Effect of vericiguat on cardiovascular outcomes in patients with heart failure with and without diabetes: Insights from the VICTORIA trial
Bibliographic record
Abstract
Abstract Objective To investigate the impact of type 2 diabetes mellitus (T2D) on outcomes in patients with worsening heart failure with reduced ejection fraction (HFrEF) and to assess the efficacy of vericiguat in these patients. Methods Patients with HF and a left ventricular ejection fraction ≤45% were randomized to receive vericiguat or placebo in addition to standard therapy. The primary outcome was a composite of cardiovascular death or first hospitalization for HF (HHF). A Cox proportional hazards model was used to calculate hazard ratios (HR) and 95% confidence intervals (CI) to assess if the effect of vericiguat differed by history of T2D. Results Of the 5048 patients, 2369 (46.9%) had T2D. The risks of the primary outcome, HHF, and all-cause and cardiovascular death were already very high in those without T2D and even higher in patients with T2D. The beneficial effect of vericiguat on the primary outcome did not differ in patients with (HR, 0.91 [95% CI, 0.80–1.03]) or without (HR, 0.87 [95% CI, 0.76–0.99] (P-interaction = 0.64) T2D (Figure). No significant differences were noted among patients with and without T2D with respect to the effect of vericiguat on HHF and all-cause or cardiovascular death. Conclusion Notwithstanding the increased incidence of cardiovascular death and HHF conferred by T2D, in this post-hoc analysis of VICTORIA, vericiguat compared with placebo significantly reduced the risk of cardiovascular death or HHF in patients with worsening HFrEF regardless of T2D status.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".