Perioperative Heparin Bridging in Patients Receiving Oral Anticoagulation: Meta-Analysis of Bleeding and Thromboembolic Rates
Bibliographic record
Abstract
Abstract Abstract 545 Background: Periprocedural bridging using unfractionated heparin (UFH) or low molecular weight heparin (LMWH) in patients receiving chronic oral anticoagulation (OAC) is often utilized with the view to reduce the risk of thromboembolic (TE) events. Optimal perioperative anticoagulant methods have not been established. Methods: Systematic review and meta-analysis of published English-language studies from 2001 to 2010 examining bleeding and TE events in patients receiving bridging therapy during temporary OAC interruption for elective procedures. Results: A search of MEDLINE, EMBASE and Cochrane Collaboration databases yielded 32 studies on 6760 bridged patients. Studies were reviewed by 2 independent data collectors (k=0.869). Study quality was generally poor with risk of bias. Thirty-one studies were observational with 1 randomized controlled trial. Low TE risk and/or non-OAC patient groups were used for comparison in 12 observational studies. Major (22/32, 68.8%) and non-major (27/32, 84.4%) procedures were represented. TE events occurred in 67 of 6760 bridged (0.87%; 95% CI 0.40%–1.35%) and 29 of 4897 non-bridged (0.77%; 95% CI 0.24%–1.30%) patients. Using a random effects model, there was no difference in the risk of TE events in bridged versus non-bridged patients (OR 1.02, 95% CI 0.53–1.95). Bridged patients had a significantly increased risk of overall bleeding (OR 5.47, 95% CI 3.89–7.70) and major bleeding (OR 3.43, 95% CI 1.13–10.4) compared to non-bridged patients. There was no difference in TE events (OR 2.44, 95% CI 0.34–17.4) or overall bleeding (OR 2.40 95% CI 0.72–8.05) in patients receiving full versus intermediate/low dose LMWH. Summary: Patients receiving heparin bridging during OAC interruption appear to be at increased risk of bleeding and similar risk of TE events compared to non-bridged patients. Studies of high methodologic quality are needed to develop an optimal anticoagulation strategy and inform clinical decision-making. Disclosures: Lim: Leo Pharma: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bayer: Consultancy; Pfizer: Honoraria. Kaatz:Boehringer-Ingelheim: Consultancy, Research Funding, Speakers Bureau; Bristol Myer Squibb: Consultancy, Research Funding; Bayer: Research Funding; National Institute of Health: Research Funding; Canadian Institute of Health Research: Research Funding; Pfizer: Consultancy; Johnson and Johnson: Consultancy; Ortho-McNeil: Consultancy; GlaxoSmithKline: Speakers Bureau; AC Forum: Membership on an entity's Board of Directors or advisory committees; National Certification Board of Anticoagulation Providers: Membership on an entity's Board of Directors or advisory committees; National Blood Clot Alliance: Membership on an entity's Board of Directors or advisory committees.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.053 |
| Bibliometrics | 0.004 | 0.005 |
| 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".