Real-World Safety of Sacubitril/Valsartan in Women and Men With Heart Failure and Reduced Ejection Fraction: A Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Sacubitril/valsartan (SV) is a novel and effective therapy for heart failure with reduced ejection fraction (HFrEF). Despite several sex-specific particularities that may influence drug effects, there has been no prior study evaluating the safety of SV in women with HFrEF in the "real-world." METHODS: We performed a literature search to identify observational studies evaluating SV. We contacted all authors to obtain sex-specific data on major adverse outcomes. We compared all-cause and cardiovascular (CV) deaths, heart failure hospitalizations, hyperkalemia, and hypotension in men and women. RESULTS: We identified five cohort studies enrolling 8,981 patients; 6,092 men (67.8%) and 2,889 women (32.2%). The mean age was 67 years in both sexes. The rates for all-cause mortality, CV mortality, heart failure hospitalizations, hypotension, and hyperkalemia were similar between women and men. Although the unadjusted aggregate rates of all-cause and CV mortalities were numerically higher in men than in women, these differences did not reach statistical differences. CONCLUSION: Our meta-analysis showed similar rates of major adverse events in men and women with HFrEF treated with SV. Larger observational studies with longer duration and a higher number of women are needed to confirm the long-term safety of SV in women in the clinical practice.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.037 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".