Definition of Polypharmacy in Heart Failure: A Scoping Review of the Literature
Bibliographic record
Abstract
Patients with heart failure (HF) have a high prevalence of polypharmacy, which can lead to drug interactions, cognitive impairment, and medication non-compliance. However, the definition of polypharmacy in these patients is still inconsistent. The aim of this scoping review was to find the most common definition of polypharmacy in HF patients. We conducted a scoping review searching Medline, Embase, CINAHL, and Cochrane using terms including polypharmacy, HF and deprescribing, which resulted in 7,949 articles. Articles without a definition of polypharmacy in HF patients and articles which included patients < 18 years of age were excluded; only 59 articles were included. Of the 59 articles, 49% (n = 29) were retrospective, 20% (n = 12) were prospective, 10% (n = 6) were cross-sectional, and 27% (n = 16) were review articles. Twenty percent (n = 12) of the articles focused on HF with reduced ejection fraction, 10% (n = 6) focused on HF with preserved ejection fraction and 69% (n = 41) articles either focused on both diagnoses or did not clarify the specific type of HF. The most common cutoff for polypharmacy in HF was five medications (59%, n = 35). There was no consensus regarding the inclusion or exclusion of over-the-counter medications, supplements, or vitamins. Some newer studies used a cutoff of 10 medications (14%, n = 8), and this may be a more practical and meaningful definition for HF 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 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 | 0.016 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.035 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".