Medical Treatments in Patients With Ambulatory Heart Failure: First Data From the BRING-UP-3 Heart Failure Study
Bibliographic record
Abstract
BACKGROUND: Current European Society of Cardiology guidelines introduced a 4-pillar approach for the treatment of HFrEF and a class IA recommendations for empagliflozin and dapagliflozin in HFmrEF and HFpEF. OBJECTIVES: The BRING-UP-3 Heart Failure (HF) study was designed to guide the Guideline-implementation recommendations for patients with HF enrolled in a large sample of Italian cardiology sites. METHODS: The BRING-UP-3 HF study is an observational, prospective, nationwide investigation encompassing 179 sites and enrolling ambulatory and hospitalized patients with HF. The study includes an educational intervention followed by 2 3-month enrolment periods and by a 6-month follow-up period with end-point evaluation. For patients with HFrEF, the objective is to describe the proportion of patients who receive the 4 pillars. Here we present the baseline data of the ambulatory cohort. RESULTS: A total of 3830 ambulatory patients were included in the study. The mean age was 70 ± 12 years (34.5% older than 75 years), females were 21.9%. The most prevalent group was HFrEF (58.4%), followed by HFimpEF (17.4%), HFmrEF (14.4%), and HFpEF (9.8%). Hypertension, atrial fibrillation, diabetes mellitus, and chronic kidney disease were reported in 68.2%, 40.4%, 31.0%, and 33.1%, respectively. In patients with HFrEF, a high prescription rate (65%) for the 4 therapeutic pillars was observed; beta-blockers and RASis (mostly ARNIs) were prescribed in over 90%, while SGLT2is and MRAs were prescribed in over 80% of cases. In HFmrEF and HFpEF, SGLT2i prescription rates reached 72.1% and 50.1%, respectively. CONCLUSIONS: A comprehensive analysis of a large sample of Italian cardiology sites revealed a high prevalence of prescription of guideline-recommended treatments. CLINICALTRIAL: GOV: NCT06279988.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".