Perioperative management of P2Y12 inhibitors in patients undergoing cardiac surgery within 1 year of PCI
Bibliographic record
Abstract
AIMS: To evaluate the impact of perioperative P2Y12 receptor inhibitor therapy among patients undergoing cardiac surgery within 1 year of percutaneous coronary intervention (PCI). METHODS AND RESULTS: Patients undergoing cardiac surgery in the year post-PCI at three tertiary care centres between 2011 and 2018 were stratified into those who had received at least one dose of P2Y12 inhibitor prior to surgery (within 5 days for clopidogrel or prasugrel, or within 3 days for ticagrelor) and those who had not. The outcomes of interest were major adverse cardiac and cerebrovascular events (MACCEs) and bleeding. Among 20 279 PCI patients, 359 (1.8%) underwent cardiac surgery in the ensuing year, 76.3% of whom received coronary artery bypass grafts. Overall, 33 (9.2%) MACCEs and 85 (23.7%) bleeding events occurred within 30 days post-cardiac surgery. Perioperative P2Y12 inhibition (N = 133, 37%) was not associated with the risk of MACCEs or bleeding, despite numerically lower rates of myocardial infarction or stent thrombosis (0.0% vs. 2.6%; P = 0.089). Patients who continued the P2Y12 inhibitor until the day of surgery (N = 60, 17%) had significantly higher bleeding risk [adjusted odds ratio 2.93, 95% confidence interval 1.53-5.59)]. Predictors of MACCEs included a time interval from PCI to cardiac surgery of ≤30 days and reduced ejection fraction, whereas urgent/emergent surgery predicted bleeding. Chronic kidney disease and myocardial infarction as indication for PCI predicted both MACCEs and bleeding. CONCLUSION: Among patients undergoing cardiac surgery in the year after PCI, the perioperative risk of ischaemic and bleeding events might be influenced by P2Y12 inhibitor therapy in addition to other risk parameters, including the timing and urgency of the procedure.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".