Case‐fatality rate of major bleeding events in patients on dual antiplatelet therapy after percutaneous coronary intervention: A systematic review and meta‐analysis
Bibliographic record
Abstract
Background Assessment of the case‐fatality rate (CFR) of major bleeding on dual antiplatelet therapy (DAPT) may improve balancing risks and benefits of different durations of DAPT following percutaneous coronary intervention (PCI). Objectives To determine the CFR of major bleeding in patients on DAPT after PCI and to compare rates among different durations of DAPT. Methods Medline, Embase, and CENTRAL were searched from inception to August 2021 for randomized trials that reported fatal bleeding among patients who were randomized to ≥1 month of DAPT following PCI. Summary estimates for CFRs of major bleeding were calculated using the random‐effects inverse‐variance method. Statistical heterogeneity was evaluated using the I 2 statistic. Results Of 2777 citations obtained by the search, 15 (48%) of 31 potentially eligible studies were excluded because fatal bleeding was not reported, leaving 16 studies that were included in the analysis. Overall, there were 823 major bleeding events including 91 fatal events in 48,884 patients who were assigned to receive DAPT during study follow‐up. The CFR of major bleeding was 10.8% (95% confidence interval [CI], 7.1–16.2; I 2 = 50%) in the entire study population, and 13.8% (95% CI, 6.5–27.1; I 2 = 28%), 11.2% (95% CI, 6.7–18.0; I 2 = 0%), and 5.8% (95% CI, 3.0–11.1; I 2 = 0%) in those on short‐term (≤6 months; n = 16,553), standard‐term (12 months; n = 19,453), and long‐term DAPT (>12 months; n = 10,238), respectively. Conclusion Fatal bleeding is not reported in many studies evaluating DAPT after PCI. The CFR of major bleeding on DAPT is substantial and may be higher in the first 12 months of DAPT than during long‐term DAPT.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.025 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".