Advanced Prediction Models to Optimize Treatment and Access for Veterans with Hepatitis C

背景 慢性丙型肝炎(CHC)是一个主要的公共卫生问题，目前困扰着超过25万退伍军人。这个 美国食品和药物管理局(FDA)最近批准了几种直接作用的抗病毒药物(DAA)用于治疗 慢性丙型肝炎的治疗。与以往以干扰素为基础的治疗方案不同，DAA治疗慢性丙型肝炎非常有效，具有 良好的安全配置，并具有良好的耐受性，引发了巨大的消费者需求。然而，DAA也是 成本极其高昂，使得医疗保健系统难以满足患者日益增长的需求。 医疗保健系统也缺乏足够数量的训练有素的提供者来在一年内治疗所有CHC患者 时间短，进一步限制了访问。但对所有感染患者的立即治疗可能不仅仅是 昂贵得令人望而却步--这也是不必要的。 慢性丙型肝炎患者可分为两大类：(1)晚期肝病患者(肝硬变或“疤痕”)。 肝脏)或其他慢性丙型肝炎的肝外表现(如肾脏疾病)(约占25% 患者)；(2)无进展性肝病或肝外表现者(占~75%) 这项提案的感兴趣人群)。虽然数据和指导方针清楚地说明了 在前一组(即，肝硬化组)的治疗中，他们对后一组的益处不太清楚 (无肝硬化者)。事实上，这些非肝硬变、无症状的患者中的大多数在多年后进展缓慢。 到几十年(低风险患者)，因此可能不需要立即治疗。其他人的进步更快 并且可以从立即治疗中受益。然而，临床医生往往不确定如何处理 这样的病人。目前非肝硬化性慢性丙型肝炎的治疗方法在不同的医疗保健领域有很大的不同 这在很大程度上是由于在指导何时治疗这类患者方面存在差异。因此，治疗是 通常受患者喜好、临床医生判断和药物可获得性的综合影响。退伍军人 处于疾病进展的高风险，但不积极寻求治疗可能因此无法获得潜在的 救命疗法。此外，指导方针继续根据新的可获得性快速变化 毒品。一种系统、严格的治疗方法，由最先进的预测模型提供信息 对非肝硬变退伍军人进行风险分层，有助于指导基于风险的治疗并缓解这种情况 医疗保健服务的缺陷。 目标 本研究的目的是为基于风险的慢性丙型肝炎治疗奠定基础。 退伍军人健康管理局(VHA)中的肝硬变退伍军人：(1)发展准确、临床 相关的、可实施的风险预测模型；(2)让退伍军人就如何 实施基于风险的治疗；以及(3)评估基于风险的治疗的临床和经济效果。 方法 在我们的前期工作中，我们论证了使用机器学习(ML)风险预测模型的可行性 在临床试验队列中确定疾病进展的高风险和低风险患者。我们建议4- 一年的研究，我们将使用2004-2014年的退伍军人管理局电子数据来调整、验证和改进此模型 在退伍军人中。然后，我们将与退伍军人接触，引出他们对基于风险的 通过应用协商一致的技术(例如，协商民主)来处理社区卫生问题。最后，为了估计 基于风险的治疗相对于当前治疗的增量收益，我们将使用模拟建模。

Background Chronic hepatitis C (CHC) is a major public health problem that currently afflicts over 250,000 Veterans. The Food and Drug Administration (FDA) recently approved several direct acting antiviral agents (DAAs) for the treatment of CHC. Unlike prior interferon-based treatment regimens for CHC, DAAs are highly effective, have a favorable safety profile, and are well tolerated, eliciting significant consumer demand. However, DAAs are also extremely costly, making it difficult for healthcare systems to meet this growing demand from patients. Healthcare systems also lack a sufficient number of trained providers to treat all patients with CHC within a short time, further limiting access. But immediate treatment of all infected patients may be not only prohibitively expensive – it is also unnecessary. Patients with CHC fall into two broad categories: (1) those with advanced liver disease (cirrhosis, or “scarring” of the liver) or other extrahepatic manifestations of CHC (e.g., kidney disease) (accounting for ~ 25% of patients); (2) those without advanced liver disease or extrahepatic manifestations (accounting for ~ 75% -- the population of interest for this proposal). While data and guidelines are clear about the short-term benefit of treatment in the former group (i.e., those with cirrhosis), they are less clear about the benefit in the latter group (those without cirrhosis). In fact, most of these non-cirrhotic, asymptomatic patients progress slowly over years to decades (low-risk patients) and thus may not require immediate treatment. Others progress more rapidly and could benefit from immediate treatment. However, clinicians are often uncertain about how to approach such patients. Current treatment approaches for non-cirrhotic CHC vary substantially across healthcare systems, owing largely to discrepancies in guidance on when to treat such patients. As a result, treatment is often driven by a combination of patient preferences, clinician judgment, and drug availability. Veterans who are at high-risk for disease progression but do not actively seek care may therefore fail to receive potentially life-saving therapy. In addition, the guidelines continue to change rapidly based on the availability of new drugs. A systematic, rigorous approach to treatment, one informed by state-of-art prediction modeling to risk-stratify non-cirrhotic Veterans, could help guide risk-based treatment and mitigate this healthcare delivery shortcoming. Objectives The purpose of this study is to lay the groundwork for risk-based treatment of CHC among non- cirrhotic Veterans in the Veterans Health Administration (VHA) by: (1) developing accurate, clinically relevant, and implementable risk prediction models; (2) engaging Veterans to develop consensus on how to implement risk-based treatment; and (3) evaluating the clinical and economic effects of risk-based treatment. Methods In our preliminary work, we demonstrated the feasibility of using a machine-learning (ML) risk prediction model to identify patients at high risk and low risk for disease progression in a clinical trial cohort. We propose a 4- year study where we will use VA electronic data from 2004-2014 to adapt, validate and refine this model among Veterans. We will then engage Veterans, eliciting their preferences and values regarding risk-based treatment of CHC by applying consensus techniques (e.g., deliberative democracy). Finally, to estimate the incremental benefit of risk-based treatment over current treatment, we will use simulation modeling.