Abstract 17064: Frailty Predicts Mortality and Hospital Readmission Amongst Elderly Heart Failure (HF) Patients Discharged From Hospital or the Emergency Department (ED)
Bibliographic record
Abstract
Introduction: Frailty is commonly encountered in elderly patients with HF. However, the impact of both frailty and HF on outcomes in the population has not been delineated. There is a need for operationalizable definitions of frailty to explore variations in care among the elderly. Objective: To assess common indicators of frailty as predictors of mortality and rehospitalisation within 1 year in a population-based study of very elderly HF patients. Methods: Community-dwelling HF patients aged >75 yrs discharged from hospital or ED in Ontario, Canada, were studied using the Enhanced Feedback for Effective Cardiac Treatment and Emergency HF Mortality Risk Grade databases. Surrogates of frailty, including dementia, falls, incontinence, immobility, caregiver dependence, and abnormal weight loss, were identified from chart abstraction and linked hospitalization records. Survival analyses were performed using multiple Cox regression, for frail vs non-frail status and for each frailty component. Results: 9964 patients were identified (age 84±5 yrs). After adjustment for age, sex, vital signs, laboratory variables, and comorbidities, presence of any frailty indicator was associated with mortality: adjusted hazard ratio [HR] 1.18 (95%CI; 1.06-1.32, p=0.003). Frailty increased the risk of readmission and the association was more pronounced than for mortality (see Figure ): adjusted HR 1.29 (95%CI; 1.18-1.41, p<.001). Among the individual frailty components, significant predictors of mortality were: dementia (HR 1.49, 95%CI; 1.34-1.64, p<.001) and care provider dependency (HR 1.17, 95%CI; 1.01-1.36, p=0.038). In contrast, other frailty components increased the risk of readmission, specifically: falls (HR 1.17, 95%CI; 1.06-1.29, p=0.003) and incontinence (HR 1.64, 95%CI; 1.18-2.29, p=0.004). Conclusion: Frailty indicators derived from clinical and administrative data sources are associated with death and readmission in a very elderly HF cohort.
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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 | 0.002 |
| 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) | 0.004 | 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".