Clinical Profile and Treatment Adherence in Patients with Type 2 Diabetes and Chronic Kidney Disease Who Initiate an SGLT2 Inhibitor: A Multi-cohort Study
Bibliographic record
Abstract
The clinical landscape for the treatment of patients with chronic kidney disease (CKD) and type 2 diabetes (T2D) is rapidly evolving. As part of the FOUNTAIN platform (NCT05526157; EUPAS48148), we described and compared cohorts of adult patients with CKD and T2D initiating a sodium-glucose cotransporter 2 inhibitor (SGLT2i) before the launch of finerenone in Europe, Japan, and the United States (US). This was a multinational, multi-cohort study of patients with T2D in five data sources: the Danish National Health Registers (DNHR) (Denmark), PHARMO Data Network (The Netherlands), Valencia Health System Integrated Database (VID) (Spain), Japan Chronic Kidney Disease Database Extension (J-CKD-DB-Ex) (Japan), and Optum’s de-identified Clinformatics ® Data Mart Database (CDM) (US). Eligible patients had CKD (based on either diagnosis codes, eGFR values, and/or urine ACR) and initiated an SGLT2i between 2012 and 2021. Baseline demographic, lifestyle, and clinical characteristics were analyzed, and drug utilization patterns were described. The final cohorts included 21,739 patients in DNHR, 381 in PHARMO, 31,785 in VID, 1157 in J-CKD-DB-Ex, and 56,219 in CDM. Across data sources, approximately 41–70% had CKD stage 1 or 2 at baseline; severe CKD (stage 4) was uncommon (1.6–6.7%). The median duration of SGLT2i therapy ranged from 7.5 months in PHARMO to 17.0 months in VID. At least 50% of patients were currently receiving SGLT2i treatment at 1 year after initiation. At a 1-year follow-up, at least half of the patients with CKD and T2D were receiving SGLT2i treatment across the data sources. In patients initiating SGLT2i, treatment options for T2D and CKD were heterogeneous and dynamic within and among data sources.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".