1230-P: Low Real-World Use of SGLT2 Inhibitors in People with Diabetes and Heart Failure—A Population Cohort Study
Bibliographic record
Abstract
Background: One of the most striking benefits of sodium-glucose cotransporter 2 inhibitors (SGTL2is) in clinical trials has been the reduction in heart failure (HF) hospitalization. Thus, guidelines recommend SGLT2i use in people with diabetes and HF. Objective: We assessed the real-world dispensation of SGTL2is and the factors associated with their dispensation in people with diabetes and HF. Methods: This retrospective, population-based cohort study identified people with diabetes and HF between Jan 1, 2014 to Dec 31, 2017 in Alberta, Canada (population ~ 4.3 million). Individuals were followed until Dec 31, 2020. The index date was the date of physician or hospitalization claim for HF. The primary exposure was SGTL2i dispensation after HF index date. Demographic and clinical characteristics were summarized, and multivariate logistic regression assessed the factors associated with SGTL2i dispensation adjusting for age, sex, baseline HbA1c, diabetes duration, chronic kidney disease (CKD), atherosclerotic cardiovascular disease (ASCVD), comorbidities (Charlson Comorbidity Index (CCI) score), material deprivation, and urban/rural residence. Results: We identified 22,025 people (43.4% female, mean age 74.7 years ± 11.8 years) with diabetes and HF. Only 10.2% (n=2,247) of individuals with diabetes and HF were dispensed an SGTL2i. Male sex (aOR 1.55; 95% CI 1.39-1.72), age < 65 years (aOR 2.28; 95% CI 2.05-2.54), higher HbA1c (aOR 1.35; 95% 1.31-1.40), no CKD (aOR 3.11; 95% CI 2.78-3.49), ASCVD (aOR 1.48; 95% CI 1.33-1.64), and urban residence (aOR 1.17; 95% CI 1.05-1.31) were associated with SGTL2i dispensation while more co-morbidities were associated with less dispensation (CCI score ≥ 5 aOR 0.36; 95% CI 0.30-0.43). Conclusions: Only 1 in 10 people with diabetes and HF were dispensed an SGTL2i. We identified several factors associated with SGTL2i dispensation and importantly this study highlights a significant opportunity to improve management in people with diabetes and HF. Disclosure S.Butalia: None. C.Wen: None. P.A.Senior: Advisory Panel; Novo Nordisk Canada Inc., Consultant; Novo Nordisk Canada Inc., Bayer Inc., Viatris Inc., Vertex Pharmaceuticals Incorporated, ViaCyte, Inc., Insulet Corporation. R.J.Sigal: Research Support; Novo Nordisk. H.Quan: None. M.Chu: None. P.Kaul: None. Funding Diabetes Canada (OG-3-22-5662-SB)
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".