Bayesian Methods for Optimizing Combination Antiretroviral Therapy for Mentalhealth in People with HIV

优化艾滋病毒感染者心理健康联合抗逆转录病毒治疗的贝叶斯方法

基本信息

批准号：
10642867
负责人：
Yanxun Xu
金额：
$ 39.71万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-10 至 2027-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10642867
关键词：
Address Adverse effects Age Aging Area Bayesian Method Bayesian Modeling Behavioral Caring Central Nervous System Characteristics Chronic Disease Clinical Complex Computational algorithm Computer software Data Data Set Data Sources Dimensions Disease Disease Outcome Disease Progression Drug Combinations Drug Interactions Elderly Encapsulated FDA approved Genetic Genetic Polymorphism Goals Guidelines HIV HIV Infections HIV therapy Health Health Care Costs Heterogeneity Individual Knowledge Laboratories Learning Link Long-Term Effects Longitudinal Studies Mathematics Measurement Mediating Mental Depression Mental Health Methods Modeling Morbidity - disease rate National Institute of Mental Health Outcome Pattern Persons Pharmaceutical Preparations Physicians Population Prevalence Public Health Recommendation Recording of previous events Reporting Research Risk Selection for Treatments Self Care Statistical Methods Statistical Models Structure Time Trees Uncertainty United States Dept. of Health and Human Services Viral Women&apos s Interagency HIV Study antiretroviral therapy central nervous system injury clinical translation combinatorial comorbidity complex data data complexity demographics drug metabolism efavirenz high dimensionality improved kernel methods model building mortality multiple data sources open source personalized medicine practical application precision medicine prevent public health relevance side effect software development study population success transmission process treatment adherence treatment choice treatment planning user-friendly web app

项目摘要

Project Summary/Abstract The use of combination antiretroviral therapy (cART) has significantly reduced HIV-related morbidity and mortality. However, cART may exacerbate the central nervous system (CNS)-related adverse effects on mental health for people with HIV (PWH). These adverse effects may result in ART discontinuation with undesirable downstream consequences such as HIV disease progression, decreased health outcomes, and increased likelihood of HIV transmission, causing public health concerns. Depression is the most frequently reported mental health comorbidity caused by CNS injury in PWH, with prevalence ranging from 20% to 60%. Understanding factors (e.g., drug-drug interactions) contributing to ART-related depression is critical and remains a high priority NIMH research area. In addition, since PWH must continue cART indefinitely, optimizing sequential cART treatments over a long-time span tailored to individuals’ evolving clinical characteristics and treatment histories is important for improving long-term mental health for PWH. However, there are numerous possible drug combinations with complicated drug-drug interactions and thus creating complex data patterns, such as heterogeneity, high-dimensionality, and sparseness, making it highly challenging to develop appropriate statistical models for these problems - which is a critical gap we aim to fill. This proposal will leverage large public HIV datasets, including Women's Interagency HIV Study (WIHS), to develop data-driven approaches to facilitate deciphering cART-depression relations and guide more effective cART treatments. This proposal is organized into three aims: 1) Develop Bayesian methods to learn longitudinal cART effects on depression and investigate effect modifiers (e.g., polymorphic drug metabolism, aging); 2) Develop Bayesian decision frameworks to optimize personalized sequential cART assignments with the goal of improving long-term mental health outcomes for PWH; (3) encapsulate statistical methods and computational algorithms into user-friendly open-source software for practical use, clinical translation, and dissemination. Findings from this study are expected to expand our understanding of cART effects on depression, and have potential clinical utility to facilitate precision medicine in HIV.

项目概要/摘要联合抗逆转录病毒疗法 (cART) 的使用显着降低了 HIV 相关发病率然而，cART 可能会加剧中枢神经系统 (CNS) 相关的精神不良影响。这些不利影响可能会导致 ART 的终止，从而产生不良后果。下游后果，例如艾滋病毒疾病进展减少、健康结果增加艾滋病毒传播的可能性，引起公共健康问题抑郁症是最常报告的精神疾病。感染者中枢神经系统损伤引起的健康合并症，患病率范围为 20% 至 60%。导致 ART 相关抑郁症的因素（例如药物间相互作用）至关重要，并且仍然是高度优先的事项此外，由于 PWH 必须无限期地继续 cART，因此优化了序贯 cART。根据个人不断变化的临床特征和治疗史进行长期治疗对于改善感染者的长期心理健康非常重要，但是，有许多可能的药物。与复杂的药物相互作用的组合，从而创建复杂的数据模式，例如异构性、高维性和稀疏性，使得开发合适的这些问题的统计模型 - 这是我们旨在填补的一个关键空白。公共艾滋病毒数据集，包括妇女机构间艾滋病毒研究（WIHS），以开发数据驱动的方法促进破译 cART 与抑郁症的关系并指导更有效的 cART 治疗。分为三个目标：1) 开发贝叶斯方法来了解纵向 cART 对抑郁症和抑郁症的影响研究效应调节因素（例如多态药物代谢、衰老）；2) 制定贝叶斯决策；优化个性化顺序 cART 作业的框架，目标是改善长期心理 PWH 的健康结果；（3）将统计方法和计算算法封装成用户友好的用于实际使用、临床转化和传播的开源软件。预计将扩大我们对 cART 对抑郁症影响的理解，并具有潜在的临床实用性促进艾滋病毒精准医疗。