Sodium‐Glucose Co‐Transporter Inhibitors and Atrial Fibrillation: A Systematic Review and Meta‐Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background Sodium‐glucose co‐transporter (SGLT) inhibitors reduce cardiovascular outcomes including mortality in several populations; however, their effect on atrial fibrillation/flutter (AF) remains unclear. Our objective was to determine whether SGLT inhibitors reduce AF and whether a history of AF modifies the effect of SGLT inhibitors on the composite of heart failure hospitalization or cardiovascular death. Methods and Results We searched MEDLINE, Embase, and CENTRAL to March 2021. Pairs of reviewers identified randomized controlled trials that compared an SGLT inhibitor with placebo or no therapy. We pooled data using RevMan 5.4.1, assessed risk of bias using the Cochrane tool, and determined the overall quality of evidence using Grades of Recommendation, Assessment, Development and Evaluation. Thirty‐one eligible trials reported on AF events (75 279 participants, mean age 62 years, 35.0% women). Moderate quality evidence supported a lower risk of serious AF events with SGLT inhibitors (1.1% versus 1.5%; risk ratio 0.75 [95% CI, 0.66–0.86]; I 2 =0%). A similar reduction in total AF events was also noted with SGLT inhibitors. Three trials reported on heart failure hospitalization/cardiovascular death stratified by a baseline history of AF (18 832 participants, mean age 66 years, 38.1% women); in patients with a history of AF, SGLT inhibitors resulted in a lower risk in the composite of heart failure hospitalization or cardiovascular death (hazard ratio, 0.70 [95% CI, 0.57–0.85]; I 2 =0%)—similar to the effect estimate for patients without AF, P value for interaction: 1.00. Conclusions SGLT inhibitors may reduce AF events and likely reduce heart failure hospitalization/cardiovascular death to a similar extent in patients with and without AF.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.033 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".