Eligibility for Vericiguat in a Real-World Heart Failure Population According to Trial, Guideline and Label Criteria: Data from the Swedish Heart Failure Registry
Bibliographic record
Abstract
AIM: We investigated the eligibility for vericiguat in a real-world heart failure (HF) population based on trial, guideline and label criteria. METHODS AND RESULTS: From the Swedish HF registry, 23 573 patients with HF with reduced ejection fraction (HFrEF) enrolled between 2000 and 2018, with a HF duration ≥6 months, were considered. Eligibility for vericiguat was calculated based on criteria from (i) the Vericiguat Global Study in Subjects with Heart Failure and Reduced Ejection Fraction (VICTORIA) trial; (ii) European and American guidelines on HF; (iii) product labelling according to the Food and Drug Administration and European Medicines Agency. Estimated eligibility for vericiguat in the trial, guidelines, and label scenarios was 21.4%, 47.4%, and 47.4%, respectively. Prior HF hospitalization within 6 months was the criterion limiting eligibility the most in all scenarios (met by 49.1% of the population). In the trial scenario, other criteria meaningfully limiting eligibility were elevated N-terminal pro-B-type natriuretic peptide levels and nitrate use. In all scenarios, eligibility was higher among patients hospitalized for HF at baseline (44.3% vs. 21.4% [trial scenario] and 97.3% vs. 47.4% [guideline/label scenarios] for hospitalized vs. non-hospitalized patients). Overall, eligible patients were older, had more severe HF, more comorbidities, and consequently higher cardiovascular mortality and HF hospitalization rates compared with ineligible patients across all scenarios. CONCLUSION: In a large and contemporary real-world HFrEF cohort, we estimated that 21.4% of patients would be eligible for vericiguat according to the VICTORIA trial selection criteria, 47.4% based on guidelines and labelling. Eligibility for vericiguat translated into the selection of a population at high risk of morbidity/mortality.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".