Cargando…

Modelling trends of CD4 counts for patients on antiretroviral therapy (ART): a comprehensive health care clinic in Nairobi, Kenya

BACKGROUND: In resource-limited settings, changes in CD4 counts constitute an important component in patient monitoring and evaluation of treatment response as these patients do not have access to routine viral load testing. In this study, we quantified trends on CD4 counts in patients on highly act...

Descripción completa

Detalles Bibliográficos
Autores principales: Mugo, Caroline W., Shkedy, Ziv, Mwalili, Samuel, Awoke, Tadesse, Braekers, Roel, Wandede, Dolphine, Mwachari, Christina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8725499/
https://www.ncbi.nlm.nih.gov/pubmed/34983418
http://dx.doi.org/10.1186/s12879-021-06977-w
_version_ 1784626131604865024
author Mugo, Caroline W.
Shkedy, Ziv
Mwalili, Samuel
Awoke, Tadesse
Braekers, Roel
Wandede, Dolphine
Mwachari, Christina
author_facet Mugo, Caroline W.
Shkedy, Ziv
Mwalili, Samuel
Awoke, Tadesse
Braekers, Roel
Wandede, Dolphine
Mwachari, Christina
author_sort Mugo, Caroline W.
collection PubMed
description BACKGROUND: In resource-limited settings, changes in CD4 counts constitute an important component in patient monitoring and evaluation of treatment response as these patients do not have access to routine viral load testing. In this study, we quantified trends on CD4 counts in patients on highly active antiretroviral therapy (HAART) in a comprehensive health care clinic in Kenya between 2011 and 2017. We evaluated the rate of change in CD4 cell count in response to antiretroviral treatment. We further assessed factors that influenced time to treatment change focusing on baseline characteristics of the patients and different initial drug regimens used. This was a retrospective study involving 432 naïve HIV patients that had at least two CD4 count measurements for the period. The relationship between CD4 cell count and time was modeled using a semi parametric mixed effects model while the Cox proportional hazards model was used to assess factors associated with the first regimen change. RESULTS: Majority of the patients were females and the average CD4 count at start of treatment was 362.1 [Formula: see text] . The CD4 count measurements increased nonlinearly over time and these trends were similar regardless of the treatment regimen administered to the patients. The change of logarithm CD4 cell count rises fast for in the first 450 days of antiretroviral initiation. The average time to first regimen change was 2142 days. Tenoforvir (TDF) based regimens had a lower drug substitution(aHR 0.2682, 95% CI:0.08263- 0.8706) compared to Zidovudine(AZT). CONCLUSION: The backbone used was found to be associated with regimen changes among the patients with fewer switches being observed, with the use of TDF when compared to AZT. There was however no significant difference between TDF and AZT in terms of the rate of change in logarithm CD4 count over time. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-021-06977-w.
format Online
Article
Text
id pubmed-8725499
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-87254992022-01-06 Modelling trends of CD4 counts for patients on antiretroviral therapy (ART): a comprehensive health care clinic in Nairobi, Kenya Mugo, Caroline W. Shkedy, Ziv Mwalili, Samuel Awoke, Tadesse Braekers, Roel Wandede, Dolphine Mwachari, Christina BMC Infect Dis Research BACKGROUND: In resource-limited settings, changes in CD4 counts constitute an important component in patient monitoring and evaluation of treatment response as these patients do not have access to routine viral load testing. In this study, we quantified trends on CD4 counts in patients on highly active antiretroviral therapy (HAART) in a comprehensive health care clinic in Kenya between 2011 and 2017. We evaluated the rate of change in CD4 cell count in response to antiretroviral treatment. We further assessed factors that influenced time to treatment change focusing on baseline characteristics of the patients and different initial drug regimens used. This was a retrospective study involving 432 naïve HIV patients that had at least two CD4 count measurements for the period. The relationship between CD4 cell count and time was modeled using a semi parametric mixed effects model while the Cox proportional hazards model was used to assess factors associated with the first regimen change. RESULTS: Majority of the patients were females and the average CD4 count at start of treatment was 362.1 [Formula: see text] . The CD4 count measurements increased nonlinearly over time and these trends were similar regardless of the treatment regimen administered to the patients. The change of logarithm CD4 cell count rises fast for in the first 450 days of antiretroviral initiation. The average time to first regimen change was 2142 days. Tenoforvir (TDF) based regimens had a lower drug substitution(aHR 0.2682, 95% CI:0.08263- 0.8706) compared to Zidovudine(AZT). CONCLUSION: The backbone used was found to be associated with regimen changes among the patients with fewer switches being observed, with the use of TDF when compared to AZT. There was however no significant difference between TDF and AZT in terms of the rate of change in logarithm CD4 count over time. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-021-06977-w. BioMed Central 2022-01-04 /pmc/articles/PMC8725499/ /pubmed/34983418 http://dx.doi.org/10.1186/s12879-021-06977-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Mugo, Caroline W.
Shkedy, Ziv
Mwalili, Samuel
Awoke, Tadesse
Braekers, Roel
Wandede, Dolphine
Mwachari, Christina
Modelling trends of CD4 counts for patients on antiretroviral therapy (ART): a comprehensive health care clinic in Nairobi, Kenya
title Modelling trends of CD4 counts for patients on antiretroviral therapy (ART): a comprehensive health care clinic in Nairobi, Kenya
title_full Modelling trends of CD4 counts for patients on antiretroviral therapy (ART): a comprehensive health care clinic in Nairobi, Kenya
title_fullStr Modelling trends of CD4 counts for patients on antiretroviral therapy (ART): a comprehensive health care clinic in Nairobi, Kenya
title_full_unstemmed Modelling trends of CD4 counts for patients on antiretroviral therapy (ART): a comprehensive health care clinic in Nairobi, Kenya
title_short Modelling trends of CD4 counts for patients on antiretroviral therapy (ART): a comprehensive health care clinic in Nairobi, Kenya
title_sort modelling trends of cd4 counts for patients on antiretroviral therapy (art): a comprehensive health care clinic in nairobi, kenya
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8725499/
https://www.ncbi.nlm.nih.gov/pubmed/34983418
http://dx.doi.org/10.1186/s12879-021-06977-w
work_keys_str_mv AT mugocarolinew modellingtrendsofcd4countsforpatientsonantiretroviraltherapyartacomprehensivehealthcareclinicinnairobikenya
AT shkedyziv modellingtrendsofcd4countsforpatientsonantiretroviraltherapyartacomprehensivehealthcareclinicinnairobikenya
AT mwalilisamuel modellingtrendsofcd4countsforpatientsonantiretroviraltherapyartacomprehensivehealthcareclinicinnairobikenya
AT awoketadesse modellingtrendsofcd4countsforpatientsonantiretroviraltherapyartacomprehensivehealthcareclinicinnairobikenya
AT braekersroel modellingtrendsofcd4countsforpatientsonantiretroviraltherapyartacomprehensivehealthcareclinicinnairobikenya
AT wandededolphine modellingtrendsofcd4countsforpatientsonantiretroviraltherapyartacomprehensivehealthcareclinicinnairobikenya
AT mwacharichristina modellingtrendsofcd4countsforpatientsonantiretroviraltherapyartacomprehensivehealthcareclinicinnairobikenya