Heart Failure Readmission in Patients With ST-Segment Elevation Myocardial Infarction and Active Cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Although numerous studies have examined readmission with heart failure (HF) after acute myocardial infarction (AMI), limited data are available on HF readmission in cancer patients post-AMI. Objectives: This study aimed to assess the rates and factors associated with HF readmission in cancer patients presenting with ST-segment elevation myocardial infarction (STEMI). Methods: A nationally linked cohort of STEMI patients between January 2005 and March 2019 were obtained from the UK Myocardial Infarction National Audit Project registry and the UK national Hospital Episode Statistics Admitted Patient Care registry. Multivariable Fine-Gray competing risk models were used to evaluate HF readmission at 30 days and 1 year. Results: < 0.001) and percutaneous coronary intervention (58.4% vs. 69.5%). There was a significant prescription gap in the administration of post-AMI medications upon discharge such as an angiotensin-converting enzyme inhibitor/angiotensin receptor blocker (49.5% vs 71.1%) and beta-blockers (58.4% vs 68.0%) in cancer patients. The cancer group had a higher rate of HF readmission at 30 days (3.2% vs 2.3%) and 1 year (9.4% vs 7.3%). However, after adjustment, cancer was not independently associated with HF readmission at 30 days (subdistribution HR: 1.05; 95% CI: 0.86-1.28) or 1 year (subdistribution HR: 1.03; 95% CI: 0.92-1.16). The opportunity-based quality indicator was associated with higher rates of HF readmission independent of cancer diagnosis. Conclusions: Cancer patients receive care that differs in important ways from patients without cancer. Greater implementation of evidence-based care may reduce HF readmissions, including in cancer patients.
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.
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 it