Practical Management of Frailty in Older Patients with Heart Failure
Bibliographic record
Abstract
AIMS: The heart failure (HF) prognosis in older patients remains poor with a high 5-years mortality rate more frequently attributed to noncardiovascular causes. The complex interplay between frailty and heart failure contribute to poor health outcomes of older adults with HF independently of ejection fraction. The aim of this position paper is to propose a practical management of frailty in older patients with heart failure. METHODS: A panel of multidisciplinary experts on behalf the Heart Failure Working Group of the French Society of Cardiology and on behalf French Society of Geriatrics and Gerontology conducted a systematic literature search on the interlink between frailty and HF, met to propose an early frailty screening by non-geriatricians and to propose ways to implement management plan of frailty. Statements were agreed by expert consensus. RESULTS: Clinically relevant aspects of interlink between frailty and HF have been reported to identify the population eligible for screening and the most suitable screening test(s). The frailty screening program proposed focuses on frailty model defined by an accumulation of deficits including geriatric syndromes, comorbidities, for older patients with HF in different settings of care. The management plan of frailty includes optimization of HF pharmacological treatments and non-surgical device treatment as well as optimization of a global patient-centred biopsychosocial blended collaborative care pathway. CONCLUSION: The current manuscript provides practical recommendations on how to screen and optimize frailty management in older patients with heart failure.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".