Contemporary Medical Therapy for Heart Failure Across the Ejection Fraction Spectrum: The OPTIPHARM-HF Registry
Bibliographic record
Abstract
AIMS: Despite guideline recommendations, guideline-directed medical therapy (GDMT) remains underused and underdosed in patients with heart failure (HF) across the ejection fraction (EF) spectrum. The aim of this study was to evaluate GDMT use, dosing, and implementation in a contemporary, nationwide HF cohort. METHODS AND RESULTS: The OPTIPHARM-HF (NCT06192524) is a prospective, multicentre, observational study enrolling adult patients with HF, across 32 Italian HF centres. Clinical characteristics, medical therapy prevalence and change after first visit have been assessed in patients with reduced (HFrEF: EF ≤40%), mildly reduced (HFmrEF: EF 40-49%), and preserved EF (HFpEF: EF ≥50%). From September 2022 to December 2024, 3054 patients (mean age 69 ± 12 years, 25% female) were enrolled: 56% with HFrEF, 21% with HFmrEF, and 23% with HFpEF. Among HFrEF, prescription frequencies were: 90% for beta-blockers; 19% for angiotensin-converting enzyme inhibitors (ACEi)/angiotensin II receptor blockers (ARB); 61% for angiotensin receptor-neprilysin inhibitors (ARNI); 72% for mineralocorticoid receptor antagonists (MRA); and 69% for sodium-glucose co-transporter 2 inhibitors (SGLT2i). Less than 60% achieved ≥50% of target doses. Quadruple therapy was received by 47% of the patients. After first visit, there was an increase in prescription of all classes of drugs, and titration to quadruple therapy was attained in 64% (p < 0.001). Among HFmrEF, 88% were on beta-blockers, 34% on ACEi/ARB, 49% on ARNI, 63% on MRA, and 59% on SGLT2i. In the HFpEF group, 76% were on beta-blockers, 49% on ACEi/ARB, 18% on ARNI, 49% on MRA and 40% on SGLT2i. After the first visit, SGLT2i prescription significantly increased both in HFmrEF (74%, p < 0.001) and HFpEF (54%, p < 0.001). CONCLUSIONS: Use of GDMT remains suboptimal across the EF spectrum although the adoption of quadruple GDMT in HFrEF and of SGLT2i in HFmrEF and HFpEF increased in recent years.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".