Age- and sex-related differences in heart failure characteristics and treatment patterns across the ejection fraction spectrum: first data from the BRING-UP 3 Heart Failure study
Bibliographic record
Abstract
AIMS: Older adults and females are under-represented in randomized clinical trials, and evidence on age- and sex-related differences in heart failure (HF) characteristics and treatment patterns is limited. OBJECTIVES: To evaluate age- and sex-related differences in clinical characteristics and treatments of an all-comers cohort of HF patients. METHODS AND RESULTS: The BRING-UP 3 HF study is a prospective, observational, multicenter investigation encompassing 179 Italian cardiology sites. Multivariable logistic regression models were applied to evaluate predictors of HF treatments across sex and age strata. Optimal medical therapy (OMT) was defined as the patient receiving all guideline-recommended medications. A total of 5203 HF patients (median age 72 years; 24% female) were included over three months. Females were older, had a higher prevalence of non-ischemic HF etiologies, and were less likely to receive OMT compared with males. Similarly, older patients also experienced suboptimal treatment. After multivariable adjustment only age remained independently associated with a lower likelihood of receiving OMT (odds ratio per 5-year age increase 0.94, 95% confidence interval 0.88-0.93). CONCLUSION: Significant age- and sex-related disparities exist in HF characteristics and treatment patterns. Tailored management strategies are needed to optimize medical therapy for females and older adults, to improve HF outcomes and ensure equitable care. TRIAL REGISTRATION NUMBER: 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".