DAPA-CKD: A Regional Analysis of Kidney and Cardiovascular Outcomes
Bibliographic record
Abstract
Background: The DAPA-CKD trial (NCT03036150) demonstrated that dapagliflozin reduced the risk of kidney and cardiovascular (CV) events in patients with chronic kidney disease (CKD) and albuminuria, with and without type 2 diabetes. We aimed to determine whether the effects of dapagliflozin varied by pre-specified geographic region. Methods: We randomized 4304 adults with baseline estimated glomerular filtration rate (eGFR) 25-75 mL/min/1.73m2 and urinary albumin-to-creatinine ratio 200-5000 mg/g to dapagliflozin 10mg or placebo once daily; median follow-up was 2.4 years. We compared baseline data, primary and secondary outcomes, and safety of the 4 regions (Asia, Northern America, Latin America, Europe). Results: Compared to other regions, participants from Asia had lower body mass index, less frequent use of diuretics and better blood pressure control. The figure displays the primary and secondary outcomes by region and treatment assignment. Dapagliflozin consistently reduced the risk of the primary composite endpoint (eGFR decline ≥50%, end-stage kidney disease, or kidney or CV death) across the 4 regions by 30 to 49%, with no significant heterogeneity (p=0.77). Similarly, there was no evidence of differences in secondary outcomes between regions. Serious adverse events in the dapagliflozin and placebo groups were similar across the 4 regions. Conclusions: Despite differences in patient characteristics, the beneficial effects of dapagliflozin on kidney and CV endpoints in patients with CKD and albuminuria were similar across pre-specified geographic regions. 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) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".