Association Between Antidepressant Use and Risk of Venous Thromboembolism: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objectives: To evaluate the association between antidepressant use and the risk of venous thromboembolism (VTE), including deep vein thrombosis and pulmonary embolism (PE), through a systematic review and meta-analysis of observational studies. Methods: A comprehensive literature search was conducted in Medline, Embase®, and Web of Science® up to December 2024. Eighteen studies (cohort, case-control, and nested case-control designs) meeting inclusion criteria were analyzed. Study quality was assessed using the Newcastle–Ottawa Scale. Pooled relative risks (RR) with 95% confidence intervals (CIs) were calculated using a random-effects model. Subgroup analyses were performed based on recency of antidepressant use, VTE onset type (first vs. recurrent), and VTE subtype (PE). Results: Antidepressant use was associated with a significantly increased risk of VTE (RR = 1.22; 95% CI: 1.12–1.32; p < 0.001). Subgroup analyses revealed a stronger association for recent use (within 90 days), first-onset VTE, recurrent VTE, and PE. Heterogeneity was high (I2 = 87.92%), but sensitivity analysis confirmed result robustness. No publication bias was detected. Conclusions: This meta-analysis indicates a modest but statistically significant increase in the risk of VTE associated with antidepressant use, particularly among recent users, individuals experiencing either first-time or recurrent VTE, and those with PE-type events. These findings highlight the importance of individualized VTE risk assessment when initiating antidepressant therapy.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.042 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".