Effectiveness and safety of direct oral anticoagulants with antiplatelet agents in patients with venous thromboembolism: A multi‐database cohort study
Bibliographic record
Abstract
BACKGROUND: Patients with venous thromboembolism (VTE) often have comorbidities that require use of antiplatelets. However, evidence on the effects of concomitant use of direct oral anticoagulants (DOACs) and antiplatelets in this high-risk population is scarce. Our international, multi-database cohort study assessed the real-world effectiveness and safety of concomitant use of DOACs and antiplatelets among patients with VTE. METHODS: We assembled two population-based cohorts using administrative health care databases from Québec and Germany. We included patients with incident VTE who initiated treatment with a DOAC or a vitamin K antagonist (VKA), while being exposed to antiplatelets (acetylsalicylic acid, clopidogrel, ticagrelor, prasugrel, dipyridamole). The study period spanned from 2012 to 2016 (Québec) or 2019 (Germany). Concomitant use of DOACs and antiplatelets was compared with concomitant use of VKAs and antiplatelets, using inverse probability of treatment weighting to balance exposure groups. Cox proportional hazards models estimated site-specific hazard ratios (HRs) and 95% confidence intervals (CIs) of major bleeding, all-cause mortality (primary outcomes), and recurrent VTE (secondary outcome). Site-specific estimates were meta-analyzed using random-effects models. RESULTS: Overall, 4971 patients with VTE initiated concomitant use of a DOAC (n = 2289) or a VKA (n = 2682) and antiplatelets. Compared with concomitant use of VKAs and antiplatelets, concomitant use of DOACs and antiplatelets was associated with similar risks of major bleeding (HR, 0.81; 95% CI, 0.46-1.45), all-cause mortality (HR, 1.25; 95% CI, 0.87-1.79), and recurrent VTE (HR, 0.96; 95% CI, 0.40-2.27). CONCLUSIONS: Among patients with VTE using antiplatelets, there were no major differences in effectiveness and safety between DOACs and VKAs.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".