Abstract 10061: Association of Frailty with In-Hospital and Long-Term Outcomes Among St-Elevation Myocardial Infarction Patients Receiving Primary PCI
Bibliographic record
Abstract
Introduction: The impact of frailty on outcomes in a contemporary ST-segment-elevation myocardial infarction (STEMI) population is unclear. This study hypothesized that frail STEMI patients undergoing primary percutaneous coronary intervention (pPCI) have worse in-hospital and 1-year outcomes compared to non-frail STEMI patients. Methods: We retrospectively identified 600 STEMI patients who had received pPCI (2013 - 2016). A frailty index (FI) was determined using the health deficit accumulation model (Table 1). Frail patients were defined as those with a FI > 0.25. The composite outcome comprised in-hospital heart failure, cardiogenic shock, re-infarction, major bleeding, stroke and all-cause mortality. A multivariable model adjusting for age and sex was performed. Results: Among 600 STEMI patients receiving pPCI, 67 (11.2%) were classified as frail. Compared to non-frail patients, frail patients were older (mean 80.3 vs. 75.8 years, p < 0.001) and had a higher comorbidity burden. After adjustment, baseline frailty was independently associated with delayed reperfusion time, in-hospital all-cause mortality, and higher incidence of the composite outcome (Figure 1). Frailty was also associated with increased 1-year all-cause mortality and cardiovascular rehospitalization. Conclusions: Among STEMI patients receiving pPCI, 1 in 10 were frail. Frailty was associated with increased rate of the primary in-hospital composite adverse outcome, delayed reperfusion time and worse long-term outcomes. Efforts to routinely identify frail STEMI patients and to implement best practices to reduce the risk of adverse events in this vulnerable population are warranted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".