Impact of frailty on the effectiveness of intervention to reduce adverse outcomes in heart failure
Bibliographic record
Abstract
Abstract Objective Heart failure (HF) is a leading cause of hospital readmission and mortality, particularly among frail older adults. This study evaluates the impact of physical, cognitive, and social frailty on the effectiveness of a disease management program (DMP) in reducing hospital readmission and mortality in HF. Methods A total of 1,070 HF patients from six Australian hospitals (2014–2017) were included. Frailty was assessed using validated tools: Fried Phenotype (physical frailty), Montreal Cognitive Assessment (MoCA) (cognitive frailty), and Makizako’s questionnaire (social frailty). Patients were stratified into usual care (n=636) or DMP (n=434). Primary and secondary outcomes were all-cause readmission or death at 1-month and 3-month post-discharge. Multivariable logistic regression models adjusted for confounders evaluated associations. Results All three frailty domains independently predicted higher readmission and mortality at 1-month and 3-month follow-ups. Physical frailty increased risk by 1.69 times (95% CI: 1.02–2.80), cognitive frailty by 2.03 times (95% CI: 1.48–2.77), and social frailty by 2.07 times (95% CI: 1.41–3.02). The effectiveness of the DMP was greater for HF patients with either physical or cognitive frailty and appeared to be greatest for those with both physical and cognitive frailty. Presence of social frailty did not modify the effectiveness of the DMP. Conclusion Presence of frailty predicts worse post-discharge outcomes in HF and may determine how HF patients respond to a DMP. Screening for physical and cognitive frailty in HF patients before implementation of a DMP may allow personalized plans to maximize the effects of the DMP.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".