Division of Gastroenterology, Department of Medicine Veterans Affairs Puget Sound Healthcare System and University of Washington, Seattle, WA, United States; Health Services Research and Development, Veterans Affairs Puget Sound Healthcare System, Seattle, WA, United States.
Health Services Research and Development, Veterans Affairs Puget Sound Healthcare System, Seattle, WA, United States.
J Hepatol. 2018 Nov;69(5):1088-1098. doi: 10.1016/j.jhep.2018.07.024. Epub 2018 Aug 21.
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.
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.
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.
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.
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.
大多数丙型肝炎病毒（HCV）感染患者将接受直接作用抗病毒药物（DAA）的抗病毒治疗，并实现持续病毒学应答（SVR）。我们旨在开发用于估计抗病毒治疗后肝细胞癌（HCC）风险的模型。
我们从 2009 年 1 月 1 日至 2015 年 12 月 31 日在退伍军人事务部（VA）国家医疗保健系统中确定了 45810 名开始抗病毒治疗的患者，其中包括 29309 名（64%）仅使用 DAA 方案和 16501 名（36%）干扰素+DAA 方案。我们对患者进行了回顾性随访，直到 2017 年 6 月 15 日，以确定 HCC 的新发病例。我们使用 Cox 比例风险回归来开发和内部验证使用抗病毒治疗时的基线特征预测 HCC 风险的模型。
在平均 2.5 年（范围 1.0-7.5 年）的随访中，我们发现了 1412 例至少在抗病毒治疗开始后 180 天确诊的 HCC 新发病例。为以下四个亚组患者分别开发和验证了预测抗病毒治疗后 HCC 风险的模型：肝硬化/SVR、肝硬化/无 SVR、无肝硬化/SVR、无肝硬化/无 SVR。四个预测因素（年龄、血小板计数、血清天冬氨酸转氨酶/√丙氨酸转氨酶比值和白蛋白）占模型预测价值的大部分，而性别、种族、HCV 基因型、体重指数、血红蛋白和血清甲胎蛋白的贡献较小。拟合模型具有良好的校准度和很好的区分度。决策曲线表明，与全筛或不筛策略相比，使用基于模型的 HCC 风险估计来确定是否推荐筛查可获得更高的净收益。
我们开发并内部验证了可用于估计抗病毒治疗后 HCC 风险的模型。这些模型可作为基于网络的工具，可用于为个体患者提供基于风险的 HCC 监测策略。
大多数丙型肝炎病毒患者已接受或即将接受直接作用抗病毒药物治疗。重要的是，我们可以对这些患者的肝细胞癌风险进行建模，以便我们制定最佳的筛查策略，避免不必要的筛查，同时充分筛查那些风险增加的患者。在此，我们开发并验证了可作为基于网络的工具的模型，可用于指导筛查策略。
