Abstract 18502: Bleeding Post PCI: Predictors of “ANY” Bleeds: Six Month Results from the PARIS (Patterns of Non-Adherence to Antiplatelet Regimens in Stented Patients) Registry
Bibliographic record
Abstract
Introduction. While recent studies have highlighted the clinical importance of minor, or “nuisance” bleeding, the predictors of all bleeding complications including minor bleeding events have not been examined in detail. Methods. The PARIS registry was a multicenter, international, all-comers registry of patients who had undergone successful PCI in 2009-2010 for stable angina, acute coronary syndrome (ACS) or documented silent ischemia. All bleeding events were adjudicated by a Clinical Events Committee according to the Bleeding Academic Research Consortium (BARC) criteria from source documents. We compared baseline clinical and treatment variables in patients who had any versus no bleeding at 6-months. Results. Of 5,033 patients, 235 (4.7%) had a bleeding event in the first 6-months. Of these, 27.7% had BARC type 1 bleeding, 40.4% had BARC type 2 bleeding, 30.2% had type 3, 0.4% had type 4 (CABG-related), and 1.3% had type 5 (fatal) bleeding. Compared to patients with no bleeding, patients who had bleeding were more likely to be older, female, non-smokers, and to be prescribed on warfarin on discharge (Table). After multivariable analysis, independent predictors of 6-month bleeding were age (OR 1.02, 95% CI 1.002 - 1.03), female gender (OR 1.9, 95% CI 1.3 - 2.9), and warfarin therapy (OR 2.6, 95% CI 1.7 - 3.7, all p<0.05), whereas hypertension was associated with reduced risk of bleeding (OR 0.7, 95% CI 0.52 - 0.98, p=0.04). Treatment with prasugrel (vs. clopidogrel) was not a predictor. Conclusion. In this real world, contemporary PCI registry with close evaluation of bleeding events, the 6-month rate of any bleeding was 4.7%. Almost 70% of these bleeding incidents were BARC type 1 and 2, which may be classified as minor or “nuisance” bleeds. Age, female gender, and warfarin were independent predictors of bleeding events. Table.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".