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

• 批准号: 10828961
• 负责人: Bankole Olatosi
• 金额: $ 14.46万
• 依托单位: UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10828961
• 关键词: Accounting; Address; Alcohol consumption; Area; Artificial Intelligence; Big Data; Binding; Caring; Centers for Disease Control and Prevention (U.S.); Clinical; Complex; County; Data; Data Sources; Databases; Diagnosis; Drug usage; Economics; Electronic Health Record; Epidemic; Epidemiology; Failure; Geographic Locations; Goals; HIV; HIV Infections; HIV diagnosis; Health Personnel; Health Professional; Health care facility; Health system; Individual; Knowledge; Laboratories; Link; Literature; Managed Care; Measures; Medical Records; Modeling; Outcome; Patients; Pattern; Persons; Phase; Population; Regimen; Research; Risk; Rural; Sampling; Services; Social Environment; South Carolina; Surveys; System; Time; Translating; Treatment Protocols; United States; United States National Institutes of Health; Viral; Viral Load result; Viremia; Visit; alcohol and other drug; antiretroviral therapy; cohort; comorbidity; demographics; design; economic indicator; experience; follow-up; health care service utilization; hospital care; immune function; improved; medical specialties; mortality; national surveillance; open data; pilot test; predictive modeling; prisma; restoration; risk prediction model; social; socioeconomics; substance use; success; surveillance data; therapy adherence; tool; transmission process; treatment response; uptake; viral rebound

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.

抽象的 病毒抑制是艾滋病毒级联治疗的最后阶段，该级联治疗是联合国艾滋病规划署的框架 90-90-90 目标。持续病毒抑制是“终结艾滋病毒流行”的四个战略领域之一 (EtHE)：“美国计划”联邦运动于 2019 年 2 月启动，旨在减少新的 到 2025 年和 2030 年，美国的艾滋病毒感染率将分别减少 75% 和 90%。 EtHE 活动 重点关注美国 48 个县和 7 个州，其中包括南卡罗来纳州 (SC)。鉴于病毒的重要性 遏制美国艾滋病毒流行，病毒状态的最佳预测模型可以帮助临床医生 识别那些面临病毒控制不佳风险的人，并为艾滋病毒治疗和护理的临床改进提供信息。各种各样的 文献中提出了表征纵向病毒学结果的指标，例如 持续病毒抑制、病毒反弹、低水平病毒血症（LLV）、持续LLV和病毒学信号。 然而，我们在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差距。 这些差距包括使用有限的病毒学结果指标、有限的随访时间、有限的 数据来源，缺乏对结构和社会环境数据的考虑，数据规模小或不具有代表性 HIV 感染者 (PLWH) 样本，将研究成果转化为可用服务的努力有限 临床使用的工具。自 2017 年以来，在 NIH 的支持 (R01AI127203) 下，我们利用大数据方法 检查全州范围内的治疗差距（例如，错过诊断和护理联系的机会） SC 的 PLWH 队列。这项正在进行的研究从六个方面提取了纵向电子健康记录数据 国家机构然后将患者级数据与县级数据（例如社会经济指标、 来自多个公开数据的医疗保健专业人员、医院和医疗保健机构的数量） 来源。由此产生的综合数据库使我们能够成功“追踪”11,470 名患者 2005 年至 2016 年在 SC 诊断出艾滋病毒并确定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，我们提交此申请以审查 病毒抑制的纵向动态模式，开发各种病毒的最佳预测模型 抑制指标，并将模型转化为可供临床使用的可用工具。在提议的 研究中，我们将：1）继续“跟踪”我们的队列另外五年（并通过以下方式扩展队列） 添加 2016 年至 2020 年期间诊断的 PLWH）； 2）扩展我们的数据库以包含关键的附加数据 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）来自两个 新参与的全州数据源； 3）采用基于人工智能（AI）的建模来理解 动态病毒载量模式及其预测因素； 4) 制定并试点测试多因素决策 系统供临床使用。结果将能够识别病毒控制不佳的感染者，并提出建议 “何时”以及“如何”帮助这些感染者实现并维持病毒抑制。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 南卡罗来纳州及其他地区的 PLWH 管理。