Direct Oral Anticoagulants Versus Vitamin K Antagonists in Cerebral Venous Thrombosis: A Systematic Review and Meta-Analysis of 4,929 Patients
Bibliographic record
Abstract
Background Cerebral venous thrombosis (CVT), a rare cause of stroke, primarily occurs in young individuals. The established treatment regimen involves anticoagulation with low molecular weight heparin (LMWH) and vitamin K antagonists (VKA). Although direct oral anticoagulants (DOACs) have emerged as a promising alternative, their efficacy and safety remain unclear. This meta-analysis compared the efficacy and safety of DOACs versus VKA in managing CVT. Methods Electronic databases, including PubMed, Cochrane Library, and ScienceDirect, were searched from inception until April 2025. Risk ratios (RR) with a 95% Confidence interval (CI) were pooled under the random effects model in the Review Manager 5.4.1. Quality assessment was done through the Cochrane risk of bias (RoB 2.0) tool and the Newcastle Ottawa Scale (NOS). Subgroup analyses based on study design and different types of DOACs were carried out. Results Thirty-one studies, including five randomized controlled trials (RCTs) and 26 observational studies, were included in this meta-analysis. Our analysis showed a significant reduction in the risk of recurrent venous thromboembolism (VTE) in the DOACs arm compared to VKA (RR = 0.84; 95%CI: [0.71,0.99]; p = 0.04; I 2 = 0%). Similarly, the DOACs showed significant superiority over VKA regarding the intracranial hemorrhage (ICH) (RR = 0.67; 95%CI: [0.50,0.89]; p = 0.007; I 2 = 0%). Other endpoints, including major hemorrhage (RR = 0.70; 95%CI:[0.42,1.15]; p = 0.16; I 2 = 0%), all-cause mortality (RR = 0.96; 95%CI:[0.68,1.35]; p = 0.81; I 2 = 0%), and full recanalization (RR = 0.92; 95%CI:[0.82,1.03]; p = 0.16; I 2 = 21%), are comparable between the two arms. Conclusion DOACs showed a significant reduction in the risk of recurrent VTE and ICH compared to VKA, whereas other endpoints are comparable. Further RCTs with a robust sample size are required to validate and confirm these findings.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".