Efficacy and Safety of Dapagliflozin in Patients With CKD Across Major Geographic Regions
Bibliographic record
Abstract
Introduction This study aimed to examine the efficacy and safety of dapagliflozin in the Dapagliflozin and Prevention of Adverse Outcomes in Chronic Kidney Disease (DAPA-CKD) trial (NCT03036150) by geographic region. Methods Adults with chronic kidney disease (CKD) with or without type 2 diabetes, with estimated glomerular filtration rate (eGFR) 25 to 75 ml/min per 1.73 m 2 and urinary albumin-to-creatinine ratio (UACR) 200 to 5000 mg/g were randomized to dapagliflozin (10 mg once daily) or placebo. The primary end point was a composite of a sustained decline in eGFR of ≥50%, end-stage kidney disease or death from kidney or cardiovascular causes. We categorized recruiting countries into 4 broad global regions: Asia, Europe, Latin America, and North America. Of 4304 randomized patients, 1346 (31.3%) were from Asia, 1233 (28.6%) from Europe, 912 (21.2%) from Latin America, and 813 (18.9%) from North America. Results The relative risk of the primary composite end point was lower in patients randomized to dapagliflozin (relative to placebo) in all regions, with hazard ratios (95% CI) of 0.70 (0.48–1.00), 0.60 (0.43–0.85), 0.61 (0.43–0.86), and 0.51 (0.34–0.76) among patients from Asia, Europe, Latin America, and North America, respectively. There was no effect modification by region (interaction P = 0.77). Occurrence of serious adverse events (SAEs) was lower among patients randomized to dapagliflozin versus placebo (21.9% vs. 26.8%, 34.1% vs. 38.6%, 29.8% vs. 31.5%, and 34.9% vs. 41.0% in Asia, Europe, Latin America, and North America, respectively). Conclusion Dapagliflozin reduced kidney and cardiovascular events and prolonged survival in patients with CKD, with and without type 2 diabetes, with no apparent effect modification by geographic region.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".