Real-world Efficacy of Direct-Acting Antiviral Therapy for HCV Infection Affecting People Who Inject Drugs Delivered in a Multidisciplinary Setting
Bibliographic record
Abstract
Abstract Background Many clinicians and insurance providers are reluctant to embrace recent guidelines identifying people who inject drugs (PWID) as a priority population to receive hepatitis C virus (HCV) treatment. The aim of this study was to evaluate the efficacy of direct-acting antiviral (DAA) HCV therapy in a real-world population comprised predominantly of PWID. Methods A retrospective analysis was performed on all HCV-infected patients who were treated at the Vancouver Infectious Diseases Centre between March 2014 and December 2017. All subjects were enrolled in a multidisciplinary model of care, addressing medical, psychological, social, and addiction-related needs. The primary outcome was achievement of sustained virologic response (undetectable HCV RNA) 12 or more weeks after completion of HCV therapy (SVR-12). Results Overall, 291 individuals were enrolled and received interferon-free DAA HCV therapy. The mean age was 54 years, 88% were PWID, and 20% were HCV treatment experienced. At data lock, 62 individuals were still on treatment and 229 were eligible for evaluation of SVR by intent-to-treat (ITT) analysis. Overall, 207 individuals achieved SVR (90%), with 13 losses to follow-up, 7 relapses, and 2 premature treatment discontinuations. ITT SVR analysis show that active PWID and treatment-naïve patients were less likely to achieve SVR (P = .0185 and .0317, respectively). Modified ITT analysis of active PWID showed no difference in achieving SVR (P = .1157) compared with non-PWID. Conclusion Within a multidisciplinary model of care, the treatment of HCV-infected PWID with all-oral DAA regimens is safe and highly effective. These data justify targeted efforts to enhance access to HCV treatment in this vulnerable and marginalized population.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".