Oral anticoagulation in patients with left ventricular thrombus: a systematic review and meta-analysis
Bibliographic record
Abstract
AIMS: Direct oral anticoagulants (DOACs) are increasingly used off-label to treat patients with left ventricular thrombus (LVT). We analysed available meta-data comparing DOACs and vitamin K antagonists (VKAs) for efficacy and safety. METHODS: We conducted a systematic search and meta-analysis of observational and randomized data comparing DOACs vs. VKAs in patients with LVT. Endpoints of interest were stroke or systemic embolism, thrombus resolution, all-cause death, and a composite bleeding endpoint. Estimates were pooled using a random-effects model meta-analysis, and their robustness was investigated using sensitivity and influential analyses. RESULTS: We identified 22 articles (18 observational studies, 4 small randomized clinical trials) reporting on a total of 3587 patients (2489 VKA vs. 1098 DOAC therapy). The pooled estimates for stroke or systemic embolism [odds ratio (OR): 0.81; 95% confidence interval (CI): 0.57, 1.15] and thrombus resolution (OR: 1.12; 95% CI: 0.86, 1.46) were comparable, and there was low heterogeneity overall across the included studies. The use of DOACs was associated with lower odds of all-cause death (OR: 0.65; 95% CI: 0.46, 0.92) and a composite bleeding endpoint (OR: 0.67; 95% CI: 0.47, 0.97). A risk of bias was evident particularly for observational reports, with some publication bias suggested in funnel plots. CONCLUSION: In this comprehensive analysis of mainly observational data, the use of DOACs was not associated with a significant difference in stroke or systemic embolism, or thrombus resolution, compared with VKA therapy. The use of DOACs was associated with a lower rate of all-cause death and fewer bleeding events. Adequately sized randomized clinical trials are needed to confirm these findings, which could allow a wider adoption of DOACs in patients with LVT.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".