Characterizing the impact of HIV disease severity in prediction models for tuberculosis treatment outcomes

项目摘要 全球结核病(TB)的负担令人震惊。2017年，1000万人罹患结核病，160万人罹患结核病 全世界都有人死于结核病。成功治疗药物敏感的结核病，定义为结合 临床治愈或治疗完成，需要至少六个月的治疗。尽管可以广泛获得 这样的治疗，全球的治疗成功率是次优的，2016年估计只有82%。结核病/艾滋病毒共同作用 感染使这两种疾病的治疗进一步复杂化，因为药物-药物相互作用，高药片负担，以及 免疫功能减弱。然而，HIV相关疾病特征的程度，如CD4 细胞计数、HIV-1RNA病毒载量和开始抗逆转录病毒治疗的时间对结核病治疗结果的影响是 不清楚。为了优化成功的治疗结果，医疗保健提供者需要简单有效的工具 开始治疗，以确定预后不良风险最大的结核病患者，其预后可能 通过量身定制的护理管理和治疗监测加以改进。 为了解决这些知识差距，这项建议旨在开发一个结核病治疗的预测模型 结果，重点是确定艾滋病毒严重程度的特征。我还将把药物基因组数据纳入 乙酰化状态，由已知的NAT2基因变异所告知，这些变异参与了 异烟肼。异烟肼是在整个6个月的标准治疗过程中服用的两种结核病药物之一，其疗效 是治愈结核病的基础。量化艾滋病毒严重程度和乙酰化状态附加值的计划 预测模型将有助于理解收集此类数据是否会改善结核病结果预测 值得在临床环境中收集。 这项研究将利用区域结核病前瞻性观察研究的现有数据 (报告)-巴西项目，由美国国立卫生研究院和巴西卫生部共同资助的观察性队列 在巴西登记了940例经培养确认的肺结核病例。拟议的研究将解决一个 临床、流行病学和药物基因组学因素是什么组合的基本问题 能够最好地预测结核病治疗结果。它将支持未来结核病治疗中以患者为中心的方法。 此外，创新的培训计划将促进我的学术发展，因为我正在攻读博士前 在范德比尔特大学接受流行病学培训。强大和充满活力的指导相结合，相关 结核病方面的专门知识，以及预测建模和药物基因组数据方面的重点方法培训 分析将提供一个坚实的基础，以建立一个成功的职业生涯作为独立调查员 结核病流行病学方面的专业知识。

PROJECT ABSTRACT The global burden of tuberculosis (TB) is staggering. In 2017, 10 million people developed TB and 1.6 million people died from TB worldwide. Successful treatment of drug-susceptible TB, defined as the combination of clinical cure or treatment completion, requires at least six months of therapy. Despite widespread availability of such treatment, the global treatment success rate is sub-optimal, estimated at only 82% in 2016. TB/HIV co- infection further complicates treatment of both conditions due to drug-drug interactions, high pill burden, and diminished immune function. However, the degree to which HIV-related disease characteristics, such as CD4 cell count, HIV-1 RNA viral load, and timing of antiretroviral therapy initiation, impact TB treatment outcomes is unclear. To optimize successful treatment outcomes, healthcare providers need simple and effective tools at the start of treatment to identify TB patients at the greatest risk of poor outcomes, and whose prognosis could be improved through tailored care management and treatment monitoring. To address these knowledge gaps, this proposal aims to develop a prediction model for TB treatment outcomes with a focus on characterizing HIV severity. I will also incorporate pharmacogenomic data on acetylation status, informed by known genetic variants of NAT2 that are involved in the metabolism of isoniazid. Isoniazid is one of two TB drugs taken for the entire 6 months of standard treatment, and its efficacy is fundamental to TB cure. The plan to quantify the added value of HIV severity and acetylation status in the prediction model will be useful to understand whether collection of such data improves TB outcome prediction and is worthwhile to collect in clinical settings. This study will leverage existing data from the Regional Prospective Observational Research for Tuberculosis (RePORT)-Brazil project, an observational cohort co-funded by the NIH and Brazilian Ministry of Health that has enrolled 940 culture-confirmed, pulmonary TB cases in Brazil. The proposed research will address a fundamental question about what combination of clinical, epidemiologic, and pharmacogenomic factors are best able to predict TB treatment outcomes. It will support future patient-centered approaches in TB therapy. Additionally, the innovative training plan will foster my academic development as I further my predoctoral training in epidemiology at Vanderbilt University. The combination of strong and dynamic mentorship, relevant TB-specific knowledge, and focused methodologic training in prediction modeling and pharmacogenomic data analysis will provide a solid foundation to build a successful career as an independent investigator with expertise in TB epidemiology.