Dapagliflozin in Patients with Low and High uACR
Bibliographic record
Abstract
Background: We explored clinical outcomes in new users of dapagliflozin 10 mg with low urine albumin to creatinine ratio (uACR; 30-200 mg/g) and high uACR (≥ 200 mg/g) in patients with chronic kidney disease (CKD), with and without type 2 diabetes (T2D). Methods: This study used claims data from the USA between 2021 and 2022. Patients with and without T2D who were new users of dapagliflozin 10 mg were indexed at treatment start and grouped according to baseline uACR (low or high). Patients on dialysis or immunosuppressive drugs or with polycystic kidney disease or stage 5 CKD were excluded. Incidence of hospitalization for cardiorenal events (CKD or heart failure) and atherosclerotic cardiovascular diseases (ASCVD) were compared separately within non-T2D and T2D populations using age- and sex-adjusted survival regression. Results: In non-T2D patients, 332 (54%) new users of dapagliflozin had low uACR and 286 (46%) had high uACR. Corresponding numbers for patients with T2D were 1987 (53%) and 1741 (47%). New users of dapagliflozin with low and high uACR had similar baseline characteristics, both with and without T2D. In non-T2D patients, event rates for cardiorenal hospitalizations were similar in the low and high uACR groups (8.3 and 9.2 per 100 patient-years, respectively). There was no significant risk difference between low and high uACR (hazard ratio [HR] 1.16, 95% confidence interval [CI] 0.54-2.49; Figure). Similar results were seen in patients with T2D (HR 0.92, 95% CI 0.68-1.24). In contrast, the risk of ASCVD was significantly higher in patients with high uACR versus low uACR (HR 1.67, 95% CI 1.03-2.72). Conclusions: Dapagliflozin's beneficial effect on cardiorenal risks appeared similar in patients with low and high uACR independent of T2D. ASCVD risk remained higher in high versus low uACR patients. These findings suggest cardiorenal effectiveness of dapagliflozin in patients with CKD and low uACR, without T2D. 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".