Sociodemographic disparities in sodium‐glucose cotransporter‐2 inhibitor use among US kidney transplant recipients: An observational study of real‐world pharmacy records
Bibliographic record
Abstract
Abstract Background Recent clinical trials demonstrate benefits of sodium‐glucose cotransporter‐2 inhibitors (SGLT2i) in patients with chronic kidney disease, but data on use in kidney transplant (KTx) recipients are limited. Methods We examined a novel database linking SRTR registry data for KTx recipients (2000–2021) with outpatient fill records from a large pharmaceutical claims warehouse (2015–2021). Adult (≥18 years) KTx recipients treated with SGLT2i were compared to those who received other noninsulin diabetes medications without SGLT2i. Characteristics associated with SGLT2i use were quantified by multivariable logistic regression (adjusted odds ratio, 95%LCL aOR 95%UCL ). Results Among 18 988 KTx recipients treated with noninsulin diabetes agents in the study period, 2224 filled an SGLT2i. Mean time from KTx to prescription was 6.7 years for SGLT2i versus 4.7 years for non‐SGLT2i medications. SGLT2i use was more common in Asian adults (aOR, 1.09 1.31 1.58 ) and those aged > 30–59 years (compared with 18–30 years) or with BMI > 35 kg/m 2 (aOR, 1.19 1.41 1.67 ), and trended higher with self‐pay status. SGLT2i use was lower among KTx recipients who were women (aOR, .79 .87 .96 ), Black (aOR, .77 .88 1.00 ) and other (aOR, .52 .75 1.07 ) race, publicly insured (aOR, .82 .92 1.03 ), or with less than college education (aOR, .78 .87 .96 ), and trended lower in those age 75 years and older. SGLT2i use in KTx patients increased dramatically in 2019–2021 (aOR, 5.01 5.63 6.33 vs. prior years). Conclusion SGLT2i use is increasing in KTx recipients but varies with factors including race, education, and insurance. While ongoing study is needed to define risks and benefits of SGLT2i use in KTx patients, attention should also focus on reducing treatment disparities related to sociodemographic traits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".