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

INTRODUCTION: 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 gen...

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Autores principales: Zhang, Jiajia, Yang, Xueying, Weissman, Sharon, Li, Xiaoming, Olatosi, Bankole
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10186088/
https://www.ncbi.nlm.nih.gov/pubmed/37188476
http://dx.doi.org/10.1136/bmjopen-2022-070869
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author Zhang, Jiajia
Yang, Xueying
Weissman, Sharon
Li, Xiaoming
Olatosi, Bankole
author_facet Zhang, Jiajia
Yang, Xueying
Weissman, Sharon
Li, Xiaoming
Olatosi, Bankole
author_sort Zhang, Jiajia
collection PubMed
description INTRODUCTION: 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. METHODS AND ANALYSIS: 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. ETHICS AND DISSEMINATION: 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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spelling pubmed-101860882023-05-16 Protocol for developing a personalised prediction model for viral suppression among under-represented populations in the context of the COVID-19 pandemic Zhang, Jiajia Yang, Xueying Weissman, Sharon Li, Xiaoming Olatosi, Bankole BMJ Open HIV/AIDS INTRODUCTION: 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. METHODS AND ANALYSIS: 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. ETHICS AND DISSEMINATION: 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. BMJ Publishing Group 2023-05-15 /pmc/articles/PMC10186088/ /pubmed/37188476 http://dx.doi.org/10.1136/bmjopen-2022-070869 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle HIV/AIDS
Zhang, Jiajia
Yang, Xueying
Weissman, Sharon
Li, Xiaoming
Olatosi, Bankole
Protocol for developing a personalised prediction model for viral suppression among under-represented populations in the context of the COVID-19 pandemic
title Protocol for developing a personalised prediction model for viral suppression among under-represented populations in the context of the COVID-19 pandemic
title_full Protocol for developing a personalised prediction model for viral suppression among under-represented populations in the context of the COVID-19 pandemic
title_fullStr Protocol for developing a personalised prediction model for viral suppression among under-represented populations in the context of the COVID-19 pandemic
title_full_unstemmed Protocol for developing a personalised prediction model for viral suppression among under-represented populations in the context of the COVID-19 pandemic
title_short Protocol for developing a personalised prediction model for viral suppression among under-represented populations in the context of the COVID-19 pandemic
title_sort protocol for developing a personalised prediction model for viral suppression among under-represented populations in the context of the covid-19 pandemic
topic HIV/AIDS
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10186088/
https://www.ncbi.nlm.nih.gov/pubmed/37188476
http://dx.doi.org/10.1136/bmjopen-2022-070869
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