Quality of care and long-term survival after ST-elevation myocardial infarction in adults with cancer
Bibliographic record
Abstract
BACKGROUND: While current evidence suggests that the clinical outcomes of ST-elevation myocardial infarction (STEMI) are worse among patients with cancer, it is unknown what role the quality of care received during admission plays. We aimed to evaluate the association between care quality and patient survival after discharge. METHODS AND RESULTS: A nationally-linked cohort of STEMI patients (January 2005-March 2019) were obtained from the UK Myocardial Infarction National Audit Project and UK national Hospital Episode Statistics Admitted Patient Care registries. We used the composite opportunity-based quality indicator to measure overall care quality. Survival outcomes were assessed using Cox proportional hazard models and Kaplan-Meier and cumulative survival curves. In total, 6787 STEMI indexed admissions with cancer were identified. Of those, 4340 (63.9%) patients received optimum care, 1320 (19.5%) intermediate care, and 1127(25.2%) low care quality. Patients with low care quality were older [optimum quality median (IQR) = 72.8 (65.1, 79.6), intermediate quality 75.5 (67.9, 82.1), low quality 78.2 (69.2, 84.7)] and more frequently women (optimum quality 21.6%, intermediate quality 27.3%, low quality 35.5%). Compared to patients with optimum care, patients with low care quality had a higher risk of death at 30 days [hazard ratio (HR) 7.0, 95% confidence interval (CI) 5.7-8.7], 1 year (HR 4.0, 95% CI 3.6-4.4), and 5 years (HR 2.6, 95% CI 2.4-2.8). Relative survival analysis revealed that the number of patients who would survive nationally if they received optimal care is 84 (95% CI 67-102), 508 (95% CI 468-548), and 1096 (95% CI 1034-1158) at 30 days, 1 year, and 5 years, respectively. The association between care quality and survival was more profound in the Northwest and Northeast regions. CONCLUSION: Quality of care is closely associated with short- and long-term survival among STEMI patients with cancer. Improving quality of care may save hundreds to thousands of lives in the shorter and longer term.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".