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：
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
项目类别：
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

项目摘要

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HIV治疗级联的最后阶段，它是UNAIDS的框架 90-90-90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（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（美国）（美国）的艾滋病毒感染分别为75％和90％。 Ethe运动重点关注包括南卡罗来纳州（SC）在内的48个县和7个州。鉴于病毒的重要性在结束美国艾滋病毒流行中的抑制，病毒状况的最佳预测模型可以帮助临床医生确定患有病毒性不佳的风险的人，并为艾滋病毒治疗和护理方面的临床改善提供依据。各种各样的在文献中提出了表征纵向病毒结局的指标持续的病毒抑制，病毒反弹，低水平的病毒血症（LLV），持续性LLV和病毒学刀片。但是，我们为开发一种最佳的病毒抑制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结局指标，随访时间有限，有限数据源，缺乏对结构和社会环境数据的考虑患有艾滋病毒（PLWH）的人的样本，以及将研究结果转化为服务准备就绪的努力用于临床使用的工具。自2017年以来，借助NIH支持（R01AI127203），我们利用了大数据方法在全州范围内 SC中的PLWH队列。这项正在进行的研究提取了六个的纵向电子健康记录数据州机构，然后将患者级别的数据与县级数据联系起来（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，医院和医疗保健设施的数量来源。由此产生的集成数据库使我们能够成功地“跟踪” 11,470名患者从2005年到2016年在SC中被诊断为艾滋病毒，并确定HIV治疗联系和保留率的差距。根据R01AI127203的经验和成就，我们提交此申请以检查病毒抑制的纵向动态模式，发展各种病毒的最佳预测模型抑制指标，并将模型转换为可服务的工具供临床使用。在提议中研究，我们将：1）继续“关注”我们的同伙又五年（也扩大了队列在2016 - 2020年之间添加诊断的PLWH）； 2）扩展我们的数据库，以包括关键的其他数据从两个新参与的全州数据源； 3）雇员人工智能（AI）的建模动态病毒载荷模式及其预测因子； 4）制定和试点测试多因素决定临床使用系统。结果将使病毒控制不佳的PLWH识别，并建议 “何时”和“如何”帮助这些PLWH实现并保持病毒抑制。拟议的研究将提高我们对病毒抑制纵向动态的理解，并告知量身定制的艾滋病毒护理 SC及以后的PLWH之间的管理。