Ischaemic and bleeding risk after ST-elevation myocardial infarction in patients with active cancer: a nationwide study
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Aims Treatment of patients with cancer presenting with ST-elevation myocardial infarction (STEMI) is complex given the increased risk of both thrombotic and major bleeding complications. Methods and results A nationally linked cohort of STEMI patients between January 2005 and March 2019 was obtained from the UK Myocardial Infarction National Audit Project and the UK National Hospital Episode Statistics Admitted Patient Care registries. The primary outcomes were major bleeding and re-infarction at 1 year following admission with STEMI. Major bleeding was defined as bleeding events that require hospital admission. Re-infarction was defined as acute MI according to the fourth Universal Definition of Myocardial Infarction. A total of 322 776 STEMI-indexed admissions were identified between January 2005 and March 2019. Of those, 7050 (2.2%) patients were diagnosed with active cancer. Cancer patients were older with more cardiovascular comorbidities. Cancer patients received invasive coronary angiography (62.2% vs. 72.7%, P < 0.001) and percutaneous coronary intervention (58.4% vs. 69.5%, P < 0.001) less often compared with patients without cancer and were less likely to be prescribed dual antiplatelet therapy (85% vs. 95.4%, P < 0.001). The incidence of major bleeding (6.5% vs. 3.5%, P < 0.001) and re-infarction (cancer 5.7%, no cancer 5.1%, P = 0.01) was higher in cancer patients at 1 year. After adjustment for differences in baseline covariates, a similar risk of re-infarction (sub-hazard ratios (SHR) 1.10, 95% CI 0.94–1.27) and a 50% increased risk of major bleeding (SHR 1.49, 95% CI 1.30–1.71) were observed in cancer patients. Conclusion Compared with non-cancer patients, cancer patients have a higher risk of major bleeding but not of re-infarction. Mitigating bleeding risk in STEMI patients with cancer is of paramount importance to improve outcomes.
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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) | 0.000 | 0.000 |
| 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 it