The Impact of Hormone Replacement Therapy on the Risk of Heart Failure in Postmenopausal Women: A Meta‐Analysis of Clinical and Observational Studies
Bibliographic record
Abstract
PURPOSE: The relationship between heart failure (HF) and hormone replacement therapy (HRT) in postmenopausal women remains unclear. This paper aimed to elucidate the association between HRT and HF outcomes in postmenopausal women by scrutinizing evidence from clinical trials and observational studies. METHODS: The meta-analysis was systematically executed following the PRISMA guidelines to include studies identified from the electronic databases, including PubMed, EMBASE, EBSCO, ICTRP, and NIH clinical trials. The primary endpoint of the effect comprised risk ratios (RR) for HF incidence and mortality, attended by 95% confidence intervals (CIs). The risk of bias was assessed employing the Cochrane Risk of Bias 2 (RoB2) tool for clinical trials and the Newcastle-Ottawa Scale (NOS) for observational studies. RESULTS: The search yielded a total of eight reports, originating from six individual studies, for inclusion in the current study, and 25 047 participants were included. The meta-analysis demonstrated no remarkable association between HRT and the incidence of HF in postmenopausal women (RR: 1.07, 95% CI: 0.91-1.25, p = 0.37). However, a significant reduction in all-cause mortality was observed among post-menopausal HF patients who received HRT (RR: 0.65, 95% CI: 0.49-0.87, p = 0.003). In age-related subgroup analyses, no significant change in the risk of HF was noticed among participants on HRT. CONCLUSIONS: The findings of this paper demonstrate that HRT use is not associated with a significant increase in the risk of incident HF. This meta-analysis also suggests a benefit in all-cause mortality when HRT is administered to postmenopausal women with HF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.051 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".