Vericiguat and Health-Related Quality of Life in Patients With Heart Failure With Reduced Ejection Fraction: Insights From the VICTORIA Trial
Bibliographic record
Abstract
Background: We examined the effects of vericiguat compared with placebo in patients with heart failure with reduced ejection fraction enrolled in VICTORIA (Vericiguat Global Study in Patients With Heart Failure With Reduced Ejection Fraction) on health status outcomes measured by the Kansas City Cardiomyopathy Questionnaire (KCCQ) and evaluated whether clinical outcomes varied by baseline KCCQ score. Methods: KCCQ was completed at baseline and 4, 16, and 32 weeks. We assessed treatment effect on KCCQ using a mixed-effects model adjusting for baseline KCCQ and stratification variables. Cox proportional-hazards modeling was performed to evaluate the effect of vericiguat on clinical outcomes by tertiles of baseline KCCQ clinical summary score (CSS), total symptom score (TSS), and overall summary score (OSS). Results: Of 5050 patients, 4664, 4741, and 4470 had KCCQ CSS (median [25th to 75th], 65.6 [45.8–81.8]), TSS (68.8 [47.9–85.4]), and OSS (59.9 [42.0–77.1]) at baseline; 94%, 88%, and 82% had data at 4, 16, and 32 weeks. At 16 weeks, CSS improved by a median of 6.3 in both arms; no significant differences in improvement were seen for TSS and OSS between the 2 groups ( P =0.69, 0.97, and 0.13 for CSS, TSS, and OSS). Trends were similar at 4 and 32 weeks. Vericiguat versus placebo reduced cardiovascular death or heart failure hospitalization risk similarly across tertiles of baseline KCCQ CSS, TSS, and OSS (interaction P =0.13, 0.21, and 0.65). Conclusions: Vericiguat did not significantly improve KCCQ scores compared with placebo. Vericiguat reduced the risk of cardiovascular death or heart failure hospitalization across the range of baseline health status. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02861534.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 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) | 0.002 | 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".