Prevalence and outcome of patients with cancer and acute coronary syndrome undergoing percutaneous coronary intervention: a BleeMACS substudy
Bibliographic record
Abstract
BACKGROUND: The prevalence and outcome of patients with cancer that experience acute coronary syndrome (ACS) have to be determined. METHODS AND RESULTS: The BleeMACS project is a multicentre observational registry enrolling patients with acute coronary syndrome undergoing percutaneous coronary intervention worldwide in 15 hospitals. The primary endpoint was a composite event of death and re-infarction after one year of follow-up. Bleedings were the secondary endpoint. 15,401 patients were enrolled, 926 (6.4%) in the cancer group and 14,475 (93.6%) in the group of patients without cancer. Patients with cancer were older (70.8±10.3 vs. 62.8±12.1 years, P<0.001) with more severe comorbidities and presented more frequently with non-ST-segment elevation myocardial infarction compared with patients without cancer. After one year, patients with cancer more often experienced the composite endpoint (15.2% vs. 5.3%, P<0.001) and bleedings (6.5% vs. 3%, P<0.001). At multiple regression analysis the presence of cancer was the strongest independent predictor for the primary endpoint (hazard ratio (HR) 2.1, 1.8-2.5, P<0.001) and bleedings (HR 1.5, 1.1-2.1, P=0.015). Despite patients with cancer generally being undertreated, beta-blockers (relative risk (RR) 0.6, 0.4-0.9, P=0.05), angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (RR 0.5, 0.3-0.8, P=0.02), statins (RR 0.3, 0.2-0.5, P<0.001) and dual antiplatelet therapy (RR 0.5, 0.3-0.9, P=0.05) were shown to be protective factors, while proton pump inhibitors (RR 1, 0.6-1.5, P=0.9) were neutral. CONCLUSION: Cancer has a non-negligible prevalence in patients with acute coronary syndrome undergoing percutaneous coronary intervention, with a major risk of cardiovascular events and bleedings. Moreover, these patients are often undertreated from clinical despite medical therapy seems to be protective. Registration:The BleeMACS project (NCT02466854).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".