Incidence, Predictors, and Prognostic Implications of Hospitalization for Late Bleeding After Percutaneous Coronary Intervention for Patients Older Than 65 Years
Bibliographic record
Abstract
BACKGROUND: Previous data on bleeding after percutaneous coronary intervention (PCI) have been obtained primarily from randomized trials that focused on in-hospital bleeding. The incidence of late bleeding after PCI, its independent predictors, and its prognostic importance in clinical practice has not been fully addressed. METHODS AND RESULTS: We evaluated 22 798 patients aged >65 years who underwent PCI from December 1, 2003, to March 31, 2007, in Ontario, Canada. Cox proportional hazard models were used to determine factors associated with late bleeding, which was defined as hospitalization for bleeding after discharge from the index PCI, and to estimate risk of death or myocardial infarction associated with late bleeding. We found that 2.5% of patients were hospitalized for bleeding in the year after PCI, with 56% of bleeding episodes due to gastrointestinal bleed. The most significant predictor of late bleeding was warfarin use after PCI (hazard ratio [HR], 3.12). Other significant predictors included age (HR, 1.41 per 10 years), male sex (HR, 1.24), cancer (HR, 1.80), previous bleeding (HR, 2.42), chronic kidney disease (HR, 1.93), and nonsteroidal antiinflammatory drug use (HR, 1.73). After adjusting for baseline covariates, hospitalization for a bleeding episode was associated with a significantly increased 1-year hazard of death or myocardial infarction (HR, 2.39; 95% CI, 1.93 to 2.97) and death (HR, 3.38; 95% CI, 2.60 to 4.40). CONCLUSIONS: Hospitalization for late bleeding after PCI is associated with substantially increased risk of death and myocardial infarction. The use of triple therapy (i.e., aspirin, thienopyridine, and warfarin) is associated with the highest risk of late bleeding.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".