Risk Factors for Venous Thromboembolism in Inflammatory Bowel Disease: A Systematic Review and Meta-Analysis by Phase of Care
Bibliographic record
Abstract
BACKGROUND: Risk factors for venous thromboembolism (VTE) and their relative magnitudes across different phases of care in inflammatory bowel disease (IBD) are poorly understood. Therefore, we performed a systematic review to identify risk factors for VTE in patients with IBD during the hospitalized, post-operative, post-discharge, and ambulatory phases of care. METHODS: MEDLINE, EMBASE, and Cochrane CENTRAL were systematically searched from inception through to April 2024 without language restriction. We included studies that reported risk factors for VTE among adults with IBD. Summary estimates with 95% confidence intervals (CIs) were calculated for individual risk factors overall and stratified by phase of care using random effects models. RESULTS: A total of 123 studies with over 23 510 969 patients were analyzed. We identified 48 variables for meta-analysis overall and 27 were significantly associated with VTE. The strongest risk factors were prior VTE (odds ratio [OR], 4.44; 95% CI, 2.63-7.49), surgical complications (OR, 3.06; 95% CI, 2.48-3.77), urgent surgery (OR, 2.33; 95% CI, 1.62-3.35), blood transfusions (OR, 2.68; 95% CI, 1.17-6.12), hypoalbuminemia (OR, 2.25; 95% CI, 1.93-2.62), and total parenteral nutrition (OR, 2.21; 95% CI, 1.85-2.64). Corticosteroids (OR, 1.60; 95% CI, 1.46-1.76) but not anti-tumor necrosis factor therapy (OR, 0.66; 95% CI, 0.46-0.97) were associated with an increased risk of VTE. No major differences were observed for most variables between hospitalized, post-operative, and post-discharge settings. CONCLUSIONS: We identified multiple risk factors associated with VTE across different phases of care. This work will help in the development of future predictive models to guide thromboprophylaxis in IBD.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.049 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".