Readmission and processes of care across weekend and weekday hospitalisation for acute myocardial infarction, heart failure or stroke: an observational study of the National Readmission Database
Bibliographic record
Abstract
OBJECTIVES: Variation in hospital resource allocations across weekdays and weekends have led to studies of the 'weekend effect' for ST elevation myocardial infarction (STEMI), non-ST elevation myocardial infarction (NSTEMI), heart failure (HF) and stroke. However, few studies have explored the 'weekend effect' on unplanned readmission. We aimed to investigate 30-day unplanned readmissions and processes of care across weekend and weekday hospitalisations for STEMI, NSTEMI, HF and stroke. DESIGN: We grouped hospitalisations for STEMI, NSTEMI, HF or stroke into weekday or weekend admissions. Multivariable adjusted ORs for binary outcomes across weekend versus weekday (reference) groups were estimated using logistic regression. SETTING: We included all non-elective hospitalisations for STEMI, NSTEMI, HF or stroke, which were recorded in the US Nationwide Readmissions Database between 2010 and 2014. PARTICIPANTS: The analysis sample included 659 906 hospitalisations for STEMI, 1 420 600 hospitalisations for NSTEMI, 3 027 699 hospitalisations for HF, and 2 574 168 hospitalisations for stroke. MAIN OUTCOME MEASURES: The primary outcome was unplanned 30-day readmission. As secondary outcomes, we considered length of stay and the following processes of care: coronary angiography, primary percutaneous coronary intervention, coronary artery bypass graft, thrombolysis, brain scan/imaging, thrombectomy, echocardiography and cardiac resynchronisation therapy/implantable cardioverter-defibrillator. RESULTS: Unplanned 30-day readmission rates were 11.0%, 15.1%, 23.0% and 10.9% for STEMI, NSTEMI, HF and stroke, respectively. Weekend hospitalisations for HF were associated with a statistically significant but modest increase in 30-day readmissions (OR of 1.045, 95% CI 1.033 to 1.058). Weekend hospitalisation for STEMI, NSTEMI or stroke was not associated with increased risk of 30-day readmission. CONCLUSION: There was no clinically meaningful evidence against the supposition that weekend and weekday hospitalisations have the same 30-day unplanned readmissions. Thirty-day readmission rates were high, especially for HF, which has implications for service provision. Strategies to reduce readmission rates should be explored, regardless of day of hospitalisation.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".