HCV Eradication with Direct-Acting Antivirals Does Not Impact HCC Progression on the Waiting List or HCC Recurrence after Liver Transplantation
Bibliographic record
Abstract
Background: The introduction of direct-acting antivirals (DAA) for HCV has led to high rates of HCV eradication. Treatment of patients awaiting liver transplantation (LT) has been controversial. Recent data suggests that DAA treatment may accelerate recurrent HCC. The impact of DAA on delisting for HCC progression or recurrent HCC post-LT has not been well characterized. Methods: A retrospective review of both waitlist patients and LT recipients at a single institution was performed. Patient demographics, HCV treatment, HCC features and treatments, biopsy results, and graft and patient survival were evaluated. Patients on the LT waitlist or who were transplanted between January 2014 and December 2015 were included. Data was collected through December 2017 to have a minimum of two years of follow-up. Results: In the study period, 128 adult LT were performed. 44 patients were HCV+, and 68.2% (N=30) also had HCC. 38.6% (N=17) of HCV+ patients received DAA pre-LT, and 94.1% (N=16/17) achieved sustained virologic response (SVR) pre-LT. Among untreated HCV+ patients who underwent LT, 81.5% (N=22/27) received DAA post-LT, with 82.6% achieving SVR post-LT (N=18/22). 82.1% (N=23/28) of untreated post-LT patients underwent liver biopsy prior to therapy, and 52.2% had at least F1 METAVIR fibrosis. 87.5% (N=14/16) of active waitlist patients received DAA and achieved SVR. HCV eradication did not result in higher rates of delisting for HCC progression. Due to local HCC listing criteria of total tumor volume and AFP, 60% (N=18/30) of HCV+/HCC patients were beyond Milan criteria at the time of LT. Despite this, there was no difference in HCC recurrence rates post-LT, whether patients achieved SVR pre- or post-LT. Conclusions: These data suggest that HCV eradication pre-LT does not significantly impact waitlist time for HCV+ patients with HCC. HCV eradication does not impact rates of delisting for HCC progression or rates of HCC recurrence post-LT.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".