Association of Frailty With In-hospital and Long-term Outcomes Among STEMI Patients Receiving Primary Percutaneous Coronary Intervention
Bibliographic record
Abstract
BackgroundFrailty 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).MethodsThis 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.Results1,579 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).ConclusionIn 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".