Dosing of losartan in men versus women with heart failure with reduced ejection fraction: the <scp>HEAAL</scp> trial
Bibliographic record
Abstract
AIMS: In heart failure with reduced ejection fraction (HFrEF), guidelines recommend up-titration of angiotensin-converting enzyme inhibitors (ACEi) and angiotensin receptors blockers (ARBs) to the maximum tolerated dose. However, some studies suggest that women might need lower doses of ACEi/ARBs than men to achieve similar treatment benefit. METHODS AND RESULTS: The HEAAL trial compared low vs. high dose of losartan. We reassessed the efficacy and safety of high- vs. low-dose in men vs. women using Cox models and machine learning algorithms. The mean age was 66 years and 30% of patients were women. Men appeared to have benefited more from high-dose than from low-dose losartan, whereas women appeared to have responded similarly to low and high doses [hazard ratio (95% confidence interval) comparing high- vs. low-dose losartan for the composite outcome of all-cause death or all-cause hospitalization: 0.89 (0.81-0.98) in men and 1.10 (0.95-1.28) in women; interaction P = 0.018]. Female sex clustered along with older age, ischaemic heart failure, New York Heart Association class III/IV, and estimated glomerular filtration rate <60 mL/min. Patients with these features had a poorer response to high-dose losartan. Subgroup analyses supported no benefit from high-dose losartan in patients with poorer kidney function and severe heart failure symptoms. CONCLUSIONS: Compared with men, women might need lower doses of losartan to achieve similar treatment benefit. However, beyond sex, other factors (e.g. kidney function, age, and symptoms) may influence the response to high-dose losartan, suggesting that sex-based subgroup findings may be biased by other confounders.
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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 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".