Development of models estimating the risk of hepatocellular carcinoma after antiviral treatment for hepatitis C
Author(s): ,
Kristin Berry
Affiliations:
Health Services Research and Development, Veterans Affairs Puget Sound Healthcare System, Seattle, WA, United States
,
Kathleen F. Kerr
Affiliations:
Department of Biostatistics, University of Washington, Seattle, WA, United States
,
Elijah J. Mun
Affiliations:
Division of General Internal Medicine, Department of Medicine Veterans Affairs Puget Sound Healthcare System and University of Washington, Seattle, WA, United States
,
Lauren A. Beste
Affiliations:
Division of General Internal Medicine, Department of Medicine Veterans Affairs Puget Sound Healthcare System and University of Washington, Seattle, WA, United States
,
Pamela K. Green
Affiliations:
Health Services Research and Development, Veterans Affairs Puget Sound Healthcare System, Seattle, WA, United States
George N. Ioannou
Affiliations:
Health Services Research and Development, Veterans Affairs Puget Sound Healthcare System, Seattle, WA, United States
Corresponding author. Address: Veterans Affairs Puget Sound Health Care System, Gastroenterology, S-111-Gastro, 1660 S. Columbian Way, Seattle, WA 98108, United States. Tel.: +1 206 277 3136; fax: +1 206 764 2232.
EASL LiverTree™. Ioannou G. Nov 1, 2018; 234543
Dr. George Ioannou
Dr. George Ioannou
Contributions Biography
Journal Abstract
References
Graphical abstract

Graphical abstract

We developed and validated models to estimate HCC risk after antiviral treatment for HCV. Using these models may improve HCC screening strategies. Models are available as web-based tools.

Background & Aims

Most patients with hepatitis C virus (HCV) infection will undergo antiviral treatment with direct-acting antivirals (DAAs) and achieve sustained virologic response (SVR). We aimed to develop models estimating hepatocellular carcinoma (HCC) risk after antiviral treatment.

Methods

We identified 45,810 patients who initiated antiviral treatment in the Veterans Affairs (VA) national healthcare system from 1/1/2009 to 12/31/2015, including 29,309 (64%) DAA-only regimens and 16,501 (36%) interferon ± DAA regimens. We retrospectively followed patients until 6/15/2017 to identify incident cases of HCC. We used Cox proportional hazards regression to develop and internally validate models predicting HCC risk using baseline characteristics at the time of antiviral treatment.

Results

We identified 1,412 incident cases of HCC diagnosed at least 180 days after initiation of antiviral treatment during a mean follow-up of 2.5 years (range 1.0–7.5 years). Models predicting HCC risk after antiviral treatment were developed and validated separately for four subgroups of patients: cirrhosis/SVR, cirrhosis/no SVR, no cirrhosis/SVR, no cirrhosis/no SVR. Four predictors (age, platelet count, serum aspartate aminotransferase/√alanine aminotransferase ratio and albumin) accounted for most of the models’ predictive value, with smaller contributions from sex, race-ethnicity, HCV genotype, body mass index, hemoglobin and serum alpha-fetoprotein. Fitted models were well-calibrated with very good measures of discrimination. Decision curves demonstrated higher net benefit of using model-based HCC risk estimates to determine whether to recommend screening or not compared to the screen-all or screen-none strategies.

Conclusions

We developed and internally validated models that estimate HCC risk following antiviral treatment. These models are available as web-based tools that can be used to inform risk-based HCC surveillance strategies in individual patients.

Lay summary

Most patients with hepatitis C virus have been treated or will be treated with direct-acting antivirals. It is important that we can model the risk of hepatocellular carcinoma in these patients, so that we develop the optimum screening strategy that avoids unnecessary screening, while adequately screening those at increased risk. Herein, we have developed and validated models that are available as web-based tools that can be used to guide screening strategies.

Keyword(s)
Liver cancer, Screening, Prediction models, Antivirals
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