Patterns and predictors of viral suppression: A Big Data approach

摘要 病毒抑制是艾滋病毒治疗级联的最后阶段，它是联合国艾滋病规划署的框架。 90-90-90个进球。持续的病毒抑制是“结束艾滋病毒流行”的四个战略领域之一 2019年2月发起的联邦运动(Ethe：A Plan for America)，旨在减少新的 到2025年和2030年，美国的艾滋病毒感染率分别减少75%和90%。Ethe运动 专注于美国48个县和7个州，包括南卡罗来纳州(SC)。鉴于病毒的重要性 抑制在结束美国艾滋病毒流行中，病毒状态的最佳预测模型可以帮助临床医生 确定那些病毒控制不佳的人，并告知艾滋病毒治疗和护理的临床改进情况。五花八门 在文献中已经提出了表征纵向病毒学结果的指标，例如 持续的病毒抑制、病毒反弹、低水平病毒血症(LLV)、持续性LLV和病毒学斑点。 然而，在我们开发病毒抑制的最佳预测模型的努力中仍然存在一些关键的差距。 这些差距包括使用有限的病毒学结果指标、有限的后续时间、有限的 数据来源，缺乏对结构和社会环境数据的考虑，规模小或不具代表性 艾滋病毒携带者样本(PLWH)，以及将研究成果转化为服务准备的有限努力 临床使用的工具。自2017年以来，在NIH(R01AI127203)的支持下，我们利用大数据方法 检查全州范围内的治疗差距(例如，错过的诊断机会和与护理的联系) 南卡罗来纳州PLWH队列。这项正在进行的研究从六个人中提取了纵向电子健康记录数据 然后将患者级数据与县级数据(例如，社会经济指标， 卫生保健专业人员、医院和卫生保健设施的数量)来自多个公共可用数据 消息来源。由此产生的集成数据库使我们能够成功地“跟踪”11,470名 2005年至2016年在SC被诊断为艾滋病毒，并找出艾滋病毒治疗、联系和保留方面的差距。 基于R01AI127203的经验和成就，我们提交了这份申请，以审查 病毒抑制的纵向动态模式，开发各种病毒的最优预测模型 抑制指标，并将模型转换为临床使用的服务就绪工具。在建议的 研究，我们将：1)继续“跟踪”我们的队列五年(并扩大队列 添加2016-2020年间诊断的PLWH)；2)扩展我们的数据库，以包括更多关于关键疾病的数据 病毒抑制的预测因素(例如，治疗和实验室数据、酒精和药物使用数据) 新加入的全州范围的数据源；3)使用基于人工智能(AI)的建模来理解 动态病毒载量模式及其预测因素；以及4)开发和试行多因素决策 临床使用的系统。这一结果将有助于识别病毒控制较差的PLWH，并提示 什么时候和如何帮助那些PLWH实现和保持病毒抑制。拟议的研究将 提高我们对病毒抑制纵向动态的理解，并为量身定制的艾滋病毒护理提供信息 在供应链及更高级别的公共卫生部门进行管理。

Abstract Viral suppression is the final stage of the HIV treatment cascade, which serves as the framework for UNAIDS’ 90-90-90 goals. Sustained viral suppression is one of four strategic areas of the “Ending the HIV Epidemic (EtHE): A Plan for America” federal campaign, launched in February 2019, which aims for the reduction of new HIV infections in the United States (US) by 75% and 90% by 2025 and 2030, respectively. The EtHE campaign focuses on 48 US counties and 7 states, including South Carolina (SC). Given the importance of viral suppression in ending the US HIV epidemic, an optimal predictive model of viral status can help clinicians identify those at risk of poor viral control and inform clinical improvements in HIV treatment and care. Various indicators to characterize the longitudinal virologic outcomes have been proposed in the literature such as sustained viral suppression, viral rebound, low-level viraemia (LLV), persistent LLV, and virologic blips. However, some critical gaps still exist in our efforts to develop an optimal predictive model of viral suppression. These gaps include the use of limited indicators of virologic outcomes, limited duration of follow-up, limited data sources, lack of consideration of structural and socioenvironmental data, small or unrepresentative samples of people living with HIV (PLWH), and limited efforts to translate research findings into service-ready tools for clinical use. With NIH support (R01AI127203) since 2017, we have utilized a Big Data approach to examine treatment gaps (e.g., missed opportunities for diagnosis and linkage to care) among a statewide cohort of PLWH in SC. This ongoing research extracted longitudinal electronic health records data from six state agencies and then linked the patient-level data with county-level data (e.g., socioeconomic indicators, number of health care professionals, hospitals, and health care facilities) from multiple publicly available data sources. The resultant integrated database has enabled us to successfully “track” 11,470 patients who were diagnosed with HIV from 2005 to 2016 in SC and identify the gaps in HIV treatment linkage and retention. Based on the experience and accomplishment of the R01AI127203, we submit this application 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. In the proposed research, we will: 1) continue to “follow” our cohort for another five years (and also expand the cohort by adding PLWH diagnosed between 2016-2020); 2) expand our database to include additional data on critical predictors of viral suppression (e.g., treatment and laboratory data, alcohol and substance use data) from two newly participating statewide data sources; 3) employ artificial intelligence (AI)-based modeling to understand the dynamic viral load patterns and their predictors; and 4) develop and pilot-test a multifactorial decision system for clinical use. The results will enable the identification of PLWH with poor viral control and suggest “when” and “how” to help those PLWH achieve and maintain viral suppression. The proposed research will improve our understanding of the longitudinal dynamics of viral suppression and inform tailored HIV care management among PLWH in SC and beyond.