Effects of Sacubitril/Valsartan According to Polypharmacy Status in PARAGON-HF
Bibliographic record
Abstract
Abstract Aims Patients with heart failure (HF) and preserved ejection fraction (HFpEF) have a particularly high prevalence of comorbidities, often necessitating treatment with many medications. The aim of this study was to evaluate the association between polypharmacy status and outcomes in PARAGON-HF. Methods and results In this post hoc analysis, baseline medication status was available in 4793 of 4796 patients included in the primary analysis of PARAGON-HF. The effects of sacubitril/valsartan, compared with valsartan, were assessed according to the number of medications at baseline: 683 non-polypharmacy (<5 medications); 2750 polypharmacy (5–9 medications), and 1360 hyper-polypharmacy (≥10 medications). The primary outcome was total HF hospitalizations and cardiovascular deaths. Patients with hyper-polypharmacy were older, had more severe limitations due to HF (worse New York Heart Association class and Kansas City Cardiomyopathy Questionnaire scores), and had greater comorbidity. The non-adjusted risk of the primary outcome was significantly higher in patients taking more medications, and similar trends were seen for HF hospitalization and cardiovascular and all-cause death. The effect of sacubitril/valsartan versus valsartan on the primary outcome from the lowest to highest polypharmacy category was (as a rate ratio): 1.19 (0.76–1.85), 0.94 (0.77–1.15), and 0.77 (0.61–0.96) (pinteraction = 0.16). Treatment-related adverse events were more common in patients in the higher polypharmacy categories but not more common with sacubitril/valsartan, versus valsartan, in any polypharmacy category. Conclusions Polypharmacy is very common in patients with HFpEF, and those with polypharmacy have worse clinical status and a higher rate of non-fatal and fatal outcomes. The benefit of sacubitril/valsartan was not diminished in patients taking a larger number of medications at baseline.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".