Prescription Patterns of Sodium-Glucose Cotransporter-2 Inhibitors in Adults With Type 2 Diabetes and Heart Failure With Reduced Ejection Fracture Admitted to a Tertiary Care Centre in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: Our aim in this study was to assess early adoption patterns of sodium-glucose cotransporter-2 inhibitors (SGLT2i) in eligible patients with type 2 diabetes (T2DM) and heart failure with reduced ejection fracture (HFrEF), and to identify gaps in practice. METHODS: A retrospective chart review was conducted of patients with T2DM and HFrEF admitted with decompensated heart failure to The Ottawa Hospital under cardiology or general internal medicine from June 2019 to May 2021. Patterns were assessed at 8-month intervals (1 period before release of the Diabetes Canada 2020 guidelines and 2 periods afterward). Baseline patient characteristics, comorbidities, and prescriber information were collected. RESULTS: Of the 98 patients who met the inclusion criteria, 36.7% had a prescription for an SGLT2i, either on admission, discharge, or follow-up. Trends showed a gradual increase over time. On admission, 9.8% of patients were on an SGLT2i in period 1, 19.2% in period 2, and 23.3% in period 3. Patients receiving a prescription for SGLT2i on discharge were 0.0% in period 1, 10.0% in period 2, and 9.5% in period 3, all of whom were admitted under cardiology. On follow-up, 13.9% of eligible patients were started on an SGLT2i in period 1, 21.1% in period 2, and 35.0% in period 3. Endocrinology was the main prescriber of SGLT2i in the outpatient setting, followed by cardiology. CONCLUSIONS: Overall, trends show a slow but steady increase in early prescriptions of SGLT2i. However, most eligible patients were not started on therapy during our study period, with variability in practice between specialties, highlighting opportunities to boost uptake in the future.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".