Long‐Term Mortality of Older Patients With Acute Myocardial Infarction Treated in US Clinical Practice
Bibliographic record
Abstract
BACKGROUND: There is limited information about the long-term survival of older patients after myocardial infarction (MI). METHODS AND RESULTS: CRUSADE (Can rapid risk stratification of unstable angina patients suppress adverse outcomes with early implementation of the ACC/AHA guidelines) was a registry of MI patients treated at 568 US hospitals from 2001 to 2006. We linked MI patients aged ≥65 years in CRUSADE to their Medicare data to ascertain long-term mortality (defined as 8 years post index event). Long-term unadjusted Kaplan-Meier mortality curves were examined among patients stratified by revascularization status. A landmark analysis conditioned on surviving the first year post-MI was conducted. We used multivariable Cox regression to compare mortality risks between ST-segment-elevation myocardial infarction and non-ST-segment-elevation myocardial infarction patients. Among 22 295 MI patients ≥ age 65 years (median age 77 years), we observed high rates of evidence-based medication use at discharge: aspirin 95%, β-blockers 94%, and statins 81%. Despite this, mortality rates were high: 24% at 1 year, 51% at 5 years, and 65% at 8 years. Eight-year mortality remained high among patients who underwent percutaneous coronary intervention (49%), coronary artery bypass graft (46%), and among patients who survived the first year post-MI (59%). Median survival was 4.8 years (25th, 75th percentiles 1.1, 8.5); among patients aged 65-74 years it was 8.2 years (3.3, 8.9) while for patients aged ≥75 years it was 3.1 years (0.6, 7.6). Eight-year mortality was lower among ST-segment-elevation myocardial infarction than non-ST-segment-elevation myocardial infarction patients (53% versus 67%); this difference was not significant after adjustment (hazard ratio 0.94, 95% confidence interval, 0.88-1.00). CONCLUSIONS: Long-term mortality remains high among patients with MI in routine clinical practice, even among revascularized patients and those who survived the first year.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".