Vericiguat and health status outcomes in heart failure with reduced ejection fraction: insights from the VICTORIA trial
Bibliographic record
Abstract
Abstract Background In the VICTORIA trial, vericiguat compared with placebo reduced the risk of the primary endpoint of cardiovascular death (CVD) or hospitalization for heart failure (HFH) among 5050 patients with worsening HF with reduced ejection fraction (HFrEF). Purpose We evaluated whether the efficacy of vericiguat on clinical outcomes varied according to participants' baseline health status, as assessed by the Kansas City Cardiomyopathy Questionnaire (KCCQ)-23, and how vericiguat affected health status post-randomization. Methods KCCQ-23 was completed at randomization and at 4, 16, and 32 weeks. Patients were grouped based on tertiles of baseline KCCQ total symptom score (TSS; <55.2, 55.2–79.2, and >79.2), clinical summary score (CSS; <52.1, 52.1–76.0, and >76.2) and overall summary score (OSS; <48.5, 48.5–70.8, and >70.8) across tertiles 1–3, respectively. Cox proportional hazard models were performed for the tertiles to evaluate the effects of vericiguat on the primary outcomes. Results Overall 4741, 4664, and 4470 participants had KCCQ-TSS (median 68.8 [interquartile range 47.9, 85.4]), KCCQ-CSS (65.6 [45.8, 81.8]) and KCCQ-OSS (59.9 [42.0, 77.1]) available at baseline. Vericiguat reduced CVD or HFH risk across baseline KCCQ-TSS (P=0.21), KCCQ-CSS (P=0.13) and KCCQ-OSS (P=0.65) tertiles (Table). The effect of vericiguat on HFH alone was also not modified by baseline KCCQ-TSS, CSS and OSS (all P>0.05) scores. At 4 weeks after randomization, improvement in both vericiguat and placebo arms was seen in KCCQ-TSS (vericiguat 6.3 vs. placebo 6.3; P=0.85), KCCQ-CSS (vericiguat: 5.7 vs. placebo 5.7, P=0.54), and KCCQ-OSS (vericiguat 6.3 vs. placebo 5.7, P=0.36). Similar results were seen at weeks 16 and 32. Conclusion Vericiguat reduced the risk of the composite outcome of CVD or HFH as well as HFH alone across the range of baseline health status. Addition of vericiguat to best standard of care did not significantly improve health status compared with standard of care alone in HF patients with a recent worsening event. The early improvement in KCCQ seen in both randomized groups underscore the need to assess trajectory of health status changes in the spectrum of patients with HFrEF. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): Merck & Co., Inc. and Bayer
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".