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...
Autores principales: | , , , , , , |
---|---|
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 |