Antiviral Treatment of Recurrent Hepatitis C After Liver Transplantation: Predictors of Response and Long-Term Outcome
Bibliographic record
Abstract
BACKGROUND: Efficacy and long-term outcome of antiviral therapy for recurrent hepatitis C after liver transplantation is poorly defined. AIM: This study aimed at assessing the efficacy of antiviral therapy regarding sustained hepatitis C virus (HCV) clearance, liver histology, and patient survival. METHODS: We retrospectively reviewed all 446 patients who received a liver allograft at our institution for HCV-related cirrhosis between January 1992 and December 2006. Two hundred thirty-two patients (52%) were eligible for antiviral therapy based on predefined criteria (Metavir stage > or =1 and/or grade > or =2; protocol biopsies). One hundred seventy-two patients (39%) had no contraindication for treatment, received more than or equal to 1 dose of interferon-alpha-based combination therapy, and form the basis of this analysis. Therapy was aimed for 48 weeks; median posttreatment follow-up was 68 months. RESULTS: The overall sustained virological response (SVR) rate was 50% (genotype 1/4: 40%; genotype 2/3: 76%). SVR was higher on cyclosporine A (CsA) (56%) than on tacrolimus (44%, P=0.05), largely because of a lower relapse rate (6% vs. 19%, P=0.01). In multivariate analysis, genotype 2/3, CsA use, donor age, and pretreatment necroinflammatory activity were independently associated with SVR. SVR significantly improved histology and long-term survival (actuarial 5-year survival 96% vs. 69% in nonresponders, P<0.0001). CONCLUSION: Antiviral therapy of recurrent hepatitis C after liver transplantation is able to clear HCV in half the patients, more likely on CsA than on tacrolimus, and markedly improves outcome.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".