Impact of an invasive strategy in the elderly hospitalized with acute coronary syndrome with emphasis on the nonagenarians
Bibliographic record
Abstract
BACKGROUND: Published data about nonagenarians with acute coronary syndrome (ACS) were mainly descriptive and limited by small sample sizes and unadjusted outcomes. We aim to describe the characteristics, management, and the impact of an invasive strategy on major adverse events in elderly patients hospitalized with ACS with focus on the nonagerians. METHODS AND RESULTS: We analyzed data collected as part of the AMI-OPTIMA study, a cluster-randomized study of knowledge translation intervention versus usual care on optimal discharge medications in patients admitted with ACS at 24 Canadian hospitals. To determine whether an invasive strategy improved outcomes in the elderly, we used inverse probability weighting to adjust for confounders between patients who underwent invasive versus conservative strategies. Of 4,569 consecutive patients: 2,395 (52%) were <70 years old, 1,031 (23%) were septuagenarians, 941 (21%) were octogenarians, and 202 (4.4%) were nonagenarians. An invasive strategy was associated with reduced in-hospital all-cause mortality in all age groups: 1.1% versus 3.8% in patients <70 years old (P < 0.001), 2.9% versus 7.4% in septuagenarians (P < 0.001), 5.1% versus 14.7% in octogenarians (P < 0.001), and 12.0% versus 25.1% in nonagenarians (P = 0.001). An invasive strategy was also associated with higher thrombolysis in myocardial infarction major bleeds in the nonagenarians (9.0% vs. 2.0%; P = 0.003). CONCLUSIONS: The reduction in in-hospital mortality associated with an invasive strategy in elderly and nonagenarians presented with ACS is generating hypothesis and merits further studies to confirm these benefits and to guide clinicians in the management of these high-risk patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".