Heart Failure Outcomes Captured by Adverse Event Reporting in Participants with Type 2 Diabetes and Atherosclerotic Cardiovascular Disease: Observations from the VERTIS CV Trial
Bibliographic record
Abstract
AIMS: In VERTIS CV, ertugliflozin was associated with a 30% risk reduction for adjudication-confirmed, first and total hospitalizations for heart failure (HHF) in participants with type 2 diabetes and atherosclerotic cardiovascular disease. We evaluated the impact of ertugliflozin on the broader spectrum of all reported heart failure (HF) events independent of adjudication confirmation. METHODS AND RESULTS: Data from participants who received ertugliflozin (5 or 15 mg) were pooled and compared versus placebo. HF events included all investigator-reported HF adverse events (AEs) and serious AEs (SAEs) based on the narrow standardized Medical Dictionary for Regulatory Activities (MedDRA) query 'cardiac failure'. Terms for orthopnoea, dyspnoea, and peripheral oedema were evaluated separately. The effect of ertugliflozin on the first HF event was assessed by Cox proportional hazard models. Total HF events were assessed by Andersen-Gill models to account for first and recurrent events. A total of 8238 participants received ≥1 dose of ertugliflozin or placebo (mean follow-up 3.5 years). Investigator-reported HF events and AE capture yielded 420 first and 627 total HF events (vs. 238 and 345 adjudication-confirmed HHF events, respectively, in the primary analyses). Ertugliflozin reduced the risk for first (hazard ratio [HR] 0.69; 95% confidence interval [CI] 0.57-0.84; p < 0.001) and total HF AEs (HR 0.66; 95% CI 0.57-0.78; p < 0.001), with similar results for first and total HF SAEs. Additionally, ertugliflozin reduced oedema risk, but not orthopnoea/dyspnoea. CONCLUSION: The effect of ertugliflozin was consistent across the spectrum of total investigator-reported HF AEs and was similar in magnitude to adjudicated HHF events.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".