Protocol for developing a personalised prediction model for viral suppression among under-represented populations in the context of the COVID-19 pandemic.
Protocol for developing a personalised prediction model for viral suppression among under-represented populations in the context of the COVID-19 pandemic.
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DOI：
10.1136/bmjopen-2022-070869
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发表时间：
2023-05-15
期刊：
影响因子：
2.9
通讯作者：
Olatosi, Bankole
中科院分区：
文献类型：
--
作者：
Zhang, Jiajia;Yang, Xueying;Weissman, Sharon;Li, Xiaoming;Olatosi, Bankole
持续的病毒抑制是长期治疗成功和降低死亡率的指标，是2019年发起的“结束艾滋病毒流行”联邦运动的四个战略领域之一。代表性不足的人口，如种族或少数民族人口、性少数群体和性别少数群体以及社会经济弱势群体，受艾滋病毒的影响不成比例，并经历了更显著的病毒学失败。新冠肺炎大流行可能会放大未被充分代表的艾滋病毒携带者中病毒抑制不完全的风险，原因是医疗保健服务中断以及其他恶化的社会经济和环境条件。然而，生物医学研究很少包括代表性不足的人群，导致算法存在偏见。这项提案针对的是广义上未被充分代表的艾滋病毒人群。它的目标是利用机器学习(ML)技术，利用我们所有人(AOU)的数据纳入多级因素，开发一个个性化的病毒抑制预测模型。这项队列研究将使用来自AOU研究计划的数据，该计划旨在招募在生物医学研究中历来代表性不足的广泛、多样化的美国群体。该方案不断协调来自多个来源的数据。它招募了约4,800名具有一系列自我报告调查数据(如生活方式、医疗保健准入、新冠肺炎参与者体验)和相关纵向电子健康记录数据的 公共健康中心。我们将研究病毒抑制方面的变化，并使用ML技术开发个性化的病毒抑制预测，这些技术包括基于树的分类器(分类和回归树、随机森林、决策树和极端梯度提升)、支持向量机、朴素贝叶斯和长期短期记忆。机构审查委员会批准了南卡罗来纳大学的这项研究(Pro00124806)作为非人类主题研究。调查结果将发表在同行评议的期刊上，并在国家和国际会议上以及通过社交媒体传播。
Sustained viral suppression, an indicator of long-term treatment success and mortality reduction, is one of four strategic areas of the ‘Ending the HIV Epidemic’ federal campaign launched in 2019. Under-represented populations, like racial or ethnic minority populations, sexual and gender minority groups, and socioeconomically disadvantaged populations, are disproportionately affected by HIV and experience a more striking virological failure. The COVID-19 pandemic might magnify the risk of incomplete viral suppression among under-represented people living with HIV (PLWH) due to interruptions in healthcare access and other worsened socioeconomic and environmental conditions. However, biomedical research rarely includes under-represented populations, resulting in biased algorithms. This proposal targets a broadly defined under-represented HIV population. It aims to develop a personalised viral suppression prediction model using machine learning (ML) techniques by incorporating multilevel factors using All of Us (AoU) data. This cohort study will use data from the AoU research programme, which aims to recruit a broad, diverse group of US populations historically under-represented in biomedical research. The programme harmonises data from multiple sources on an ongoing basis. It has recruited ~4800 PLWH with a series of self-reported survey data (eg, Lifestyle, Healthcare Access, COVID-19 Participant Experience) and relevant longitudinal electronic health records data. We will examine the change in viral suppression and develop personalised viral suppression prediction due to the impact of the COVID-19 pandemic using ML techniques, such as tree-based classifiers (classification and regression trees, random forest, decision tree and eXtreme Gradient Boosting), support vector machine, naïve Bayes and long short-term memory. The institutional review board approved the study at the University of South Carolina (Pro00124806) as a Non-Human Subject study. Findings will be published in peer-reviewed journals and disseminated at national and international conferences and through social media.
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影响因子：
4.4
作者：
Matsumoto S;Nagai M;Luong DAD;Nguyen HDT;Nguyen DT;Van Dinh T;Van Tran G;Tanuma J;Pham TN;Oka S
通讯作者：
Oka S
影响因子：
4.7
作者：
通讯作者：
--
DOI：
10.1056/nejmsr1809937
发表时间：
2019-08-15
期刊：
The New England journal of medicine
影响因子：
--
作者：
All of Us Research Program Investigators;Denny JC;Rutter JL;Goldstein DB;Philippakis A;Smoller JW;Jenkins G;Dishman E
通讯作者：
Dishman E
影响因子：
11.8
作者：
Althoff, Keri N.;Schlueter, David J.;Schully, Sheri D.
通讯作者：
Schully, Sheri D.
影响因子：
3.5
作者：
Figueroa RL;Zeng-Treitler Q;Kandula S;Ngo LH
通讯作者：
Ngo LH