Baseline Kidney Function and the Effects of Dapagliflozin On Health Status in Heart Failure in DEFINE-HF and PRESERVED-HF
Bibliographic record
Abstract
Abstract Aims Sodium–glucose co-transporter-2 (SGLT2) inhibitors improve health status and outcomes in the setting of heart failure (HF) across the range of ejection fraction (EF). Baseline kidney disease is common in HF, complicates HF management and is strongly linked to worse health status. This study aimed to assess whether the treatment effects of dapagliflozin on health status vary based on estimated glomerular filtration rate (eGFR). Methods and Results We conducted a pooled participant-level analysis of two double-blind, randomized trials, DEFINE-HF (n = 236) and PRESERVED-HF (n = 324), which evaluated dapagliflozin versus placebo. Both multicentre studies enrolled adults with HF, New York Heart Association Class II or higher, elevated natriuretic peptides, and an EF < 40% in DEFINE-HF or >45% in PRESERVED-HF. The primary exposure was eGFR. The main outcome was the Kansas City Cardiomyopathy Questionnaire Clinical Summary Score (KCCQ-CSS) at 12 weeks. Across both trials, there were 583 (99.3%) participants with a baseline eGFR. The median (25th, 75th) eGFR was 59 (46, 77) mL/min/1.73 m2. Dapagliflozin improved KCCQ-CSS at 12 weeks [placebo-adjusted difference, +5.0 points, 95% confidence interval (CI) 2.6–7.5; P < 0.001], and this was consistent in participants with an eGFR ≥ 60 (+6.0 points, 95% CI 2.4–9.7; P = 0.001) and eGFR < 60 (+4.1 points, 95% CI 0.5–7.7; P = 0.025) (P interaction = 0.46). The benefits of dapagliflozin on KCCQ-CSS remained robust across eGFR when modelled as a continuous variable (P interaction = 0.48). Conclusions Dapagliflozin led to early and clinically meaningful improvements in health status in HF patients, regardless of EF or baseline eGFR.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".