Thrombolytics for venous thromboembolic events: a systematic review with meta-analysis
Bibliographic record
Abstract
Thrombolytic therapy might reduce venous thromboembolism-related mortality and morbidity, but it could also increase the risk of major bleeding. We systematically reviewed the literature to evaluate the effectiveness and safety of thrombolytics in patients with pulmonary embolism (PE) and/or deep venous thrombosis (DVT). We searched Medline, Embase, and Cochrane databases for relevant randomized controlled trials up to February 2019. Multiple investigators independently screened and collected data. We included 45 studies (4740 participants). Pooled estimates of PE studies indicate probable reduction in mortality with thrombolysis (risk ratio [RR], 0.61; 95% confidence interval [CI], 0.40-0.94) (moderate certainty) and possible reduction in nonfatal PE recurrence (RR, 0.56; 95% CI, 0.35-0.89) (low certainty). Pooled estimates of DVT studies indicate the possible absence of effects on mortality (RR, 0.77; 95% CI, 0.26-2.28) (low certainty) and recurrent DVT (RR, 0.99; 95% CI, 0.56-1.76) (low certainty), but possible reduction in postthrombotic syndrome (PTS) with thrombolytics (RR, 0.70; 95% CI, 0.59-0.83) (low certainty). Pooled estimates of the complete body of evidence indicate increases in major bleeding (RR, 1.89; 95% CI, 1.46-2.46) (high certainty) and a probable increase in intracranial bleeding (RR, 3.17; 95% CI 1.19-8.41) (moderate certainty) with thrombolytics. Our findings indicate that thrombolytics probably reduce mortality in patients with submassive- or intermediate-risk PE and may reduce PTS in patients with proximal DVT at the expense of a significant increase in major bleeding. Because the balance between benefits and harms is profoundly influenced by the baseline risks of critical outcomes, stakeholders involved in decision making would need to weigh these effects to define which clinical scenarios merit the use of thrombolytics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.030 | 0.008 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".