Prevalence and Prognosis Impact of Frailty Among Older Adults in Cardiac Intensive Care Units
Bibliographic record
Abstract
Background Whether frailty, defined as a biological syndrome that reflects a state of decreased physiological reserve and vulnerability to stressors, may impact the outcomes of elderly patients admitted to a cardiac intensive care unit (CICU) remains unclear. We aimed to determine the prevalence of frailty and its impact on mortality in patients aged ≥ 80 years admitted to a CICU. Methods This prospective single-centre observational study was conducted among patients aged ≥ 80 years admitted to a CICU in a tertiary centre. Frailty was assessed using the Edmonton Frail Scale (EFS), which provides a score ranging from 0 (not frail) to 17 (very frail). The population was divided into 3 classes: EFS-score of 0-3, EFS-score of 4-6, and EFS-score > 7. Results A total of 199 patients were included, and median follow-up duration was 365 days. The mean age was 84.8 years, and 50 patients (25.1%) died during the follow-up period. In all, 45 (22.6%), 60 (30.2%), and 94 patients (47.2%) had an EFS-score of 0-3, 4-6, and ≥ 7, respectively. The all-cause mortality rate was 4.4%, 27.1%, and 37.2% in the 0-3, 4-6, and ≥ 7 EFS-score groups, respectively ( P < 0.001). After multivariate analysis, frailty status remained associated with all-cause mortality: hazard ratio was 2.60 (95% confidence interval 0.54-12.45) within the 4-6 EFS-score group, and 5.46 (95% confidence interval 1.23-24.08) within the ≥ 7 EFS-score group. Conclusions Frailty is highly prevalent in older adults admitted to the population hospitalized in a CICU and represents a strong prognostic factor for 1-year all-cause mortality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".