Pharmacotherapy for heart failure with reduced ejection fraction and <scp>health‐related</scp> quality of life: a systematic review and <scp>meta‐analysis</scp>
Bibliographic record
Abstract
AIMS: The aim of this study was to synthesize the evidence on the effect of heart failure with reduced ejection fraction (HFrEF) pharmacotherapy on health-related quality of life (HRQoL). METHODS AND RESULTS: We searched MEDLINE, Embase, CENTRAL, CINAHL, ClinicalTrials.gov and the World Health Organization International Clinical Trials Registry Platform in June 2020. Randomized placebo-controlled trials evaluating contemporary HFrEF pharmacotherapy and reporting HRQoL as an outcome were included. Two reviewers independently assessed studies for eligibility, extracted data, and assessed risk of bias and GRADE certainty of evidence. The primary outcome was HRQoL at last available follow-up analysed using a random-effects model. We included 37 studies from 5770 identified articles. Risk of bias was low in 10 trials and high/unclear in 27 trials. High certainty evidence from meta-analyses demonstrated improved HRQoL over placebo with sodium-glucose co-transporter 2 (SGLT2) inhibitors [standardized mean difference (SMD) 0.16, 95% confidence interval (CI) 0.08-0.23] and intravenous iron (SMD 0.52, 95% CI 0.04-1.00). Furthermore, high certainty evidence from ≥1 landmark trial further supported improved HRQoL with angiotensin receptor blockers (ARBs) (SMD 0.09, 95% CI 0.02-0.17), ivabradine (SMD 0.14, 95% CI 0.04-0.23), hydralazine-nitrate (SMD 0.24, 95% CI 0.04-0.44) vs. placebo, and for angiotensin receptor-neprilysin inhibitor (ARNI) compared with an angiotensin-converting enzyme (ACE) inhibitor (SMD 0.09, 95% CI 0.02-0.17). Findings were inconclusive for ACE inhibitors, beta-blockers, digoxin, and oral iron based on low-to-moderate certainty evidence. CONCLUSION: ARBs, ARNIs, SGLT2 inhibitors, ivabradine, hydralazine-nitrate, and intravenous iron improved HRQoL in patients with HFrEF. These findings can be incorporated into discussions with patients to enable shared decision-making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".