How to Handle Polypharmacy in Heart Failure. A Clinical Consensus Statement of the Heart Failure Association of the ESC
Bibliographic record
Abstract
The multiplicity of coexisting comorbidities affecting patients with heart failure (HF), together with the availability of multiple treatments improving prognosis in HF with reduced ejection fraction, has led to an increase in the number of prescribed medications to each patient. Polypharmacy is defined as the regular use of multiple medications, and over the last years has become an emerging aspect of HF care, particularly in older and frailer patients who are more frequently on multiple treatments, and are therefore more likely exposed to tolerability issues, drug-drug interactions and practical difficulties in management. Polypharmacy negatively affects adherence to treatment, and is associated with a higher risk of adverse drug reactions, impaired quality of life, more hospitalizations and worse prognosis. It is important to adopt and implement strategies for the management of polypharmacy from other medical disciplines, including medication reconciliation, therapeutic revision and treatment prioritization. It is also essential to develop new HF-specific strategies, with the primary goal of avoiding the use of redundant treatments, minimizing adverse drug reactions and interactions, and finally improving adherence. This clinical consensus statement document from the Heart Failure Association of the European Society of Cardiology proposes a rationale, pragmatic and multidisciplinary approach to drug prescription in the current era of multimorbidity and 'multi-medication' in HF.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".