Change in Angina Symptom Status After Acute Myocardial Infarction and Its Association With Readmission Risk: An Analysis of the Translational Research Investigating Underlying Disparities in Acute Myocardial Infarction Patients' Health Status (TRIUMPH) Registry
Bibliographic record
Abstract
BACKGROUND: Angina is common both before and after myocardial infarction (MI). Whether the change in angina status within the first 30 days after MI is associated with subsequent readmission and angina persistence is unknown. METHODS AND RESULTS: We studied 2915 MI patients enrolled at 24 hospitals in the Translational Research Investigating Underlying Disparities in Acute Myocardial Infarction Patients' Health Status (TRIUMPH) registry. Angina before and 30 days after MI was assessed with the Seattle Angina Questionnaire. Patients were divided into angina-free pre- and post-MI (-/-), resolved angina (+/-), new angina (-/+), and persistent angina (+/+) groups. Multivariable proportional hazards and hierarchical modified Poisson models were performed to assess the association of each group with all-cause readmission, readmission for MI or unplanned revascularization, and angina persistence at 1 year. Overall, 1293 patients (44%) had angina before their MI and 849 (29%) reported angina within 30 days of discharge. Patients with post-MI angina were more likely to be younger, nonwhite, and uninsured. Compared with patients who were angina-free pre- and post-MI, 1-year all-cause readmission risks were significantly higher for patients with persistent angina (hazard ratio [HR], 1.35; 95% CI 1.06-1.71) or new angina (HR, 1.40; 95% CI, 1.08-1.82). At 1 year, angina was present in 22% of patients and was more likely if angina was persistent (HR, 3.55; 95% CI, 3.05-4.13) or new (HR, 3.38; 95% CI, 2.59-4.42) at 30 days compared with patients who were angina-free pre- and post-MI. CONCLUSIONS: Post-MI angina, whether new or persistent, is associated with higher likelihood of readmission. Prioritizing post-MI angina management is a potential means of improving 1-year outcomes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".