Bleeding Complications in Acute Coronary Syndromes and Percutaneous Coronary Intervention: Predictors, Prognostic Significance, and Paradigms for Reducing Risk
Bibliographic record
Abstract
In clinical trials up to 30% of patients with acute coronary syndromes (ACS) or undergoing percutaneous coronary intervention (PCI) experience bleeding complications, and even higher rates have been reported in contemporary practice. A growing body of data suggests a strong correlation between bleeding and both short- and long-term adverse outcomes, including mortality, which is independent of baseline characteristics and remains evident in most trials, despite variations in the definition of major bleeding. Although the value of antithrombin and antiplatelet therapy in reducing the risk of ischemic events is well established, the mechanisms of action that confer the benefits of these therapies have an inherent tendency to increase the risk of bleeding complications. As a result, characterization of baseline hemorrhagic risk is critical and must be accomplished before selecting an antithrombotic therapy. Risk factors for bleeding may be divided into two categories: nonmodifiable (including age, gender, race, weight, renal insufficiency, anemia, and acuity of presentation) and modifiable (including choice of antithrombotic therapy and PCI procedural characteristics). Of these predictive factors, the choice, dosage, and duration of the antithrombin and/or antiplatelet regimen are perhaps the most readily modifiable, especially in patients with an increased risk of bleeding. This review explores the nature of the association between bleeding and adverse outcomes, including mortality; evaluates risk factors for bleeding; and examines mechanisms for reducing bleeding complications through the selection of appropriate antithrombotic therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".