Abstract 12891: Predictors of Net Adverse Cardiovascular Events in Patients on Triple Antithrombotic Therapy
Bibliographic record
Abstract
Introduction: Patients on dual antiplatelet therapy (DAPT) following percutaneous coronary intervention (PCI) often have indications for concurrent oral anticoagulation or triple antithrombotic therapy (TT). While TT may decrease ischemic complications, it may confer increased bleeding risk. Hypothesis: We set to determine predictors of complications in patients on TT. In particular, we sought to determine if use of ticagrelor in TT is associated with higher complications. Methods: Patients discharged on TT after PCI were followed prospectively for 12 months. The primary endpoint was a composite of ischemic (death, myocardial infarction, stroke) and bleeding (Bleeding Academic Research Consortium (BARC)) complications or net adverse clinical event (NACE). A major secondary endpoint was BARC Types 2, 3 or 5 bleeding. Outcomes were compared between ticagrelor and clopidogrel treated patients. Multivariable analyses were performed to elucidate predictors of complications. Results: Twenty-seven of 152 patients discharged on TT were on ticagrelor. NACE occurred in 52% and BARC 2, 3 or 5 bleeding occurred in 18% of patients. There was no difference in the primary or secondary outcome between ticagrelor versus clopidogrel sub-group. On multiple logistic regressions (Figure 1 and 2), use of TT in patients with acute coronary syndrome (ACS) (p = 0.002) and bridging in with ticagrelor (p=0.02) was associated with increased NACE. Low eGFR was an independent predictor of bleeding (p = 0.03). Conclusions: From our analysis ticagrelor was not found to be an independent predictor of higher bleeding in TT. Instead, low eGFR is associated with higher risk of bleeding. Use of bridging anticolagulation with ticagrelor is associated with higher NACE.
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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 | 0.000 | 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) | 0.003 | 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".