Renal Protective Treatment Use for Non-Diabetic CKD in Japan, Sweden, and the United States
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) is an underdiagnosed disease affecting 10% of people worldwide. Appropriate management of CKD delays progression and reduces its burden. Renin-angiotensin system inhibitors (RASis) have been the mainstay of CKD treatment until recently. Here we describe the use of RASis and the sodium-glucose co-transporter-2 inhibitor dapagliflozin in a contemporary population of patients with CKD. Methods: This study used secondary data extracted from electronic health records or claims data sources. Adult patients with CKD (either two estimated glomerular filtration rate [eGFR] measurements ≥ 90 days apart of which both were ≤ 60 mL/min/1.73 m2 or an eGFR ≤ 60 mL/min/1.73 m2 followed by a CKD diagnosis) who were new RASi or dapagliflozin users during 2021-2023 were included. Patients with type 1 or gestational diabetes, stage 5 CKD or on dialysis were excluded. RASi and dapagliflozin doses and persistence were assessed in the year following initiation. Results: Overall, 159 220 patients were included (Japan, 57 222; Sweden, 10 861; USA, 91 137). Median ages were 75, 72 and 72 years, and 63%, 66% and 52% were males in Japan, Sweden and the USA, respectively. Of patients without type 2 diabetes, a high proportion receiving dapagliflozin remained on the evidence-based 10 mg target dose (Figure). A large proportion of patients treated with RASis received low doses. At 12-month follow-up, dapagliflozin persistence was approximately 65%, 80% and 55% in Japan, Sweden and the USA, respectively, and was 34%, 75% and 64% for RASi, respectively. Conclusions: CKD treatment with dapagliflozin was associated with a high likelihood of receiving and remaining on target dose compared with RASi treatment. Efforts to maintain patients on renal protective treatment are needed. 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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".