<p>Frailty in Older Patients with Acute Coronary Syndrome in Vietnam</p>
Bibliographic record
Abstract
BACKGROUND: There has been limited evidence about frailty in older patients with acute coronary syndrome (ACS) in Vietnam. AIM: (1) To investigate the prevalence of frailty in older patients hospitalised with ACS and its associated factors; (2) To investigate the impact of frailty on percutaneous coronary intervention (PCI) and adverse outcomes in this population. METHODS: Patients aged ≥60 with ACS admitted to two teaching hospitals in Vietnam were recruited from 9/2017 to 4/2018. Frailty was defined by the Reported Edmonton Frail Scale. Multivariate logistic regression was applied to investigate the associated factors of frailty and the impact of frailty on PCI and adverse outcomes. RESULTS: There were 324 participants, mean age 73.5±8.3, 39.2% female. The prevalence of frailty was 48.1%. Advanced age, female gender, history of hypertension, heart failure, stroke and chronic kidney disease were significantly associated with a frailty status. Overall, 50.3% of the participants received PCI (58.3% in the non-frail vs 41.7% in the frail, p=0.003). However, frailty did not have an independent impact on PCI (adjusted OR 0.66, 95% CI 0.41-1.08). Frailty was significantly associated with increased risk of having arrhythmia during hospitalisation (adjusted OR 2.24, 95% CI 1.32-3.80), hospital-acquired pneumonia (adjusted OR 2.27, 95% CI 1.24-4.17), in-hospital mortality (adjusted OR 3.02, 95% CI 1.35-6.75), 30-day mortality (adjusted OR 3.28, 95% CI 1.59-6.76), and 30-day readmission (adjusted OR 2.53, 95% CI 1.38-4.63). CONCLUSION: In this study, frailty was present in nearly half of older patients with ACS and was associated with increased adverse outcomes. These findings suggest that frailty screening should be performed in older patients with ACS in Vietnam.
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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 | 0.000 | 0.001 |
| 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".