Impact of frailty on outcomes after percutaneous coronary intervention: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Average life expectancy is rising, resulting in increasing numbers of elderly, frail individuals presenting with coronary artery disease and requiring percutaneous coronary intervention (PCI). PCI can be of value for this population, but little is known about the balance of benefit versus risk, particularly in the frail. OBJECTIVE: To determine the relationship between frailty and clinical outcomes in patients undergoing PCI. METHODS: Patients undergoing PCI, for either stable angina or acute coronary syndrome, were prospectively assessed for frailty using the Canadian Study of Health and Ageing Clinical Frailty Scale. Demographics, clinical and angiographic data were extracted from the hospital database. Mortality was obtained from the Office of National Statistics. RESULTS: Frailty was assessed in 745 patients undergoing PCI. The mean age of patients was 62±12 years and 70% were males. The median frailty score was 3 (IQR 2-4). A frailty score ≥5, indicating significant frailty, was present in 81 (11%) patients. Frail patients required longer hospitalisation after PCI. Frailty was also associated with increased 30-day (HR 4.8, 95% CI 1.4 to 16.3, p=0.013) and 1 year mortality (HR 5.9, 95% CI 2.5 to 13.8, p<0.001). Frailty was a predictor of length of hospital stay and mortality, independent of age, gender and comorbidities. CONCLUSIONS: A simple assessment of frailty can help predict mortality and the length of hospital stay, and may therefore guide healthcare providers to plan PCI and appropriate resources for frail 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".