Abstract 15684: Adherence and Persistence to SGLT2 Inhibitors in Patients With Heart Failure
Bibliographic record
Abstract
Introduction: While originally used solely as antihyperglycemic agents, sodium-glucose cotransporter 2 inhibitors (SGLT2i) are now recommended for use in heart failure (HF) to reduce hospitalization and cardiovascular mortality. Unfortunately, benefits of treatment can only be fully realized if a patient is adherent to therapy. While SGLT2i adherence has been found to be suboptimal in diabetes populations, SGLT2i adherence has not been well-studied in patients with HF. Since medication non-adherence in HF tends to be high in general, and can lead to worse clinical outcomes, this study aimed to estimate SGLT2i adherence/persistence rates in a population-based cohort of HF patients. Methods: This retrospective cohort study included patients ≥65 years discharged alive from a hospital in Ontario, Canada between 4/2016 and 3/2021, who had a primary diagnosis of HF and were dispensed a SGLT2i prescription. Prescription claims were used to identify and calculate 6-month and 1-year SGLT2i adherence and 1-year persistence, from the first date of fill for an SGLT2i. Adherence was calculated as the proportion of days covered (PDC), with PDC≥80% defined as “good” adherence. Persistence was calculated with a “permissible” gap of 30 days allowed before being considered non-persistent. A cumulative incidence function was utilized to account for the competing risk of death in persistence calculations. Results: There were 5,257 eligible patients (59% male, mean age 76.94±7 years, 73.8% with diabetes), for whom empagliflozin (64.9%), dapagliflozin (25.5%) and canagliflozin (9.5%) were used. The overall mean PDC was 83.9% at 6-months and 80.5% at 1-year, with the proportion of patients with “good” adherence (PDC≥80%) at 76.2% and 72.5%, respectively. Persistence to all prescribed SGLT2i was 71.3% at 1-year, with highest persistence with dapagliflozin at 74.4% and lowest persistence with canagliflozin at 68.1% [p=0.01]. Conclusion: In this HF cohort on SGLT2i, nearly 3 out of 4 patients were highly adherent and persistent at one year. SGLT2i adherence/persistence rates are higher than seen in studies in diabetes populations. Further studies are needed to identify those at risk of non-adherence/non-persistence and to examine association with clinical outcomes.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".