Abstract 10249: Association of Eligibility for a Sodium-Glucose Co-Transporter 2 Inhibitor and Cardiovascular Outcomes in Patients with Atrial Fibrillation
Bibliographic record
Abstract
Introduction: Heart failure (HF) is the leading cause of death in patients with atrial fibrillation (AF). Although sodium-glucose co-transporter 2 inhibitors (SGLT2i) reduce HF in a broad range of populations, they have not been studied specifically in patients with AF. Objective: We aimed to determine the proportion of AF patients who are eligible for an SGLT2i based on co-morbidities, and compare their risk of cardiovascular outcomes to ineligible patients. Methods: We pooled data from two randomized controlled trials (RCTs) of AF patients (RE-LY and ACTIVE-W). Among patients assigned to the anticoagulation arms, those meeting the inclusion criteria from at least one of the large Phase 3 SGLT2i RCTs were included in the “SGLT2i-eligible” and the others in the “SGLT2i-ineligible” group. Eligibility indications were diabetes mellitus + cardiovascular disease (DM+CVD), HF with reduced ejection fraction (HFrEF), and renal disease. We examined a primary outcome of cardiovascular death and hospitalization for HF, and compared study outcomes between SGLT2i-eligible and ineligible groups. Results: A total of 21485 AF patients (mean age: 71.2±8.8, 36.1% women, Median CHA 2 DS 2 -VASc Score = 3, IQR 2-4) met inclusion. The proportion of AF patients eligible for treatment with an SGLT2i was 23.6%, with 8.9%, 22.6%, 9.1% being eligible for Empagliflozin, Dapagliflozin, and Canagliflozin, respectively. Renal disease (17.6%) was the most common eligibility indication, followed by DM+CVD (9.0%) and HFrEF (5.3%). After a median follow-up of 1.9 years, the incidence of cardiovascular death/hospitalization for HF (10.9% vs. 6.3%, p<0.001), cardiovascular death (5.2% vs. 2.4%, p<0.001), hospitalization for HF (6.8% vs. 4.2%, p<0.001), hospitalization for AF (6.7% vs. 5.4%, p<0.001), and thromboembolic events (4.9% vs. 3.9%, p=0.003) was significantly higher in the SGLT2i-eligible than the ineligible group. Conclusion: The majority of AF patients are not eligible for an SGLT2i. Although these patients are lower-risk than eligible patients, they still have high rates of cardiovascular events. RCTs are needed to test the efficacy of SGLT2is in AF patients.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".