Health Status Outcomes in Patients With Acute Myocardial Infarction After Rehospitalization
Bibliographic record
Abstract
BACKGROUND: Rehospitalizations after acute myocardial infarction for unplanned coronary revascularization and unstable angina (UA) are common. However, despite the inclusion of these events in composite end points of many clinical trials, their association with health status has not been studied. METHODS AND RESULTS: We included 3283 patients with acute myocardial infarction enrolled in a prospective, 24-center US study who had rehospitalizations independently classified by experienced cardiologists. Health status was assessed using Seattle Angina Questionnaire and EuroQol-5D Visual Analog Scale. In the propensity-matched cohorts, 1-year health status was compared between those who did and did not experience rehospitalization for UA or revascularization using a hierarchical linear model. Overall, mean age was 59 years, 33% were women, and 70% were white. Rehospitalization rates for UA and unplanned revascularization at 1 year were 4.3% and 4.7%. One-year Seattle Angina Questionnaire summary scores were worse in patients with rehospitalizations for UA (mean difference, -10.1; 95% confidence interval, -12.4 to -7.9) and unplanned revascularization (mean difference, -5.7; 95% confidence interval, -8.8 to -2.5) when compared with patients without such rehospitalizations. Similarly, EuroQol-5D Visual Analog Scale scores were worse among patients with such readmissions. Individual Seattle Angina Questionnaire domains indicated worse 1-year angina and quality of life outcomes among patients rehospitalized for UA or unplanned revascularization. CONCLUSIONS: Within the first year after acute myocardial infarction, rehospitalizations for UA and unplanned revascularization are associated with worse health status. These findings highlight the impact of such events from a patient's perspective, beyond their economic impact and support the use of UA and unplanned revascularization as elements of composite end points.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".