Reinforcement learning evaluation of treatment policies for patients with hepatitis C virus.
Reinforcement learning evaluation of treatment policies for patients with hepatitis C virus.
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DOI：
10.1186/s12911-022-01789-7
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发表时间：
2022-03-11
影响因子：
3.5
通讯作者：
Waljee AK
中科院分区：
文献类型：
--
作者：
Oselio B;Singal AG;Zhang X;Van T;Liu B;Zhu J;Waljee AK
在丙型肝炎病毒 (HCV) 治疗等复杂情况下或在资源有限的情况下，对新治疗政策的评估通常成本高昂且具有挑战性。我们试图确定丙肝治疗的假设政策，以在保护资源（财务或其他）的同时最好地平衡肝硬化的预防。该队列由 3792 名 HCV 感染患者组成，在 2015 年至 2019 年国家退伍军人健康管理局基线时没有肝硬化或肝细胞癌病史。为了估计假设治疗政策的疗效，我们利用历史数据和强化学习，以便在构建新的 HCV 治疗策略时具有更大的灵活性。我们测试并比较了四种新的治疗策略：基于天冬氨酸转氨酶与血小板比率指数 (APRI) 的简单逐步策略、基于 APRI 的逻辑回归、针对临床意义预先指定的多个纵向和人口指标的逻辑回归，以及基于针对 HCV 感染开发的风险模型的治疗策略。基于风险的假设治疗政策在治疗最高风险 (346.4±±1.4) 和最少低风险 (361.0±±20.1) 患者的同时，总体风险最低，得分为 0.016 (90% CI 0.016, 0.019)。与治疗大约相同数量患者（1843.7 例与 1914.4 例患者）的假设治疗政策相比，基于风险的政策每位患者的未治疗时间更长（7968.4 例与 7742.9 例患者就诊），这表明医疗保健系统的成本降低。政策外评估策略对于评估假设的治疗政策但未实施非常有用。如果有质量风险模型，基于风险的治疗策略可以降低总体风险并优先考虑患者，同时降低医疗保健系统成本。在线版本包含可在 10.1186/s12911-022-01789-7 获取的补充材料。
Evaluation of new treatment policies is often costly and challenging in complex conditions, such as hepatitis C virus (HCV) treatment, or in limited-resource settings. We sought to identify hypothetical policies for HCV treatment that could best balance the prevention of cirrhosis while preserving resources (financial or otherwise). The cohort consisted of 3792 HCV-infected patients without a history of cirrhosis or hepatocellular carcinoma at baseline from the national Veterans Health Administration from 2015 to 2019. To estimate the efficacy of hypothetical treatment policies, we utilized historical data and reinforcement learning to allow for greater flexibility when constructing new HCV treatment strategies. We tested and compared four new treatment policies: a simple stepwise policy based on Aspartate Aminotransferase to Platelet Ratio Index (APRI), a logistic regression based on APRI, a logistic regression on multiple longitudinal and demographic indicators that were prespecified for clinical significance, and a treatment policy based on a risk model developed for HCV infection. The risk-based hypothetical treatment policy achieved the lowest overall risk with a score of 0.016 (90% CI 0.016, 0.019) while treating the most high-risk (346.4 ± 1.4) and the fewest low-risk (361.0 ± 20.1) patients. Compared to hypothetical treatment policies that treated approximately the same number of patients (1843.7 vs. 1914.4 patients), the risk-based policy had more untreated time per patient (7968.4 vs. 7742.9 patient visits), signaling cost reduction for the healthcare system. Off-policy evaluation strategies are useful to evaluate hypothetical treatment policies without implementation. If a quality risk model is available, risk-based treatment strategies can reduce overall risk and prioritize patients while reducing healthcare system costs. The online version contains supplementary material available at 10.1186/s12911-022-01789-7.
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影响因子：
3.5
作者：
Zheng H;Zhu J;Xie W;Zhong J
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