Effect of Dapagliflozin on Risk for Fast Decline in eGFR: Analysis from DECLARE-TIMI 58 Trial
Bibliographic record
Abstract
Background: SGLT2 inhibitors may lead to short term decrease in eGFR with later stabilization and long-term reduction in risk for end stage kidney disease. Fast decline (FD) in eGFR can be defined as reduction of ≥3 ml/min/1.73m2/year and is associated with poor long-term renal prognosis. In this post hoc analysis we studied the effect of dapagliflozin (dapa) on risk for FD in the DECLARE-TIMI 58 trial. Methods: In DECLARE-TIMI 58, 17,160 patients with T2D and established or increased risk for CVD, with mean baseline eGFR of 85.2 ml/min/1.73m2, were randomized to dapa vs. placebo and followed for median of 4.2 years. The risk for FD was compared between treatment arms. Results: In the time frame of 0.5 years (after stabilization) to 4 years, the proportion of patients with FD was reduced with dapa vs. placebo (26.8% vs. 37.1%, respectively, p<0.0001) and in all subgroups assessed (Figure). The mean (SD) reduction in eGFR per year was 6.3 (3.7) vs. 0.0 (2.5) ml/min/1.73m2/year in FD (N=4,788) vs. non-FD (N=10,224) patients. In patients that had FD, mean (SD) reduction in eGFR was -5.9 (3.2) vs. -6.6 (4.1) ml/min/1.73m2/year in dapa vs. placebo arm, while in patients that did not have fast decline it was 0.2 (2.5) vs. -0.2(2.5) ml/min/1.73m2/year, respectively. The proportion of patients with FD during entire study period (i.e. 0-4 years) was also reduced with dapa vs. placebo (33.6% vs. 37.0%, respectively, p<0.0001). Conclusions: Dapa reduced the risk for FD in eGFR in a broad population of patients with T2D and relatively preserved renal function, irrespective of patients’ baseline characteristics. Funding: Commercial Support - AstraZeneca
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".