Abstract 15007: Time to SGLT2i Initiation in Patients With Heart Failure
Bibliographic record
Abstract
Introduction: SGLT2i, initially introduced as antidiabetic agents, have shown cardiovascular benefits for heart failure (HF) patients, irrespective of their diabetes status. Given the growing evidence from post-hoc analyses of landmark trials advocating for early initiation, this study aims to investigate the temporal trends in the timing of SGLT2i initiation after HF hospitalization. Methods: A cross-sectional study was conducted using administrative health data from Ontario, Canada. Our cohort included patients aged ≥65 years who filled an SGLT2i prescription after their HF hospitalization in Ontario, Canada between 4/2016-3/2021. We computed the median number of days to the first SGLT2i prescription after the HF discharge date, exclusively for patients who started SGLT2i. Patients were further stratified based on their diabetes status. Results: We identified 64,183 HF patients, of which 5,257 filled an SGLT2i prescription after discharge (mean age was 82.49; 46.9% men; 49.6% had diabetes). The number of patients starting an SGLT2i increased from 889 (16.9%) in 2016/17 to 1,206 (22.9%) by 2020/21. The median time from discharge following the index HF admission to first dispensation of an SGLT2i prescription decreased 7-fold over this period, from 1,082 days to 156 days (Figure 1). In 2016/17, patients with diabetes started an SGLT2i at a median of 930 days, and patients without diabetes at 1,640 days. However, by 2020/21, the median initiation times were closer at 143 and 189 days, respectively. Conclusions: Our findings demonstrate a substantial reduction in the time to SGLT2i initiation for patients after HF admission, suggesting increased clinician recognition of their cardiovascular benefits. However, initiation remains delayed, especially for patients without diabetes. To optimize patient outcomes, it is crucial to encourage early initiation of SGLT2i therapy in hospitalized HF patients and address potential barriers to SGLT2i initiation.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".