Prescribing patterns and factors associated with sodium–glucose cotransporter-2 inhibitor prescribing in patients with diabetes mellitus and atherosclerotic cardiovascular disease
Bibliographic record
Abstract
Background Sodium–glucose cotransporter-2 (SGLT2) inhibitors are cardioprotective agents in patients with type 2 diabetes mellitus and atherosclerotic cardiovascular disease (CVD). Since little is known about their uptake in atherosclerotic CVD, we examined SGLT2 inhibitor prescribing trends and identified potential disparities in prescribing patterns. Methods We conducted an observational study using linked population-based health data in Ontario, Canada, from April 2016 to March 2020 of patients aged 65 years or older with concomitant type 2 diabetes and atherosclerotic CVD. To examine prevalent prescribing of SGLT2 inhibitors (canagliflozin, dapagliflozin and empagliflozin), we constructed 4 cross-sectional yearly cohorts from Apr. 1 to Mar. 31 (2016/17, 2017/18, 2018/19 and 2019/20). We estimated prevalent SGLT2 inhibitor prescribing by year and by subgroups, and identified factors associated with SGTL2 inhibitor prescribing using multivariable logistic regression. Results There were 208 303 patients in our overall cohort (median age 74.0 yr [interquartile range 68.0–80.0 yr], 132 196 [63.5%] male). Although SGLT2 inhibitor prescribing increased over time, from 7.0% to 20.1%, statin prescribing was initially 10-fold higher and later threefold higher than SGLT2 inhibitor prescribing. In 2019/20, SGLT2 inhibitor prescribing was roughly 50% lower among those aged 75 years or older than among those younger than 75 years (12.9% v. 28.3%, p < 0.001) and in women than in men (15.3% v. 22.9%, p < 0.001). Age 75 years or older, female sex, history of heart failure and kidney disease, and low income were independent factors of lower SGLT2 inhibitor prescribing. Among physician specialists, visits to endocrinologists and family physicians were stronger factors of SGLT2 inhibitor prescribing than cardiologist visits. Interpretation We found that 1 in 5 patients with diabetes and atherosclerotic CVD were prescribed SGLT2 inhibitors in 2019/20, whereas statins were prescribed for 4 of every 5 patients. Although SGLT2 inhibitor prescribing increased over the study period, disparities in adoption by age, sex, socioeconomic status, comorbidities and physician specialty remained.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".