Outcomes of ST elevation myocardial infarction in patients with cancer: a nationwide study
Bibliographic record
Abstract
AIMS: To assess processes of care and clinical outcomes in cancer patients with ST elevation myocardial infarction (STEMI) according to cancer type. METHODS AND RESULTS: This is a national population-based study of patients admitted with STEMI in the UK between January 2005 and March 2019. Data were obtained from the National Heart Attack Myocardial Infarction National Audit Project (MINAP) registry and the Hospital Episode Statistics registry. We identified 353 448 STEMI-indexed admissions between 2005 and 2019. Of those, 8581 (2.4%) had active cancer. Prostate cancer (29% of STEMI patients with cancer) was the most common cancer followed by haematologic malignancies (14%) and lung cancer (13%). Cancer patients were less likely to receive invasive coronary revascularization (60.0% vs. 71.6%, P < 0.001] and had higher in-hospital death [odd ratio (OR) 1.39, 95% confidence interval (CI) 1.25-1.54] and bleeding (OR 1.23, 95% CI 1.03-1.46). Cancer patients had higher mortality at 30 days (HR 2.39, 95% CI 2.19-2.62) and 1 year (HR 3.73, 95% CI 3.58-3.89). Lung cancer was the cancer associated with the highest risk of death in the hospital (OR 1.75, 95% CI 1.39-2.22) and at 1 year (OR 8.08, 95% CI 7.44-8.78). Colon cancer (OR 1.98, 95% CI 1.24-3.14) was the main cancer associated with major bleeding. All common cancer types were associated with higher mortality at 1 year. Cardiovascular death (62%) was the main cause of death in the first 30 days, while cancer (52%) was the main cause of death within 1 year. CONCLUSION: STEMI patients with cancer have a higher risk of short- and long-term mortality, particularly lung cancer. Colon cancer is the main cancer associated with major bleeding. Cardiovascular disease was the main cause of death in the first month, whereas cancer was the main cause of death within 1 year.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".