Abstract 10401: Uptake and Predictors of SGLT2i in Patients with Type 2 Diabetes and Cardiovascular Disease
Bibliographic record
Abstract
Introduction: While sodium glucose cotransporter 2 inhibitors (SGLT2i) improve cardiovascular outcomes in type 2 diabetes (T2D) and cardiovascular disease (CVD), their uptake in clinical practice in this population is unclear. Therefore, we evaluated trends in SGLT2i use and identified predictors of use in patients with concomitant T2D and CVD. Methods: We conducted an observational study using population-based health data in Ontario, Canada from 4/2016-3/2020 of patients ≥65 years old with a first diagnosis of T2D plus CVD. In each yearly cohort, we estimated the prevalent use of SGLT2i (≥1 prescription) within 1 year, including by demographic and comorbidity subgroups. In the overall cohort, we used multivariable logistic regression models to identify factors associated with SGLT2i use. Results: We identified 208,303 patients with T2D and CVD [median age 74.0 years (IQR 68.0-80.0); 63.5% male]. The proportion of patients dispensed an SGLT2i increased over time, from 7.0% in 2016 to 20.1% in 2019. In 2019, use in those ≥75 years was 50% lower than those <75 years (12.9% vs. 28.3%;p<.0001), and use in those without chronic kidney disease (CKD) was 50% higher than those with CKD (22.0% vs. 15.0%;p<.0001); although SGLT2i use increased in all groups over time. Independent factors associated with increased odds of SGLT2i use were HbA1c ≥8 vs. <7 (OR 3.33, 95%CI:3.22-3.46), use of 2+ non-insulin antihyperglycemic agents (OR 2.63, 95%CI:2.56-2.71), and an endocrinologist visit in prior year (OR 1.50, 95%CI:1.45-1.55). Those >75 years (OR 0.39, 95%CI:0.38-0.40), with high frailty score (OR 0.38, 95%CI:0.35-0.42), or serum creatinine 1.4-2mg/dL vs. <1.4 (OR 0.55, 95%CI:0.52-0.57) were less likely to use SGLT2i. Conclusions: In a real-world population of T2D and CVD, only 1 in 5 patients used an SGLT2i, although use increased over time. Markers of worse diabetes control were associated with increased SGLT2i use, while markers of more comorbidity were associated with less use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".