Direct-Acting Antiviral Treatment Failure Among Hepatitis C and HIV–Coinfected Patients in Clinical Care
Bibliographic record
Abstract
BACKGROUND: There are limited data on the real-world effectiveness of direct-acting antiviral (DAA) treatment in patients coinfected with hepatitis C virus (HCV) and HIV-a population with complex challenges including ongoing substance use, cirrhosis, and other comorbidities. We assessed how patient characteristics and the appropriateness of HCV regimen selection according to guidelines affect treatment outcomes in coinfected patients. METHODS: We included all patients who initiated DAA treatment between November 2013 and July 2017 in the Canadian Co-Infection Cohort. Sustained virologic response (SVR) was defined as an undetectable HCV RNA measured between 10 and 18 weeks post-treatment. We defined treatment failure as virologic failure, relapse, or death without achieving SVR. Bayesian logistic regression was used to estimate the posterior odds ratios (ORs) associated with patient demographic, clinical, and treatment-related risk factors for treatment failure. RESULTS: Two hundred ninety-five patients initiated DAAs; 31% were treatment-experienced, 29% cirrhotic, and 80% HCV genotype 1. Overall, 92% achieved SVR (263 of 286, 9 unknown), with the highest rates in females (97%) and lowest in cirrhotics (88%) and high-frequency injection drug users (89%). Many patients (38%) were prescribed regimens that were outside current clinical guidelines. This did not appreciably increase the risk of treatment failure-particularly in patients with genotype 1 (prior odds ratio [OR], 1.5; 95% credible interval [CrI], 0.38-6.0; posterior OR, 1.0; 95% CrI, 0.40-2.5). CONCLUSIONS: DAAs were more effective than anticipated in a diverse, real-world coinfected cohort, despite the use of off-label, less efficacious regimens. High-frequency injection drug use and cirrhosis were associated with an increased risk of failure.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".