Salvage Therapy with Sofosbuvir/Velpatasvir/Voxilaprevir in DAA-experienced Patients: Results from a Prospective Canadian Registry
Bibliographic record
Abstract
BACKGROUND: Despite the current highly effective therapies with direct-acting antiviral agents (DAAs), some patients with chronic hepatitis C virus (HCV) infection still do not achieve sustained virological response (SVR) and require retreatment. Sofosbuvir/velpatasvir/voxilaprevir (SVV) is recommended as the first-line retreatment option for most patients. The aim of this study was to evaluate the efficacy of SVV as salvage therapy after at least one course of DAA. METHODS: Data were collected on all HCV-infected patients who failed DAAs and were prescribed SVV from a prospective Canadian registry (CANUHC) including 17 sites across Canada. Factors associated with failure to achieve SVR with SVV therapy and the utility of RAS testing and ribavirin use were evaluated. RESULTS: A total of 128 patients received SVV after non-SVR with DAA treatment: 80% male, median age 57.5 (31-86), 44% cirrhotic, and 17 patients post liver transplant. First line regimens included: sofosbuvir/velpatasvir (27.3%), sofosbuvir/ledipasvir (26.5%), grazoprevir/elbasvir (12.5%), other (33.5%). Ribavirin was added to SVV in 26 patients due to past sofosbuvir/velpatasvir use (n = 8), complex resistance associated substitution profiles (n = 16) and/or cirrhosis (n = 9). Overall SVR rate was 96% (123/128). Of 35 patients who previously failed sofosbuvir/velpatasvir, 31 (88.5%) achieved SVR compared to 92 of 93 (99%) among those receiving any other regimen (P = .01). CONCLUSIONS: Similar to reports from phase 3 clinical trials, SVV proved highly effective as salvage therapy for patients who failed a previous DAA therapy. Those who failed SVV had at least 2 of the following factors: genotype 3, presence of cirrhosis, past liver transplantation, past exposure to sofosbuvir/velpatasvir and/or complex resistance profiles.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".