Dapagliflozin treatment of patients with chronic kidney disease without diabetes across different albuminuria levels (OPTIMISE-CKD)
Bibliographic record
Abstract
ABSTRACT Background We compared kidney and cardiorenal protection in patients without type 2 diabetes across urine albumin–creatinine ratio (UACR) levels after initiation on dapagliflozin for the treatment of chronic kidney disease (CKD). Methods OPTIMISE-CKD is an observational study describing dapagliflozin treatment for CKD. Adult patients with CKD without type 2 diabetes were included in the primary analysis. Baseline UACR was grouped as normal/mildly elevated (0–29 mg/g), low (30–200 mg/g) and high (>200 mg/g). Outcomes were estimated glomerular filtration rate (eGFR) trajectories/slopes, cardiorenal complications and all-cause mortality. Results In total, 1480 patients had low (n = 796) and high (n = 684) UACR. The two groups were similar at baseline, aged 75 and 74 years, and 42% and 39% female, respectively. After dapagliflozin initiation, an acute eGFR dip of 3 mL/min/1.73 m2 was observed, followed by a flat development in both groups. The eGFR slope [95% confidence interval (CI)] for patients with low UACR was 0.79 mL/min/1.73 m2 per year (–0.59, 2.56), and similar to patients with high UACR [0.40 mL/min/1.73 m2 per year (–0.46, 1.38)]. Risks of cardiorenal complications and all-cause mortality were similar, with adjusted hazard ratios of 0.89 (95% CI 0.66, 1.19) and 1.10 (95% CI 0.63, 1.92), respectively. Analogous results were found in those with normal/mildly elevated UACR. Conclusions Dapagliflozin in patients without type 2 diabetes for the treatment of CKD demonstrated similar kidney protection, cardiorenal and all-cause mortality risk across UACR levels. This suggests that the efficacy of dapagliflozin found in clinical trials expands to real-world patients with CKD, regardless of albuminuria levels.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".