Sex-Specific Analysis of the Rapid Up-Titration of Guideline-Directed Medical Therapies After a Hospitalization for Acute Heart Failure: Insights from the STRONG-HF Trial
Bibliographic record
Abstract
AIMS: The aim of this study was to evaluate efficacy and safety of rapid up-titration of guideline-directed medical therapies (GDMT) in men and women hospitalized for acute heart failure (AHF). METHODS AND RESULTS: In STRONG-HF, AHF patients were randomized just prior to discharge to either usual care (UC) or a high-intensity care (HIC) strategy of GDMT up-titration. In these analyses, we compared the implementation, efficacy, and safety of the HIC strategy between men and women. In the randomized AHF population, 416/1078 (39%) were women. By day 90, a higher proportion of both sexes in the HIC group had been up-titrated to full doses of GDMT compared to UC. Overall, there were no differences in the primary endpoint between the sexes. The primary endpoint, 180-day heart failure readmission or death, occurred in 15.8% HIC women versus 23.5% women in the UC group (adjusted hazard ratio [HR] 0.67, 95% confidence interval [CI] 0.40-1.13) and in 14.9% HIC men versus 23.5% UC men (adjusted HR 0.57, 95% CI 0.38-0.88) (adjusted interaction p = 0.65). There was no significant treatment-by-sex interaction in quality-of-life improvement or in adverse events, including serious or fatal adverse events. CONCLUSION: The results of the current analyses suggest that a rapid up-titration of GDMT immediately after an AHF hospitalization can and should be implemented similarly in men and women, as it results in reduction of 180-day all-cause death or heart failure readmission, quality-of-life improvement in both men and women with a similar safety profile.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".