Editorial: can we afford the new direct‐acting antivirals for treatment of genotype 1 hepatitis C?
Bibliographic record
Abstract
Saab et al.1 report their cost-effectiveness analysis of treatment of hepatitis C virus (HCV) genotype 1 infections with sofosbuvir and peginterferon plus ribavirin (IFN/RBV) as compared with all currently commercially available treatments including simeprevir and IFN/RBV. Through Markov modelling, outcomes in treatment-naïve, treatment-experienced and HCV/HIV co-infected populations were evaluated based on a US third-party payer perspective. Sofosbuvir-based arms were the most efficacious overall, and economically dominated (i.e. were less expensive and more effective) telaprevir- and boceprevir-based strategies in all subgroups. Sofosbuvir dominated simeprevir except in cirrhosis where simeprevir had a very favourable incremental cost-effectiveness ratio ($1899 per QALY gained). This paper provides the first comparison of sofosbuvir against all current treatments, although has significant limitations. The work is funded by Gilead, whereas independent health technology assessment organisations such as the California Technology Assessment Forum (CTAF)2 and the Canadian Agency for Drugs and Technologies in Health (CADTH)3 have struggled mightily to appropriately assess the sofosbuvir data set because of significant gaps, notably lack of a IFN/RBV comparator arm in the NEUTRINO study,4 and no actual data for treatment-experienced patients (values used in the study are based on modelled calculations). The analysis pools genotype 1a and 1b; distinguishing between 1a and 1b likely has significant implications on the cost effectiveness of sofosbuvir5 and simeprevir. Cost per sustained virological response (SVR) in the 1b group might be expected to favour simeprevir as it is cheaper and using published data, is associated with slightly higher SVR rates in this population (85% vs. 82%).4, 6, 7 The model does not consider retreatment and potentially risk-stratification by genotype subtype and IL28B genotype5 for initial treatment. Using more expensive strategies for treatment failures8 or interferon-intolerant individuals may be more cost effective. In noncirrhotics, it is unclear whether one should treat now or wait for the next generation of medications.9, 10 Although cure for HCV is feasible, its public health impact will continue to be felt due to treatment costs; future cost savings based on current pricing is controversial.2 Indeed, some organisations such as CTAF and CADTH recommend a strategy of only using the expensive second generation of directly acting antivirals for those with advanced liver fibrosis.2, 3 Continued economic study on optimising treatment choices for each subgroup of patients is paramount. Declaration of personal interests: SEC: None. SSL: consulting and research funding: Achilion, Abbvie, Boehringer Ingelheim, Bristol Myers Squibb, Gilead, Idenix, Janssen, Merck, Roche, Vertex. Speakers' bureau: BMS, Gilead, Merck, Roche, Vertex. Declaration of funding interests: None.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.021 | 0.014 |
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".