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Cost-Effectiveness of Vericiguat in Patients With Heart Failure With Reduced Ejection Fraction: The VICTORIA Randomized Clinical Trial

2023· article· en· W4386467308 on OpenAlexaff
Derek S. Chew, Yanhong Li, Robert Bigelow, Patricia A. Cowper, Kevin J. Anstrom, Melanie R. Daniels, Linda Davidson‐Ray, Adrian F. Hernandez, Christopher M. O’Connor, Paul W. Armstrong, Daniel B. Mark

Bibliographic record

VenueCirculation · 2023
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsCanadian VIGOUR CentreLibin Cardiovascular Institute of Alberta
Fundersnot available
KeywordsMedicineHazard ratioEjection fractionHeart failureQuartilePlaceboLife expectancyRandomized controlled trialInternal medicineConfidence intervalPopulationEnvironmental health

Abstract

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BACKGROUND: The VICTORIA trial (Vericiguat Global Study in Subjects With Heart Failure With Reduced Ejection Fraction) demonstrated that, in patients with high-risk heart failure, vericiguat reduced the primary composite outcome of cardiovascular death or heart failure hospitalization relative to placebo. The hazard ratio for all-cause mortality was 0.95 (95% CI, 0.84-1.07). In a prespecified analysis, treatment effects varied substantially as a function of baseline NT-proBNP (N-terminal pro-B-type natriuretic peptide) levels, with survival benefit for vericiguat in the lower NT-proBNP quartiles (hazard ratio, 0.82 [95% CI, 0.69-0.97]) and no benefit in the highest NT-proBNP quartile (hazard ratio, 1.14 [95% CI, 0.95-1.38]). An economic analysis was a major secondary objective of the VICTORIA research program. METHODS: Medical resource use data were collected for all VICTORIA patients (N=5050). Costs were estimated by applying externally derived US cost weights to resource use counts. Life expectancy was projected from patient-level empirical trial survival results with the use of age-based survival modeling methods. Quality-of-life adjustments were based on prospectively collected EQ-5D-based utilities. The primary outcome was the incremental cost-effectiveness ratio, comparing vericiguat with placebo, assessed from the US health care sector perspective over a lifetime horizon. Cost-effectiveness was estimated using the total VICTORIA cohort, both with and without interaction between treatment and baseline NT-proBNP. RESULTS: Life expectancy modeling results varied according to whether the observed heterogeneity of treatment effect by baseline NT-proBNP values was incorporated into the modeling. Including the interaction term, the vericiguat arm had an estimated quality-adjusted life expectancy of 4.56 quality-adjusted life-years (QALYs) compared with 4.13 QALYs for placebo (incremental discounted QALY, 0.43). Without the treatment heterogeneity/interaction term, vericiguat had 4.50 QALYs compared with 4.33 QALYs for placebo (incremental discounted QALY, 0.17). Incremental discounted costs (vericiguat minus placebo) were $28 546 with the treatment interaction and $20 948 without it. Corresponding incremental cost-effectiveness ratios were $66 509 per QALY allowing for treatment heterogeneity and $124 512 without heterogeneity. CONCLUSIONS: Vericiguat use in the VICTORIA trial met criteria for intermediate value, but the incremental cost-effectiveness ratio estimates were sensitive to whether the analysis accounted for observed NT-proBNP treatment effect heterogeneity. The cost-effectiveness of vericiguat was driven by the projected incremental life expectancy among patients in the lowest 3 quartiles of NT-proBNP. 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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.043
GPT teacher head0.344
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2023
Admission routes1
Has abstractyes

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