Comparative effectiveness of SGLT2i versus GLP1-RA on cardiovascular outcomes in routine clinical practice
Bibliographic record
Abstract
BACKGROUND: To investigate the comparative effectiveness of sodium-glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide 1 receptor agonists (GLP1-RA) on cardiovascular outcomes in routine clinical practice, which have never been directly compared in head-to-head outcome trials. METHODS: We compared outcomes of adults who newly started SGLT2i or GLP1-RA therapy in Stockholm, Sweden, during 2013-2019. The primary outcome was major adverse cardiovascular events (MACE), a composite of cardiovascular (CV) death, myocardial infarction and stroke. Secondary outcomes included the individual MACE components and hospitalization for heart failure. Cox regression with propensity score overlap weighting was used to estimate hazard ratios (HRs) with 95% confidence intervals and adjust for 57 covariates. RESULTS: We included 12,375 individuals, of which 5489 initiated SGLT2i and 6886 GLP1-RA therapy, followed for median 1.6 years. Mean age was 61 years and 37.6% were women. Compared with GLP1-RA, SGLT2i new users had similar risk of MACE risk (adjusted HR 1.04; 95% CI 0.83-1.31). The adjusted HRs (95% CI) for SGLT2i vs. GLP1-RA were 0.80 (0.59-1.09) for heart failure hospitalization, 0.95 (0.58-1.55) for cardiovascular death, 0.91 (0.67-1.24) for myocardial infarction and 1.71 (1.14-2.59) for ischemic stroke (5-year absolute risk difference for stroke 1.9% [95% CI 0.8-3.0]). CONCLUSIONS: In a largely primary-prevention population of people undergoing routine care, no differences were observed in MACE risk among initiators of SGLT2i and GLP1-RA. However, compared with GLP1RA, the use of SGLT2i was associated with an increased risk of ischemic stroke that was small in absolute magnitude.
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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 | 0.029 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".