Percutaneous coronary intervention in patients with cancer and readmissions within 90 days for acute myocardial infarction and bleeding in the USA
Bibliographic record
Abstract
AIMS: The post-discharge outcomes of patients with cancer who undergo PCI are not well understood. This study evaluates the rates of readmissions within 90 days for acute myocardial infarction (AMI) and bleeding among patients with cancer who undergo percutaneous coronary intervention (PCI). METHODS AND RESULTS: Patients treated with PCI in the years from 2010 to 2014 in the US Nationwide Readmission Database were evaluated for the influence of cancer on 90-day readmissions for AMI and bleeding. A total of 1 933 324 patients were included in the analysis (2.7% active cancer, 6.8% previous history of cancer). The 90-day readmission for AMI after PCI was higher in patients with active cancer (12.1% in lung, 10.8% in colon, 7.5% in breast, 7.0% in prostate, and 9.1% for all cancers) compared to 5.6% among patients with no cancer. The 90-day readmission for bleeding after PCI was higher in patients with active cancer (4.2% in colon, 1.5% in lung, 1.4% in prostate, 0.6% in breast, and 1.6% in all cancer) compared to 0.6% among patients with no cancer. The average time to AMI readmission ranged from 26.7 days for lung cancer to 30.5 days in colon cancer, while the average time to bleeding readmission had a higher range from 38.2 days in colon cancer to 42.7 days in breast cancer. CONCLUSIONS: Following PCI, patients with cancer have increased risk for readmissions for AMI or bleeding, with the magnitude of risk depending on both cancer type and the presence of metastasis.
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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 | 0.000 | 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 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".