Patterns and predictors of viral suppression: A Big Data approach

摘要/概要 持续的病毒抑制是长期治疗成功和死亡率降低的指标， 2010年发起的“结束艾滋病毒流行病：美国计划”联邦运动的战略领域 2019.代表性不足的人口，如种族或族裔少数人口、性和性别少数群体 受艾滋病毒影响最大的群体和社会经济弱势群体， 随后经历了更显著的病毒学失败。2019冠状病毒病大流行正在影响人们的生活 艾滋病病毒感染者（PLWH）以独特的方式。它揭示了艾滋病毒护理更明显的系统性不平等， 加剧了代表性不足的人口中先前存在的结构性差距， 本已脆弱的人群面临更严重的艾滋病毒结果风险，包括病毒抑制。的 2021年资助的父母补助金（R 01 AI 164947）旨在检查病毒的纵向动态模式 抑制，开发各种病毒抑制指标的最佳预测模型，并将 使用南卡罗来纳州（SC）全州范围的艾滋病毒电子健康模型， 记录（EHR）数据。然而，SC全州的艾滋病毒数据库，一个真实世界的数据，不能捕捉足够的 样本代表性不足的人口，因为他们历史上有限的专业护理和学术 作为EHR数据主要来源的医疗中心。我们所有的研究计划，一个国家 这项由美国国立卫生研究院支持的历史性努力，旨在招募一个更广泛的美国人口群体， 超过50%的参与者来自种族和少数民族群体，超过80%来自人口 在生物医学研究中的代表性历来不足。我们所有人的研究计划正在协调数据 持续从多个来源招募，目前已招募了约4800名PLWH，并进行了一系列自我- 报告的调查数据（例如，生活方式、医疗服务可及性、COVID-19参与者体验）及相关 纵向EHR数据（实验室和药物）。鉴于父母补助金的限制（R 01 AI 164947）， 这一行政补助金扩大了父母补助金的范围，以针对广泛定义的代表性不足的艾滋病毒 人口和开发个性化的病毒抑制预测模型使用机器学习技术 通过结合多级因素（例如，COVID-19中断，心理健康，医疗保健 利用率和社会环境因素）使用All of Us大数据资源。的可用性 全面的表型数据和研究人员在我们所有的平台充分保证了 建议项目的透明度和可重复性，从而提高研究的普遍性 调查结果。所提出的个性化病毒抑制预测可以提供定制的数据驱动证据。 针对不同的代表性不足人群的艾滋病毒治疗战略，特别是在面对意外情况时 像COVID-19大流行这样的中断，并最终实现结束艾滋病毒流行的目标 在美国.

Abstract/Summary Sustained viral suppression, an indicator of long-term treatment success and mortality reduction, is one of four strategic areas of the “Ending the HIV Epidemic (EtHE): A Plan for America” federal campaign launched in 2019. Underrepresented populations, such as racial or ethnic minority populations, sexual and gender minority groups, and socioeconomically disadvantaged populations are usually disproportionately affected by HIV and subsequently experience a more striking virological failure. The COVID-19 pandemic is affecting People living with HIV (PLWH) in unique ways. It reveals the more apparent systemic inequities of HIV care due to the exacerbated preexisting structural disparities among underrepresented populations and consequently puts the already vulnerable populations at increased risk of worse HIV outcomes, including viral suppression. The parent grant (R01 AI164947) funded in 2021 aims to examine the longitudinal dynamic pattern of viral suppression, develop optimal predictive models of various viral suppression indicators, and translate the models to service-ready tools for clinical use using the South Carolina (SC) statewide HIV electronic health record (EHR) data. However, the SC statewide HIV database, a real-world data, cannot capture an adequate sample of underrepresented populations due to their historically limited access to specialty care and academic medical centers that serve as the primary sources for EHR data. The All of Us Research Program, a national historic effort supported by the NIH, aims to recruit a broad diverse group of the US population with more than 50% of the participants from racial and ethnic minority groups and more than 80% from populations historically underrepresented in biomedical research. The All of Us Research Program is harmonizing data from multiple sources on an ongoing basis and currently it has recruited ~4800 PLWH with a series of self- reported survey data (e.g., Lifestyle, Healthcare Access, COVID-19 Participant Experience) and relevant longitudinal EHR data (laboratory and medication). Given the limitations of the parent grant (R01 AI164947), this administrative supplement expands the parent grant to target a broadly defined underrepresented HIV population and develop a personalized viral suppression prediction model using machine learning techniques by incorporating multilevel factors (e.g., COVID-19 interruption, psychological wellbeing, healthcare utilization, and social environmental factors) using All of Us big data resources. The availability of comprehensive phenotypic data and the Researcher Workbench in All of Us platform fully assures the transparency and reproducibility of the proposed project and thus increases the generalizability of research findings. The proposed personalized viral suppression prediction can provide data driven evidence on tailored HIV treatment strategies to different underrepresented populations particularly in the face of the unexpected interruptions like the COVID-19 pandemic, and eventually serve towards the goal of ending the HIV epidemic in the US.