotic events, these findings warrant future assessment for personalized medicine such as selecting patients for extended or intensive antiplatelet therapy.
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 | 0.021 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".