Association between sodium–glucose cotransporter-2 inhibitors and arrhythmic outcomes in patients with diabetes and pre-existing atrial fibrillation
Bibliographic record
Abstract
AIMS: Prior studies suggest that sodium-glucose cotransporter-2 inhibitors (SGLT2is) may decrease the incidence of atrial fibrillation (AF). However, it is unknown whether SGLT2i can attenuate the disease course of AF among patients with pre-existing AF and Type II diabetes mellitus (DM). In this study, our objective was to examine the association between SGLT2i prescription and arrhythmic outcomes among patients with DM and pre-existing AF. METHODS AND RESULTS: We conducted a population-based cohort study of adults with DM and AF between 2014 and 2019. Using a prevalent new-user design, individuals prescribed SGLT2i were matched 1:1 to those prescribed dipeptidyl peptidase-4 inhibitors (DPP4is) based on time-conditional propensity scores. The primary endpoint was a composite of AF-related healthcare utilization (i.e. hospitalization, emergency department visits, electrical cardioversion, or catheter ablation). Secondary outcome measures included all-cause mortality, heart failure (HF) hospitalization, and ischaemic stroke or transient ischaemic attack (TIA). Cox proportional hazard models were used to examine the association of SGLT2i with the study endpoint. Among 2242 patients with DM and AF followed for an average of 3.0 years, the primary endpoint occurred in 8.7% (n = 97) of patients in the SGLT2i group vs. 10.0% (n = 112) of patients in the DPP4i group [adjusted hazard ratio 0.73 (95% confidence interval 0.55-0.96; P = 0.03)]. Sodium-glucose cotransporter-2 inhibitors were associated with significant reductions in all-cause mortality and HF hospitalization, but there was no difference in the risk of ischaemic stroke/TIA. CONCLUSION: Among patients with DM and pre-existing AF, SGLT2is are associated with decreased AF-related health resource utilization and improved arrhythmic outcomes compared with DPP4is.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".