Studying patterns and predictors of HIV viral suppression using A Big Data approach: a research protocol.
Studying patterns and predictors of HIV viral suppression using A Big Data approach: a research protocol.
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DOI：
10.1186/s12879-022-07047-5
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发表时间：
2022-02-04
影响因子：
3.7
通讯作者：
Li X
中科院分区：
文献类型：
--
作者：
Zhang J;Olatosi B;Yang X;Weissman S;Li Z;Hu J;Li X
鉴于病毒抑制在结束美国和其他地方的艾滋病毒流行方面的重要性，病毒状态的最佳预测模型可以帮助临床医生识别病毒控制不良的风险，并为艾滋病毒治疗和护理的临床改进提供信息。随着电子健康记录（EHR）数据和社会环境信息的日益可用性，我们有一个独特的机会来提高我们对病毒抑制动态模式的理解。使用一个全州队列的艾滋病毒感染者（PLWH）在南卡罗来纳州（SC），拟议的研究的总体目标是检查病毒抑制的动态模式，开发各种病毒抑制指标的最佳预测模型，并将模型转化为临床决策支持的服务就绪工具的测试版。将通过SC增强的艾滋病毒/艾滋病报告系统（eHARS）确定艾滋病毒携带者/艾滋病患者队列。SC收入和财政事务办公室（RFA）将从多个卫生系统中提取SC中所有PLWH的纵向EHR临床数据，从其他州机构获得数据，并将患者级数据与来自多个公开数据源的县级数据联系起来。使用去识别数据，拟议的研究将包括三个操作阶段：第一阶段：“模式分析”，以确定使用多个病毒载量指标的病毒抑制的纵向动态;第二阶段：“模型开发”，通过基于人工智能（AI）的建模来确定多个病毒载量指标的关键预测因子，考虑多水平因素;第三阶段：“转化研究”旨在开发基于风险预测模型的多因素临床决策系统，以帮助识别患者在临床访视时病毒失败或病毒反弹的风险。通过广泛的数据整合和数据分析，拟议的研究将：（1）在考虑多层次因素的同时，提高对HIV病毒供应和HIV治疗史纵向轨迹的复杂相互关联影响的理解;以及（2）制定经验性的公共卫生方法，通过将风险预测模型转化为多因素决策系统，使人工智能辅助临床决策成为可能。
Given the importance of viral suppression in ending the HIV epidemic in the US and elsewhere, 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. With an increasing availability of electronic health record (EHR) data and social environmental information, there is a unique opportunity to improve our understanding of the dynamic pattern of viral suppression. Using a statewide cohort of people living with HIV (PLWH) in South Carolina (SC), the overall goal of the proposed research is to examine the dynamic patterns of viral suppression, develop optimal predictive models of various viral suppression indicators, and translate the models to a beta version of service-ready tools for clinical decision support. The PLWH cohort will be identified through the SC Enhanced HIV/AIDS Reporting System (eHARS). The SC Office of Revenue and Fiscal Affairs (RFA) will extract longitudinal EHR clinical data of all PLWH in SC from multiple health systems, obtain data from other state agencies, and link the patient-level data with county-level data from multiple publicly available data sources. Using the deidentified data, the proposed study will consist of three operational phases: Phase 1: “Pattern Analysis” to identify the longitudinal dynamics of viral suppression using multiple viral load indicators; Phase 2: “Model Development” to determine the critical predictors of multiple viral load indicators through artificial intelligence (AI)-based modeling accounting for multilevel factors; and Phase 3: “Translational Research” to develop a multifactorial clinical decision system based on a risk prediction model to assist with the identification of the risk of viral failure or viral rebound when patients present at clinical visits. With both extensive data integration and data analytics, the proposed research will: (1) improve the understanding of the complex inter-related effects of longitudinal trajectories of HIV viral suppressions and HIV treatment history while taking into consideration multilevel factors; and (2) develop empirical public health approaches to achieve ending the HIV epidemic through translating the risk prediction model to a multifactorial decision system that enables the feasibility of AI-assisted clinical decisions.
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