Association of Frailty With In-hospital and Long-term Outcomes Among STEMI Patients Receiving Primary Percutaneous Coronary Intervention
Bibliographic record
Abstract
Background Frailty is generally a marker of worse prognosis. The impact of frailty on both in-hospital and long-term outcomes in ST-segment-elevation myocardial infarction (STEMI) patients has not been well described. Given this, we aimed to determine the prevalence and impact of frailty on in-hospital and 1-year outcomes in STEMI patients undergoing primary percutaneous coronary intervention (pPCI). Methods This retrospective study reviewed STEMI patients ≥ 65 years who underwent pPCI at the two pPCI-capable hospitals at Vancouver Coastal Health. A frailty index (FI) was determined using a deficit accumulation model, with those with a FI > 0.25 being defined as frail. The primary outcome was 1-year all-cause mortality. The secondary outcomes included in-hospital all-cause mortality, a composite of adverse in-hospital outcomes (all-cause mortality, cardiogenic shock, heart failure, re-infarction, major bleeding, or stroke), and the individual components of the composite. Results 1,579 patients were reviewed, of which 228 (14.4%) were frail. After multivariable adjustment, greater frailty (i.e., increasing FI) was associated with increased in-hospital all-cause mortality (odds ratio [OR], 1.88; 95% confidence interval [CI], 1.50-2.35, P<0.001), the composite adverse in-hospital outcome (OR, 1.46; 95% CI, 1.27-1.68, P<0.001) and 1-year all-cause mortality (OR, 1.48; 95% CI, 1.10-2.00, P=0.011). Conclusion In a contemporary STEMI cohort of older patients receiving pPCI, 1 in 7 patients were frail, with greater frailty being independently associated with increased in-hospital and long-term adverse outcomes. These findings raise the need for the early recognition of frailty and implementation of an interdisciplinary approach towards the management of frail STEMI 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".