OPTImal PHARMacological Therapy for Patients with Heart Failure: Rationale and Design of the OPTIPHARM-HF Registry
Bibliographic record
Abstract
AIMS: Patients with heart failure (HF) remain often undertreated for multiple reasons, including treatment inertia, contraindications, and intolerance. The OPTIimal PHARMacological therapy for patients with Heart Failure (OPTIPHARM-HF) registry is designed to evaluate the prevalence of evidence-based medical treatment prescription and titration, as well as the causes of its underuse, in a broad real-world population of consecutive patients with HF across the whole ejection fraction spectrum and among different clinical phenotypes. METHODS: The OPTIPHARM-HF registry (NCT06192524) is a prospective, multicenter, observational, national study of adult patients with symptomatic HF, as defined by current international guidelines, regardless of ejection fraction. Both outpatients and inpatients with chronic and acute decompensated HF will be recruited. The study will enroll up to 2500 patients with chronic HF at approximately 35 Italian HF centres. Patients will be followed for a maximum duration of 24 months. The primary objective of the OPTIPHARM-HF registry is to assess prescription and adherence to evidence-based guideline-directed medical therapy (GDMT) in patients with HF. The primary outcome is to describe the prevalence of GDMT use according to target guideline recommendation. Secondary objectives include implementation of comorbidity treatment, evaluation of sequence of treatment introduction and up-titration, description of GDMT implementation in the specific HF population, main causes of GDMT underuse, and assessment of cumulative rate of cardiovascular events. CONCLUSION: The OPTIPHARM-HF registry will provide important implications for improving patient care and adoption of recommended medical therapy into clinical practice among HF patients.
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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 | 0.047 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".