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Optimizing HIV Treatment Monitoring under Resource Constraints

在资源限制下优化艾滋病毒治疗监测

基本信息

批准号：
9174892
负责人：
JOSEPH W HOGAN
金额：
$ 69.56万
依托单位：
BROWN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-11-15 至 2019-10-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9174892
关键词：
Algorithms Archives Biological Assay Calibration Canada Caring Cations Characteristics Cities Clinical Clinical Markers Computer software Cost Savings Data Diagnosis Diagnostic Diagnostic tests Drug resistance Early Diagnosis Effectiveness Europe Failure Funding Goals Gold Guidelines HIV HIV antiretroviral HIV therapy Individual Kenya Laboratories Machine Learning Methods Modality Modeling Monitor Morbidity - disease rate Motivation Outcome Patient Care Patient-Focused Outcomes Patients Plasma Population Prevention Primary Health Care Property Protocols documentation Provider Regimen Research Infrastructure Resources Risk Sampling Schedule Side Statistical Methods Techniques Technology Testing Time Treatment Failure Viral Viral Load result World Health Organization accurate diagnosis antiretroviral therapy base clinically relevant cohort cost design effective therapy health care availability improved learning strategy mortality novel diagnostics patient population prevent programs public health relevance resistant strain response simulation software development standard care theories treatment program virology

项目摘要

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.