Real-World Use of SGLT2 Inhibitors for Patients with IgAN and Glomerulonephritis, 2021-2023
Bibliographic record
Abstract
Background: The increasing use of SGLT2 inhibitors (SGLT2i) among patients (pts) with kidney diseases may affect clinical trial design and interpretability for glomerulonephritis (GN) conditions such as IgA nephropathy (IgAN). Methods: This retrospective cohort study characterized SGLT2i use in adult pts with GN, including IgAN, using US-based insurance claims (IQVIA Pharmetrics Plus). Pts were included who: were diagnosed with GN via ICD codes from October 1, 2015, to June 30, 2023, had a kidney biopsy on or prior to diagnosis, and did not use SGLT2i before 2021. SGLT2i use was separately assessed between 2021 and 2023 for GN pts diagnosed before and after 2021. Results: The study included 2703 GN pts (338 IgAN) diagnosed pre-2021 and 2923 pts (354 IgAN) diagnosed post-2021. SGLT2i use was 11.2% in GN and 14.5% in IgAN pts diagnosed pre-2021, and 18.4% in GN and 26% in IgAN pts diagnosed post-2021. SGLT2i use increased from 2021 to 2023 (Fig. 1). Median time to SGLT2i initiation was 4 months in GN and 3 months in IgAN pts diagnosed post-2021. Factors associated with SGLT2i use among GN pts were: male, commercial insurance, and co-existing hypertension and/or diabetes (DM). SGLT2i use was more common among IgAN pts with DM (Fig. 2). Conclusion: SGLT2i use increased, but remained relatively low in pts with GN and IgAN between 2021 and 2023. Current GN/IgAN clinical trials may better reflect real-world use of SGLT2i, which should be considered in trial interpretation and comparison.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".