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：
10890970
负责人：
Bankole Olatosi
金额：
$ 31.78万
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/10890970
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“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，美国的艾滋病毒感染率将分别减少 75% 和 90%。鉴于病毒的重要性，重点关注美国 48 个县和 7 个州，其中包括南卡罗来纳州 (SC)。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新教徒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。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自 2017 年以来，在 NIH (R01AI127203) 的支持下，我们利用大数据方法来检查检察官之间的治疗差距（例如，错过诊断和护理联系的机会）这项正在进行的研究从六名南卡罗来纳州的 PLWH 群体中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）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，优化开发各种病毒的预测模型抑制指标，并将模型转化为可供临床使用的工具。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，以包含关键的其他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 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该系统的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。拟议的研究将“何时”以及“如何”帮助这些感染者实现并维持病毒抑制。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。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Sexual orientation and gender identity measures and viral suppression for people living with HIV: a protocol for a population-based cohort study.

HIV 感染者的性取向和性别认同措施以及病毒抑制：基于人群的队列研究方案。

DOI：
发表时间：
2024-02-07
期刊：
影响因子：
2.9
作者：
Yang, Xueying;Olatosi, Bankole;Weissman, Sharon;Li, Xiaoming;Zhang, Jiajia
通讯作者：
Zhang, Jiajia

Protocol for developing a personalised prediction model for viral suppression among under-represented populations in the context of the COVID-19 pandemic.

在 COVID-19 大流行背景下开发个性化预测模型以抑制代表性不足人群的病毒的协议。

DOI：
发表时间：
2023-05-15
期刊：
影响因子：
2.9
作者：
Zhang, Jiajia;Yang, Xueying;Weissman, Sharon;Li, Xiaoming;Olatosi, Bankole
通讯作者：
Olatosi, Bankole

Developing an ethical framework-guided instrument for assessing bias in EHR-based Big Data studies: a research protocol.

开发一个道德框架指导的工具来评估基于电子病历的大数据研究中的偏见：一项研究协议。

DOI：
发表时间：
2023-08-17
期刊：
影响因子：
2.9
作者：
Qiao, Shan;Khushf, George;Li, Xiaoming;Zhang, Jiajia;Olatosi, Bankole
通讯作者：
Olatosi, Bankole

Studying patterns and predictors of HIV viral suppression using A Big Data approach: a research protocol.

使用大数据方法研究 HIV 病毒抑制的模式和预测因素：一项研究方案。

DOI：
发表时间：
2022-02-04
期刊：
影响因子：
3.7
作者：
Zhang, Jiajia;Olatosi, Bankole;Yang, Xueying;Weissman, Sharon;Li, Zhenlong;Hu, Jianjun;Li, Xiaoming
通讯作者：
Li, Xiaoming

Examining Incidence of Diabetes in People Living with HIV: Tracking the Shift in Traditional and HIV-related Risk Factors.

检查艾滋病毒感染者的糖尿病发病率：追踪传统风险因素和艾滋病毒相关风险因素的变化。

DOI：
发表时间：
2024-02-06
期刊：
影响因子：
0
作者：
Mohammad Pritom, Gazi Sakir;Yang, Xueying;Gao, Haoyuan;Chen, Shujie;Zhang, Jiajia;Olatosi, Bankole;Li, Xiaoming
通讯作者：
Li, Xiaoming