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

• 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
• 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' 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

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.

项目总结/摘要 联合抗逆转录病毒疗法（cART）的使用大大降低了艾滋病毒相关的发病率， mortality.然而，cART可能会加重中枢神经系统（CNS）相关的不良反应， 艾滋病病毒感染者的健康。这些不良反应可能导致ART停药， 下游后果，如艾滋病毒疾病进展，健康结果下降， 艾滋病毒传播的可能性，引起公共卫生问题。抑郁症是最常见的精神疾病 在PWH中，由CNS损伤引起的健康并发症的患病率范围为20%至60%。理解 因素（例如，药物间相互作用）导致ART相关抑郁症是至关重要的，仍然是一个高度优先事项 NIMH研究领域。此外，由于PWH必须无限期地继续cART，因此优化顺序cART 根据个人不断变化的临床特征和治疗史， 对改善威尔斯亲王医院的长远精神健康十分重要。然而，有许多可能的药物 复杂的药物相互作用的组合，从而产生复杂的数据模式，例如 异质性，高维性和稀疏性，使得开发适当的 这些问题的统计模型-这是我们旨在填补的一个关键空白。这项提案将大大提高 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， 有助于破译cART与抑郁症的关系，并指导更有效的cART治疗。这项建议是 分为三个目标：1）开发贝叶斯方法来学习纵向cART对抑郁症的影响， 研究效果调节剂（例如，多态药物代谢，衰老）; 2）开发贝叶斯决策 框架，以优化个性化的顺序cART分配，目标是改善长期的心理 （3）将统计方法和计算算法纳入用户友好的 开放源码软件的实际使用，临床翻译和传播。这项研究的结果是 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， 促进艾滋病毒的精准医疗。