Outcomes of patients with high bleeding risks characteristics presenting with acute coronary syndrome undergoing percutaneous coronary intervention
Bibliographic record
Abstract
Abstract Background Patients with high bleeding risk characteristics (HBR) presenting with acute coronary syndrome (ACS) pose a clinical challenge to balance risk for recurrent ischemic events versus incurring bleeding with dual antiplatelet therapy. Purpose We seek to determine the incidence and predictors of short and long term ischemic and bleeding outcomes in patients with HBR factors presenting with ACS after percutaneous coronary intervention (PCI). Method Consecutive patients over a 1-year period, who underwent PCI for ACS were categorized as having HBR based on: age ≥75, anemia (hemoglobin<110g/L), thrombocytopenia (platelet<100x109/L), renal failure (eGFR<30umol/L) or concurrent use of oral anticoagulation. Primary outcome was major adverse cardiovascular event (MACE) defined as composite of cardiovascular death, myocardial infarction, and stroke at 1 year. Key secondary outcomes include significant bleeding defined as Bleeding Academic Research Consortium (BARC) type 3 or 5, and net adverse cardiovascular event (NACE), as a composite of MACE and significant bleeding. Results Of 1351 patients presented with ACS, 389 (28.8%) had at least one HBR criteria. At 1 year, patients with HBR, compared to those without, had increased MACE (11.1% vs 4.2%, p<0.001) and cardiovascular death (5.7% vs 1.7%, p<0.001). Patients with HBR had increased significant bleeding (3.6% vs 2.3%, p=0.011) and NACE (14.4% vs 5.4%, p<0.001). Multivariate analysis showed the presence of HBR and prior history of myocardial infarction were predictors for 1-year MACE (OR 2.67, CI [1.62–4.42], p<0.001 and OR 2.18, CI [1.29–3.70], p=0.004, respectively), whereas the use of second-generation antiplatelet agent was not. Increased MACE and NACE were observed in HBR patients beyond 1 month of DAPT. Conclusion Among patients with ACS undergoing PCI, those with HBR had higher risk for both ischemic and bleeding complications. Novel strategies need to be considered for this high-risk group. Current guidelines, recommending 1 year of DAPT for patients with ACS, should be re-evaluated among patients with HBR. Funding Acknowledgement Type of funding sources: None. Kaplan-Meier curve for 1 year MACEKaplan-Meier curve for 1 year death
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".