Association Between Hospital Cardiac Management and Outcomes for Acute Myocardial Infarction Patients
Bibliographic record
Abstract
BACKGROUND: Randomized trials have shown that medical and interventional therapies improve outcomes for acute myocardial infarction (AMI) patients. The extent to which hospital quality improvement translates into better patient outcomes is unclear. OBJECTIVES: To determine hospital cardiac management markers associated with improved outcomes. RESEARCH DESIGN, SUBJECTS: Population-based longitudinal cohort study of 98,115 adults hospitalized with first episode of AMI during 2000 to 2006 in 77 Ontario hospitals with >50 annual AMI admissions. MEASURES: Rates of 30-day and 1-year mortality, readmissions for AMI or death, and major cardiac events (readmissions for AMI, angina, heart failure, or death) within 6 months, according to index hospital cardiac management markers, including appropriate initial emergency department (ED) assessment (rate of high acuity triage) high-acuity and intensity of interventional (30-day cardiac catheterization rate) and medical (discharge statin prescribing rate) therapy. RESULTS: Thirty-day risk-adjusted mortality varied 2.3-fold (7.2%-16.9%) and major cardiac events rates varied 2-fold (18.2%-35.6%) across hospitals in 2006. Patients admitted to hospitals with the highest versus lowest rates of combined medical and interventional management had lower rates of 30-day mortality (adjusted relative rate [aRR] = 0.84, 95% CI, 0.78-0.91), 1-year mortality (aRR = 0.86, 0.81-0.91), AMI readmissions or death (aRR = 0.74, 0.69-0.78), and major cardiac event (aRR = 0.65, 0.61-0.68). Patients admitted to EDs with the highest rates of appropriate initial assessment had lower 30-day (aRR = 0.93, 0.88-0.98) and 1-year mortality (aRR = 0.96, 0.93-1.00). CONCLUSIONS: Hospitals with higher levels of both medical and interventional management and higher quality initial ED assessment had better outcomes. Readmissions were particularly sensitive to care processes. In the face of the unwarranted variations in outcomes across hospitals, strategies that promote better ED and inpatient management of AMI patients are needed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".