Abstract 11087: Impact of Frailty on Short-Term and Long-Term Outcomes Among St-Elevation Myocardial Infarction Patients Receiving Primary Percutaneous Coronary Intervention
Bibliographic record
Abstract
Introduction: The impact of frailty on short-term and long-term outcomes in a contemporary STEMI population is unclear. We hypothesized that in STEMI patients undergoing primary percutaneous coronary intervention (pPCI) frailty would be independently associated with adverse in-hospital and 1-year outcomes. Methods: We retrospectively identified 1,579 STEMI patients aged ≥ 65 years who had received pPCI (2007 - 2020). 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 primary outcome was 1-year all-cause mortality. The composite adverse outcome comprised in-hospital all-cause mortality, cardiogenic shock, heart failure, re-infarction, major bleeding, or stroke. A multivariable model adjusting for age, sex, heart failure on presentation, infarct territory, prolonged reperfusion time, initial heart rate, and systolic blood pressure was performed. Results: There were 228 (14.4%) frail patients. Compared to non-frail patients, frail patients were older (mean 80.3 vs. 75.3 years, p < 0.001) and had a higher comorbidity burden. After multivariable adjustment, baseline frailty was independently associated with increased 1-year all-cause mortality, in-hospital all-cause mortality, and the composite adverse outcome (Figure 1). Conclusions: In conclusion, among STEMI patients receiving pPCI, frailty was common and was independently associated with increased in-hospital and long-term adverse outcomes. These findings raise the need for early recognition of frailty and implementation of a comprehensive care model towards the management of frail patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".