Optimizing HIV Treatment Monitoring under Resource Constraints

 描述(由申请人提供)：随着全球获得艾滋病毒抗逆转录病毒(ARV)治疗的机会不断增加，治疗失败是不可避免的，必须预见到。对治疗失败的正确和早期诊断对于节省成本、对治疗的持久反应以及预防发病率和死亡率至关重要。发展中国家的大多数艾滋病毒治疗方案要么无法获得病毒载量(VL)检测，这是治疗监测的黄金模式，要么只能在有限的基础上应用。其他监测技术，如抗药性测试，由于资金和基础设施的限制，甚至更不常见。在这项提案中，我们将开发、评估和实施方法，以优化在资源有限的环境(RLS)中监测抗逆转录病毒治疗，这些环境具有不同的VL可用性。尽管最近的几项研究已经提出并评估了低成本标记物作为VL替代品和选择性VL使用的策略，但它们通常不是基于正式的决策理论框架，该框架允许发现具有最佳属性的策略，这些策略可以用错分率、成本和其他临床相关参数来表示。我们提议开发发现最佳诊断算法所需的统计框架、理论和方法，以便在VL可用性有限或没有的情况下监测治疗失败；使用来自美国和肯尼亚的队列数据来推导、校准和交叉验证算法；使用PEPFAR资助的艾滋病毒护理计划中患者的现有血浆样本来设计和交叉验证新的诊断算法，其中包括实施汇集分析；以及开发可用的软件，使程序能够根据其患者群体的特征和当地的病毒负载测试能力来设计自己的方案。

 DESCRIPTION (provided by applicant): With escalating global access to HIV antiretroviral (ARV) therapy, treatment failure is inevitable and must be anticipated. Correct and early diagnosis of treatment failure is essential for cost savings, durable response to therapy and prevention of morbidity and mortality. Most HIV treatment programs in the developing world either do not have access to viral load (VL) testing, the gold-standard treatment monitoring modality, or can apply it only on a limited basis. Other monitoring technologies such as drug resistance testing are even less common for financial and infrastructure constraints. In this proposal we will develop, evaluate and implement methods to optimize monitoring of ARV therapy in resource limited settings (RLS) that have diverse VL availability. Though several recent studies have proposed and evaluated lower-cost markers as VL surrogates and strategies for selective VL use, they generally are not based on a formal, decision theoretic framework that allows discovery of strategies with optimality properties that can be expressed in terms of misclassification rate, cost, and other clinically relevant parameters. We propose to develop the statistical framework, theory and methods required to discover optimal diagnostic algorithms for monitoring treatment failure with limited or no VL availability; to use cohort data from both the US and Kenya to derive, calibrate and cross-validate the algorithms; to use extant plasma samples from patients in a PEPFAR-funded HIV care program to design and cross-validate a new diagnostic algorithm that includes implementation of pooled assays; and to develop usable software that will enable programs to design their own protocols based on the characteristics of their patient population and local capacity for viral load testing.