Frailty is associated with worse outcomes in acute coronary syndromes: outcomes in TRILOGY
Bibliographic record
Abstract
Purpose: Little is known about frailty in ACS patients and there is no information about the safety and efficacy of P2Y12 antagonists in this setting. We therefore assessed the impact of frailty in ACS patients enrolled in TRILOGY. Methods: TRILOGY randomized 9326 patients with unstable angina or NSTEMI who were planned for medical management without revascularization to receive prasugrel (10 mg/d; 5 mg/d for patients ≥75 y or <60 kg) or clopidogrel 75 mg/d. The primary endpoint was a composite of cardiovascular death, MI, or stroke. The Fried Frailty Score was administered to 4699 (99.9%) patients >65 y. Score items included weight loss, exhaustion, physical activity, walk time, and grip strength. Association of frailty with the primary endpoint was adjusted for GRACE Risk Score covariates; HRs and 95% CIs are presented. Results: 72.3% of patients were classed as not frail, 23.0% as pre-frail (1-2 items), and 5.1% as frail (≥3 items). Increasing frailty scores were associated with most risk factors and with increasing age: 0 items, 73.0 y; 1-2 items, 74.0 y; ≥3 items, 75.0 y; female sex (46.0%, 45.9%, and 50.2%); and higher GRACE scores (134.0, 138.0, and 144.0), respectively. Ischemic outcomes and bleeding are shown in the table. Frailty was significantly associated with the primary endpoint (pre-frail vs not frail: adjusted HR 1.33 [95% CI 1.11-1.60]; frail vs not frail: 1.55 [1.12-2.13], p<0.001). Table 1. Ischemic and bleeding outcomes Conclusions: Frailty is strongly associated with the composite of cardiovascular death, MI, or stroke as well as all-cause mortality. No association of frailty with bleeding was observed in the TRILOGY trial.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".