Impact of SGLT2 Inhibitors on Quality of Life in Heart Failure Across the Ejection Fraction Spectrum: Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: Use of a sodium-glucose cotransporter-2 inhibitor (SGLT2i) reduces hospitalization in heart failure (HF) patients across the spectrum of ejection fraction, but no study has comprehensively explored their impact on quality of life (QoL) with respect to different subgroup populations. We aimed to explore the QoL impact of SGLT2i use in HF patients across the spectrum of ejection fraction and over time. Methods: We searched MEDLINE, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) covering the period from 2019 to February 2022. We included placebo-controlled randomized controlled trials (RCTs) enrolling HF patients that evaluated QoL as an outcome. Two reviewers independently assessed studies for eligibility, extracted data, and assessed risk of bias (RoB), using the Cochrane RoB2 tool, and certainty of evidence, using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. Primary and secondary outcomes were the mean difference in QoL, and clinically important improvement in QoL, as defined in the original study, respectively. We conducted subgroup analyses based on ejection fraction category, SGLT2i agent, and timing of QoL measurement. Results: From 1477 identified reports, we included 14 RCTs (n = 23,361). The mean age was 68 years, and 34% were female. All included RCTs reported QoL using the Kansas City Cardiomyopathy Questionnaire (KCCQ). SGLT2i use improved KCCQ-overall summary score, compared with placebo (mean difference 2.0, 95% confidence interval 1.6-2.5; high certainty). More patients receiving an SGLT2i achieved a clinically important QoL improvement (risk ratio 1.14, 95% confidence interval 1.02-1.28; moderate certainty). Similar improvements were observed in the KCCQ clinical summary and total symptom subscores, and across all subgroups and timeframes. Conclusions: Use of an SGLT2i consistently provides a clinically important improvement in QoL among patients with HF, regardless of ejection fraction, with noticeable improvements seen as early as week 2.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.025 | 0.036 |
| Bibliometrics | 0.007 | 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) | 0.004 | 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".