Abstract 079: Health-related Quality of Life With Routine Invasive versus Ischemia-guided Strategies for the Elderly With Non-ST Elevation Myocardial Infarction (NSTEMI)- Insights From PREMIER/TRIUMPH
Bibliographic record
Abstract
Introduction: In elderly patients presenting with a NSTEMI, a routine invasive strategy reduces cardiovascular events as compared with an ischemia guided approach. However, the association of invasive management on health status outcomes is unknown. Methods: Among patients admitted with NSTEMI from 2 multicenter US AMI registries (TRIUMPH and PREMIER), health status was assessed at baseline and at 1, 6 and 12 months after AMI using the Seattle Angina Questionnaire (SAQ), a CAD-specific health status measure (scores range 0-100, higher scores=better QoL; ~5-points is minimally clinically relevant difference). Invasive management was defined as coronary angiography within 72 hours of admission without a preceding stress test. Propensity-adjusted linear mixed effect models were used to compare health status between treatment strategies over the year after MI. Interactions of age (divided into <65, 65-74, 75-84, ≥85 years) with treatment and with time were tested. Results: Among 3559 patients with NSTEMI, 2455 (69.0%) were treated with a routine invasive strategy. This was more common in younger patients, with 72.4% (1610/ 2223) of patients <65 years, 66.6% (476/715) of those 65-74 years, 63.1% (316/501) of those 75-84 years, and 44.2% (53/120) of those ≥ 85 years treated with a routine invasive strategy (p<0.001). In propensity-adjusted analyses (Table), invasive strategy was associated with a clinically small but statistically significant benefit on SAQ physical limitation, angina frequency, and summary scores over the year following AMI; benefits that were greatest early on. Although patients aged ≥ 85 years had slightly worse health status when treated with an invasive strategy, there was no significant interaction between age and treatment strategy for any of the health status outcomes (p≥ 0.15 for all). Conclusions: In the setting of a NSTEMI, a routine invasive strategy is associated with small but significant health status benefits. These effects were most notable early during follow-up and did not significantly differ by age. While there was no significant interaction of treatment with age, the oldest old group appeared to derive slightly less health status benefit from a routine invasive strategy—results that will need to be confirmed in a larger study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".