Polypill in heart failure: a pathway to simplified treatment and improved adherence and outcomes
Bibliographic record
Abstract
Heart failure (HF) remains a global health challenge that imposes significant clinical and economic burden. Treatment adherence to guideline-directed medical therapy (GDMT) remains a major challenge in the management of HF, despite the availability of guideline-directed medical therapy (GDMT). Polypharmacy and regimen complexity contribute to poor adherence, particularly among older adults and in resource-limited settings. The polypill strategy, involving fixed-dose combinations of essential HF medications, has emerged as a potential solution to simplify treatment regimens, enhance adherence, and improve clinical outcomes. This review explores the potential of polypill therapy as a pragmatic strategy to simplify HF treatment and improve adherence. Drawing on its successful application in other cardiovascular diseases, we propose two implementation approaches for HF: early low-dose initiation for newly diagnosed patients or switching to a pre-specified dose polypill for stable, optimized patients. This review discusses formulations tailored to different HF phenotypes and highlights ongoing clinical trials assessing the efficacy and safety of the polypill in the HF setting. While the polypill approach offers promising benefits, i.e., improved adherence, affordability, and streamlined care, critical considerations regarding the selection of optimal drug components, identification and elimination of potential drug-drug interactions, the definition of appropriate flexible dose combinations, and patient-specific factors are crucial. Future research, particularly real-world clinical trials, is essential to comprehensively evaluate the efficacy, safety, and feasibility of polypill therapy in diverse HF patient populations, ensuring its responsible integration into clinical practice across diverse healthcare settings to mitigate the persistent burden of 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".