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Personalized life expectancy and treatment benefit index of antiretroviral therapy
BACKGROUND: The progression of Human Immunodeficiency Virus (HIV) within host includes typical stages and the Antiretroviral Therapy (ART) is shown to be effective in slowing down this progression. There are great challenges in describing the entire HIV disease progression and evaluating comprehensi...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5242026/ https://www.ncbi.nlm.nih.gov/pubmed/28100241 http://dx.doi.org/10.1186/s12976-016-0047-0 |
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author | Xiao, Yanni Sun, Xiaodan Tang, Sanyi Zhou, Yicang Peng, Zhihang Wu, Jianhong Wang, Ning |
author_facet | Xiao, Yanni Sun, Xiaodan Tang, Sanyi Zhou, Yicang Peng, Zhihang Wu, Jianhong Wang, Ning |
author_sort | Xiao, Yanni |
collection | PubMed |
description | BACKGROUND: The progression of Human Immunodeficiency Virus (HIV) within host includes typical stages and the Antiretroviral Therapy (ART) is shown to be effective in slowing down this progression. There are great challenges in describing the entire HIV disease progression and evaluating comprehensive effects of ART on life expectancy for HIV infected individuals on ART. METHODS: We develop a novel summative treatment benefit index (TBI), based on an HIV viral dynamics model and linking the infection and viral production rates to the Weibull function. This index summarizes the integrated effect of ART on the life expectancy (LE) of a patient, and more importantly, can be reconstructed from the individual clinic data. RESULTS: The proposed model, faithfully mimicking the entire HIV disease progression, enables us to predict life expectancy and trace back the timing of infection. We fit the model to the longitudinal data in a cohort study in China to reconstruct the treatment benefit index, and we describe the dependence of individual life expectancy on key ART treatment specifics including the timing of ART initiation, timing of emergence of drug resistant virus variants and ART adherence. CONCLUSIONS: We show that combining model predictions with monitored CD4 counts and viral loads can provide critical information about the disease progression, to assist the design of ART regimen for maximizing the treatment benefits. |
format | Online Article Text |
id | pubmed-5242026 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-52420262017-01-23 Personalized life expectancy and treatment benefit index of antiretroviral therapy Xiao, Yanni Sun, Xiaodan Tang, Sanyi Zhou, Yicang Peng, Zhihang Wu, Jianhong Wang, Ning Theor Biol Med Model Research BACKGROUND: The progression of Human Immunodeficiency Virus (HIV) within host includes typical stages and the Antiretroviral Therapy (ART) is shown to be effective in slowing down this progression. There are great challenges in describing the entire HIV disease progression and evaluating comprehensive effects of ART on life expectancy for HIV infected individuals on ART. METHODS: We develop a novel summative treatment benefit index (TBI), based on an HIV viral dynamics model and linking the infection and viral production rates to the Weibull function. This index summarizes the integrated effect of ART on the life expectancy (LE) of a patient, and more importantly, can be reconstructed from the individual clinic data. RESULTS: The proposed model, faithfully mimicking the entire HIV disease progression, enables us to predict life expectancy and trace back the timing of infection. We fit the model to the longitudinal data in a cohort study in China to reconstruct the treatment benefit index, and we describe the dependence of individual life expectancy on key ART treatment specifics including the timing of ART initiation, timing of emergence of drug resistant virus variants and ART adherence. CONCLUSIONS: We show that combining model predictions with monitored CD4 counts and viral loads can provide critical information about the disease progression, to assist the design of ART regimen for maximizing the treatment benefits. BioMed Central 2017-01-18 /pmc/articles/PMC5242026/ /pubmed/28100241 http://dx.doi.org/10.1186/s12976-016-0047-0 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Xiao, Yanni Sun, Xiaodan Tang, Sanyi Zhou, Yicang Peng, Zhihang Wu, Jianhong Wang, Ning Personalized life expectancy and treatment benefit index of antiretroviral therapy |
title | Personalized life expectancy and treatment benefit index of antiretroviral therapy |
title_full | Personalized life expectancy and treatment benefit index of antiretroviral therapy |
title_fullStr | Personalized life expectancy and treatment benefit index of antiretroviral therapy |
title_full_unstemmed | Personalized life expectancy and treatment benefit index of antiretroviral therapy |
title_short | Personalized life expectancy and treatment benefit index of antiretroviral therapy |
title_sort | personalized life expectancy and treatment benefit index of antiretroviral therapy |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5242026/ https://www.ncbi.nlm.nih.gov/pubmed/28100241 http://dx.doi.org/10.1186/s12976-016-0047-0 |
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