Cost-effectiveness of Rifaximin treatment in patients with advanced fibrosis who maintain excessive alcohol consumption.
Bibliographic record
Abstract
Background & aimsThe ability of Rifaximin to modulate gut microbiota makes it the first potential treatment for early stages of alcohol-related liver disease (ALD)-i.e., advanced fibrosis and compensated cirrhosis.The present study aims to investigate what effect Rifaximin treatment has to have in order to be cost-effective. MethodsA partitioned survival model was developed to simulate a sample of ongoing excessive drinkers with advanced fibrosis (F3-4) over their lifetime.The costs (in 2021 US$) and benefits in terms of quality-adjusted life years (QALYs) gained were estimated from the healthcare perspective.The corresponding healthcare costs were taken from the Danish DRG.The OS and PFS curves were based on the data from the GALA-ALD natural history data, and utilities were estimated from the previously published studies.We estimated what effect Rifaximin treatment has to have in order to be a cost-effective treatment option.Furthermore, we conducted deterministic and scenario analysis, as well as one-way and probabilistic sensitivity analysis to investigate the impact of parameter uncertainty, and value of information analysis was performed to estimate the value of further clinical research aiming to reduce decision uncertainty. ResultsCompared to the standard care, Rifaximin treatment was found to be cost-effective at a willingness-to-pay threshold (WTP) of $48,000 (equivalent of 40,000) per QALY if the intervention increases the disease-free survival (DFS) by at least 3.7%.3.7% is the pivot point for cost-effectiveness, therefore, the higher the Rifaximin effect is, the higher are the chances that it will be cost-effective.The results of our sensitivity and scenario analysis confirmed robustness of the main findings. ConclusionsIt is important to prevent progression of early ALD disease which, upon reaching the stage of decompensated cirrhosis, is considered to be irreversible and is associated with very high costs and mortality rates. LAY SUMMARYRifaximin can improve the outcomes of ongoing excessive drinkers with advanced fibrosis (F3-4) by delaying or preventing the transition of these patients to decompensation and, subsequently, death state.A wider use of Rifaximin treatment should be determined based on the outcomes from Rifaximin clinical trial and evaluation of whether or not the intervention increases DFS by at least 3.7%.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".