Direct oral anticoagulants in treatment of cerebral venous thrombosis: a systematic review
Bibliographic record
Abstract
OBJECTIVES: Current guidelines do not recommend direct oral anticoagulants (DOACs) to treat cerebral venous thrombosis (CVT) despite their benefits over standard therapy. We performed a systematic review to summarise the published experience of DOAC therapy in CVT. DATA SOURCES: MEDLINE, Embase and COCHRANE databases up to 18 November 2020. ELIGIBILITY CRITERIA: All published articles of patients with CVT treated with DOAC were included. Studies without follow-up information were excluded. DATA EXTRACTION AND SYNTHESIS: Two independent reviewers screened articles and extracted data. A risk of bias analysis was performed. PRIMARY AND SECONDARY OUTCOME MEASURES: Safety data included mortality, intracranial haemorrhage (ICH) or other adverse events. Efficacy data included recurrent CVT, recanalisation rates and disability by modified Rankin Scales (mRS). RESULTS: 33 studies met inclusion criteria. One randomised controlled trial, 5 observational cohorts and 27 case series or studies reported 279 patients treated with DOAC for CVT: 41% dabigatran, 47% rivaroxaban, 10% apixaban and 2% edoxaban, in addition to 315 patients treated with standard therapy. The observational cohorts showed a similar risk of death in DOAC and standard therapy arms (RR 2.12, 95% CI 0.29 to 15.59). New ICH was reported in 2 (0.7%) DOAC-treated patients and recurrent CVT occurred in 4 (1.5%). A favourable mRS between 0 and 2 was reported in 94% of DOAC-treated patients, more likely than standard therapy in observational cohorts (RR 1.13, 95% CI 1.02 to 1.25). CONCLUSION: The evidence for DOAC use in CVT is limited although suggests sufficient safety and efficacy despite variability in timing and dose of treatment. This systematic review highlights that further rigorous trials are needed to validate these findings and to determine optimal treatment regimens.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".