The association of long-term outcome and biological sex in patients with acute heart failure from different geographic regions
Bibliographic record
Abstract
AIMS: Recent data from national registries suggest that acute heart failure (AHF) outcomes might vary in men and women, however, it is not known whether this observation is universal. The aim of this study was to evaluate the association of biological sex and 1-year all-cause mortality in patients with AHF in various regions of the world. METHODS AND RESULTS: We analysed several AHF cohorts including GREAT registry (22 523 patients, mostly from Europe and Asia) and OPTIMIZE-HF (26 376 patients from the USA). Clinical characteristics and medication use at discharge were collected. Hazard ratios (HRs) for 1-year mortality according to biological sex were calculated using a Cox proportional hazards regression model with adjustment for baseline characteristics (e.g. age, comorbidities, clinical and laboratory parameters at admission, left ventricular ejection fraction). In the GREAT registry, women had a lower risk of death in the year following AHF [HR 0.86 (0.79-0.94), P < 0.001 after adjustment]. This was mostly driven by northeast Asia [n = 9135, HR 0.76 (0.67-0.87), P < 0.001], while no significant differences were seen in other countries. In the OPTIMIZE-HF registry, women also had a lower risk of 1-year death [HR 0.93 (0.89-0.97), P < 0.001]. In the GREAT registry, women were less often prescribed with a combination of angiotensin-converting enzyme inhibitors and beta-blockers at discharge (50% vs. 57%, P = 0.001). CONCLUSION: Globally women with AHF have a lower 1-year mortality and less evidenced-based treatment than men. Differences among countries need further investigation. Our findings merit consideration when designing future global clinical trials in AHF.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".