Perioperative Management of Antiplatelet Therapy: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Objective: To summarize the available evidence about the perioperative management of patients who are receiving long-term antiplatelet therapy and require elective surgery/procedures. Methods: This systematic review supports the development of the American College of Chest Physicians guideline on the perioperative management of antiplatelet therapy. A literature search of MEDLINE, EMBASE, Scopus and Cochrane databases was conducted from each database's inception to July 16, 2020. Meta-analyses were conducted when possible. Results: In patients receiving long-term antiplatelet therapy and undergoing elective noncardiac surgery, the available evidence did not show a significant difference in major bleeding between a shorter vs longer antiplatelet interruption, with low certainty of evidence (COE). Compared with patients who received placebo perioperatively, aspirin continuation was associated with increased risk of major bleeding (relative risk [RR], 1.31; 95% CI, 1.15-1.50; high COE) and lower risk of major thromboembolism (RR, 0.74; 95% CI, 0.58-0.94; moderate COE). During antiplatelet interruption, bridging with low-molecular-weight heparin was associated with increased risk of major bleeding compared with no bridging (RR, 1.86; 95% CI, 1.24-2.79; very low COE). Continuation of antiplatelets during minor dental and ophthalmologic procedures was not associated with a statistically significant difference in the risk of major bleeding (very low COE). Conclusion: This systematic review summarizes the current evidence about the perioperative management of antiplatelet therapy and highlights the urgent need for further research, particularly with the increasing prevalence of patients taking 1 or more antiplatelet agents.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.025 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".