Management of chronic heart failure in the older population.
Bibliographic record
Abstract
Chronic heart failure (CHF) is the leading cause of hospitalization for those over the age of 65 and represents a significant clinical and economic burden. About half of hospital re-admissions are related to co-morbidities, polypharmacy and disabilities associated with CHF. Moreover, CHF also has an enormous cost in terms of poor prognosis with an average one year mortality of 33%-35%. While more than half of patients with CHF are over 75 years, most clinical trials have included younger patients with a mean age of 61 years. Inadequate data makes treatment decisions challenging for the providers. Older CHF patients are more often female, have less cardiovascular diseases and associated risk factors, but higher rates of non-cardiovascular conditions and diastolic dysfunction. The prevalence of CHF with reduced ejection fraction, ischemic heart disease, and its risk factors declines with age, whereas the prevalence of non-cardiac co-morbidities, such as chronic renal failure, dementia, anemia and malignancy increases with age. Diabetes and hypertension are among the strongest risk factors as predictors of CHF particularly among women with coronary heart disease. This review paper will focus on the specific consideration for CHF assessment in the older population. Management strategies will be reviewed, including non-pharmacologic, pharmacologic, quality care indicators, quality improvement in care transition and lastly, end-of-life issues. Palliative care should be an integral part of an interdisciplinary team approach for a comprehensive care plan over the whole disease trajectory. In addition, frailty contributes valuable prognostic insight incremental to existing risk models and assists clinicians in defining optimal care pathways for their patients.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".