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.

项目摘要/摘要 联合抗逆转录病毒疗法（CART）的使用显着降低了HIV相关的发病率和 死亡。但是，购物车可能加剧中枢神经系统（CNS）对心理的不利影响 艾滋病毒（PWH）患者的健康。这些不良影响可能导致艺术中断，不良 下游后果，例如HIV疾病进展，健康结果降低和增加 艾滋病毒传播的可能性，引起公共卫生问题。抑郁是最常报告的精神 PWH中CNS损伤引起的健康合并症，患病率在20％至60％之间。理解 导致与艺术相关抑郁症的因素（例如，药物相互作用）至关重要，仍然是高优先级 NIMH研究领域。另外，由于PWH必须无限期地继续进行推车，因此优化顺序推车 长期以来针对个人不断发展的临床特征和治疗史的治疗 对于改善PWH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结合，从而创造了复杂的数据模式，例如 异质性，高维度和稀疏性，使发展适当的挑战是高度挑战 这些问题的统计模型 - 这是我们旨在填补的关键差距。该建议将利用大量 公共艾滋病毒数据集，包括妇女间艾滋病毒艾滋病毒研究（WIHS），以开发数据驱动的方法 促进解密的手推车抑郁关系，并指导更有效的购物车治疗。该提议是 组织为三个目的：1）开发贝叶斯方法来学习纵向推车对抑郁症和 研究效应改性剂（例如，多态药物代谢，衰老）； 2）制定贝叶斯决定 框架以优化个性化的顺序推车分配，以改善长期心理 PWH的健康结果； （3）将统计方法和计算算法汇总到用户友好 用于实际使用，临床翻译和传播的开源软件。这项研究的发现是 期望扩大我们对购物车对抑郁症的影响的理解，并具有潜在的临床实用性 促进艾滋病毒中的精密医学。