Treatment choices in elderly patients with ST: elevation myocardial infarction—insights from the Vital Heart Response registry
Bibliographic record
Abstract
BACKGROUND: Management of elderly patients with ST elevation myocardial infarction (STEMI) is challenging and they are under-represented in trials. Accordingly, we analysed reperfusion strategies and their effectiveness in patients with STEMI ≥75 years compared to <75 years within a comprehensive inclusive registry. METHODS: Consecutive patients with STEMI admitted to hospital and tracked within a regional registry (2006-2011) were analysed comparing reperfusion strategy (primary percutaneous coronary intervention (PPCI), fibrinolysis and no reperfusion) between patients ≥75 vs <75 years old as well as across the reperfusion strategies in those ≥75 years. RESULTS: There were 3588 patients with STEMI with 646 (18%) ≥75 years old. Elderly patients were more likely female (46.9% vs 18.4%) and had more prior: angina (28.2% vs 17.2%), myocardial infarction (MI; 22.8% vs 13.9%), hypertension (67.6% vs 44.2%), heart failure (2.3% vs 0.3%) and atrial fibrillation (2.2% vs 0.5%) (all p<0.001). The reperfusion strategy for patients ≥75 vs <75: PPCI 45.3% vs 41.2%, fibrinolysis 24.8% vs 45.7%, and no reperfusion 29.9% vs 13.1% (p<0.001). Time from symptoms to first medical contact (median, 93 vs 78 min p=0.008) and PPCI (median, 166 vs 136 min (p<0.001) were longer for ≥75 years. In those ≥75 years outcomes by reperfusion (PPCI, fibrinolysis and none) were: in-hospital death 13.3%, 9.4% and 19.7% (p=0.018), and composite of death, recurrent-MI, cardiogenic shock and congestive heart failure 28%, 20% and 33.2% (p=0.022). CONCLUSIONS: Elderly patients have more comorbidities, worst in-hospital clinical outcomes and are less likely to receive reperfusion. Acknowledging physician selection of the reperfusion strategy; outcomes appear favourable in the elderly receiving fibrinolysis.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".