Patterns and predictors of viral suppression: A Big Data approach

病毒抑制的模式和预测因素：大数据方法

基本信息

批准号：
10658458
负责人：
Bankole Olatosi
金额：
$ 9.31万
依托单位：
UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-09 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10658458
关键词：
Academic Medical Centers Administrative Supplement Affect Age All of Us Research Program Americas Area Asian Big Data Biomedical Research Bisexual Black race COVID-19 COVID-19 pandemic COVID-19 pandemic effects Caring Categories Centers for Disease Control and Prevention (U.S.)Clinical Clinical Management Country Data Databases Development Discipline Education Electronic Health Record Environmental Risk Factor Epidemic European Failure Food Funding Gays Genetic Geographic Locations Goals Guidelines HIV HIV Infections Health Household Income Individual Injecting drug user Interruption Intersex Knowledge Laboratories Lesbian Life Style Machine Learning Measurement Medical Mental Depression Mental Health Modeling National Institute of Allergy and Infectious Disease Outcome Participant Patient Self-Report Pattern Persons Pharmaceutical Preparations Population Probability Prostitution Questionnaires Reporting Reproducibility Research Research Personnel Risk Sample Size Sampling San Francisco Series Services Sex Orientation Sexual and Gender Minorities Socioeconomic Factors Socioeconomic Status Source South Carolina Stigmatization Surveys Techniques Translating Underrepresented Populations United Nations United States United States National Institutes of Health Viral Vulnerable Populations Well in self Youth asexual base behavioral health cohort comorbidity cost data acquisition data harmonization data resource disadvantaged population ethnic minority population experience gender minority gender minority group health care availability health care service utilization health equity promotion medical specialties member men men who have sex with men mortality multiple data sources national surveillance pandemic disease parent grant personalized predictions phenotypic data physically handicapped predictive modeling queer racial minority recruit resilience sexual minority social socioeconomic disadvantage socioeconomics success surveillance data tool transgender transgender women treatment strategy

项目摘要

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.

摘要/总结持续的病毒抑制是长期治疗成功和死亡率降低的指标，是四个指标之一 “结束艾滋病毒流行（EtHE）：美国计划”联邦运动的战略领域 2019. 代表性不足的人群，例如种族或族裔少数群体、性少数群体和性别少数群体群体和社会经济弱势群体通常不成比例地受到艾滋病毒和随后经历了更引人注目的病毒学失败，COVID-19 大流行正在影响人们。它以独特的方式揭示了艾滋病毒护理的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异，从而使本来就脆弱的人群面临更严重的艾滋病毒后果（包括病毒抑制）的风险增加。 2021 年资助的家长补助金 (R01 AI164947) 旨在研究病毒的纵向动态模式抑制，开发各种病毒抑制指标的预测最佳模型，并将使用南卡罗来纳州 (SC) 居民 HIV 电子健康的模型到可供临床使用的服务就绪工具然而，SC 流行的艾滋病毒数据库（真实世界的数据）无法捕获足够的数据。由于历史上获得专业护理和学术机会有限而代表性不足的人群样本作为 EHR 数据主要来源的医疗中心，这是一项国家级研究计划。美国国立卫生研究院 (NIH) 支持的历史性努力旨在招募广泛多样化的美国人口群体，并提供更多信息超过 50% 的参与者来自种族和族裔群体，超过 80% 的参与者来自人口历史上在生物医学研究中代表性不足的“我们所有人研究计划”正在协调数据。持续从多个来源招募，目前已通过一系列自我招募招募了约 4800 名感染者报告的调查数据（例如，生活方式、医疗保健获取、COVID-19 参与者体验）和相关纵向 EHR 数据（实验室和药物）。鉴于家长补助金 (R01 AI164947) 的限制，该行政补充扩大了家长补助金，以针对广泛定义的代表性不足的艾滋病毒人口并使用机器学习技术开发个性化病毒抑制预测模型通过纳入多层次因素（例如，COVID-19 中断、心理健康、医疗保健利用我们所有人的大数据资源。全面的表型数据和 All of Us 平台中的研究人员工作台充分保证拟议项目的透明度和可重复性，从而提高研究的普遍性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。针对不同代表性不足人群的艾滋病毒治疗策略，特别是面对意外情况时像 COVID-19 大流行这样的中断，最终有助于结束艾滋病毒流行的目标在美国。