Effects of canagliflozin on myocardial infarction: a <i>post hoc</i> analysis of the CANVAS programme and CREDENCE trial
Bibliographic record
Abstract
AIMS: Given the benefits of sodium glucose co-transporter 2 inhibition (SGLT2i) in protecting against heart failure in diabetic patients, we sought to explore the potential impact of SGLT2i on the clinical features of patients presenting with myocardial infarction (MI) through a post hoc analysis of CANVAS Programme and CREDENCE trial. METHODS AND RESULTS: Individuals with type 2 diabetes and history or high risk of cardiovascular disease (CANVAS Programme) or type 2 diabetes and chronic kidney disease (CREDENCE) were included. The intervention was canagliflozin 100 or 300 mg (combined in the analysis) or placebo. MI events were adjudicated as ST-elevation myocardial infarction (STEMI), non-STEMI, and type 1 MI or type 2 MI. A total of 421 first MI events in the CANVAS Programme and 178 first MI events in the CREDENCE trial were recorded (83 fatal, 128 STEMI, 431 non-STEMI, and 40 unknown). No benefit of canagliflozin compared with placebo on time to first MI event was observed [hazard ratio (HR) 0.89; 95% confidence interval (CI) 0.75, 1.05]. Canagliflozin was associated with lower risk for non-STEMI (HR 0.78; 95% CI 0.65, 0.95) but suggested a possible increase in STEMI (HR 1.55; 95% CI 1.06, 2.27), with no difference in risk of type 1 or type 2 MI. There was no change in fatal MI (HR 1.22, 95% CI 0.78, 1.93). CONCLUSION: Canagliflozin was not associated with a reduction in overall MI in the pooled CANVAS Programme and CREDENCE trial population. The possible differential effect on STEMI and Non-STEMI observed in the CANVAS cohort warrants further investigation. TRIAL REGISTRATION: ClinicalTrials.gov identifiers: NCT01032629, NCT01989754, and NCT02065791.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".