Comparative Effectiveness of β-Blocker Use Beyond 3 Years After Myocardial Infarction and Long-Term Outcomes Among Elderly Patients
Bibliographic record
Abstract
BACKGROUND: The benefit of β-blocker use beyond 3 years after a myocardial infarction (MI) has not been clearly determined. METHODS AND RESULTS: Using data from the CRUSADE Registry (Can Rapid Risk Stratification of Unstable Angina Patients Suppress Adverse Outcomes With Early Implementation of the American College of Cardiology/American Heart Association Guidelines) linked with Medicare claims, we studied patients ≥65 years of age with MI, discharged on β-blocker therapy and alive 3 years later without a recurrent MI to evaluate β-blocker use and dose (none, <50%, and ≥50% of the recommended target) at 3 years. Using inverse probability of treatment weighting, we then examined the adjusted association between β-blocker use (and dose) at 3 years and the cardiovascular composite of all-cause mortality, hospitalization for recurrent MI, ischemic stroke, or heart failure over the subsequent 5 years. Of the 6893 patients ≥65 years age, β-blocker use at 3 years was 72.2% (n=4980); 43% (n=2162) of these were treated with ≥50% of the target β-blocker dose. β-blocker use was not associated with a significant difference on the composite outcome (52.4% versus 55.4%, adjusted hazard ratio, 0.95; 95% CI, 0.88-1.03; P=0.23). Neither low dose (<50% target dose) nor high dose (≥50% target dose) β-blocker use was associated with a significant difference in risk when compared with no β-blocker use. Results were also consistent in patients with and without heart failure or systolic dysfunction ( P interaction =0.30). CONCLUSIONS: In this observational analysis, β-blocker use beyond 3 years post-MI, regardless of the dose achieved, was not associated with improved outcomes. The role of prolonged β-blocker use, particularly in older adults, needs further investigation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".