Abstract 211: A Comparison of Bleeding in Patients Treated with Clopidogrel or Prasugrel in a US Hospital Database
Bibliographic record
Abstract
Background: Newer, more potent antiplatelet therapy is commonly perceived as being associated with a higher incidence of bleeding complications. There are limited real-world data on bleeding among patients with acute coronary syndrome (ACS) who have undergone percutaneous coronary intervention (PCI) with new antiplatelet therapy. The objective of this study was to compare “real-world” incidence of bleeding among patients on clopidogrel vs. prasugrel therapy in a large database from diverse US hospitals. Methods: We analyzed patient characteristics and the incidence of bleeding complications during the index (first) hospitalization among ACS-PCI patients treated with clopidogrel or prasugrel between July 2009 and June 2011. Bleeding was defined as the presence of bleeding ICD-9 codes and transfusion. Cohort differences in outcomes were assessed with unadjusted chi-square tests and adjusted for demographics and baseline clinical differences using logistic regression. Results: 105,490 patients received clopidogrel and 10,544 received prasugrel. Among clopidogrel and prasugrel patients, respectively, 66% and 75% were men, 37% and 46% presented with STEMI, with median age of 64 and 57. During the index hospitalization, the unadjusted incidence of bleeding events was 7% in clopidogrel-treated patients and 4% in prasugrel-treated patients (P<0.0001). Similar patterns were reported in key subgroups (e.g. ACS types, heart failure, anemia, diabetes, renal insufficiency), as well as after a multivariate adjustment. Conclusion: It appears that observed bleeding complications among patients treated with prasugrel is not higher than those treated with clopidogrel, even after multivariate adjustments. However, the potential for unobserved confounders remains a limitation for such observational research. It is conceivable that appropriate patient selection helps to control bleeding complications, as shown here in this large, “real-world” database.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".