Impact of Age on CD4 Response to  Combination Antiretroviral Therapy:  A Study in Tehran, Iran

Hasibi, Mehrdad; Hajiabdolbaghi, Mahboubeh; Hamzelou, Shahin; Sardashti, Sara; Foroughi, Maryam; Jozani, Zahra Bayat; SeyedAlinaghi, SeyedAhmad

doi:10.4236/wja.2014.42020

Cited by 5 publications

(8 citation statements)

References 12 publications

(15 reference statements)

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“…The younger people below the age of 40 have higher chances of immune recovery than the older ones. This finding is supported by some previous studies who concluded lower mean CD4 increases for older patients than younger patients [ 20 , 26 ]. Alioum and others further argued that this could be caused by the fact that older subjects may have a reduced capacity to generate CD4 cells in response to the viral killing [ 10 ].…”

Section: Discussionsupporting

confidence: 90%

Time-homogeneous Markov process for HIV/AIDS progression under a combination treatment therapy: cohort study, South Africa

Shoko

Chikobvu

2018

Theor Biol Med Model

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BackgroundAs HIV enters the human body, its main target is the CD4 cell which it turns into a factory that produces millions of other HIV particles. These HIV particles target new CD4 cells resulting in the progression of HIV infection to AIDS. A continuous depletion of CD4 cells results in opportunistic infections, for example tuberculosis (TB). The purpose of this study is to model and describe the progression of HIV/AIDS disease in an individual on antiretroviral therapy (ART) follow up using a continuous time homogeneous Markov process. A cohort of 319 HIV infected patients on ART follow up at a Wellness Clinic in Bela Bela, South Africa is used in this study. Though Markov models based on CD4 cell counts is a common approach in HIV/AIDS modelling, this paper is unique clinically in that tuberculosis (TB) co-infection is included as a covariate.MethodsThe method partitions the HIV infection period into five CD4-cell count intervals followed by the end points; death, and withdrawal from study. The effectiveness of treatment is analysed by comparing the forward transitions with the backward transitions. The effects of reaction to treatment, TB co-infection, gender and age on the transition rates are also examined. The developed models give very good fit to the data.ResultsThe results show that the strongest predictor of transition from a state of CD4 cell count greater than 750 to a state of CD4 between 500 and 750 is a negative reaction to drug therapy. Development of TB during the course of treatment is the greatest predictor of transitions to states of lower CD4 cell count. Transitions from good states to bad states are higher on male patients than their female counterparts. Patients in the cohort spend a greater proportion of their total follow-up time in higher CD4 states.ConclusionFrom some of these findings we conclude that there is need to monitor adverse reaction to drugs more frequently, screen HIV/AIDS patients for any signs and symptoms of TB and check for factors that may explain gender differences further.

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Section: Discussionsupporting

confidence: 90%

Time-homogeneous Markov process for HIV/AIDS progression under a combination treatment therapy: cohort study, South Africa

Shoko

Chikobvu

2018

Theor Biol Med Model

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“…The results are corroborated by the findings from a study that was carried out in Tehran, Iran, which showed that mean CD4 cell count increments after initiation of antiretroviral therapy are lower on older patients (> = 50 years) [12]. Prior to the study by Hasib et al [12], a study from Greece showed higher magnitudes of absolute CD4 cell count among patients 50 years and older [13]. The results reveal that, although HIV/AIDS patients generally take longer to reach a normal CD4 cell count compared to the time taken to reach an undetectable viral load, patients below the age of 45 years have reduced risks of mortality once the CD4 cell count is normal.…”

Section: Discussionsupporting

confidence: 64%

“…Patients below the age of 45 years had lower rates of viral load suppression to undetectable levels but they had faster rates of immune recovery compared to the older patients in the cohort (above 45 years of age). The results are corroborated by the findings from a study that was carried out in Tehran, Iran, which showed that mean CD4 cell count increments after initiation of antiretroviral therapy are lower on older patients (> = 50 years) [12]. Prior to the study by Hasib et al [12], a study from Greece showed higher magnitudes of absolute CD4 cell count among patients 50 years and older [13].…”

Section: Discussionmentioning

confidence: 56%

A superiority of viral load over CD4 cell count when predicting mortality in HIV patients on therapy

Shoko

Chikobvu

2019

BMC Infect Dis

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Background CD4 cell count has been identified to be an essential component in monitoring HIV treatment outcome. However, CD4 cell count monitoring sometimes fails to predict virological failure resulting in unnecessary switch of treatment lines which causes drug resistance and limitations of treatment options. This study assesses the use of both viral load (HIV RNA) and CD4 cell count in the monitoring of HIV/AIDS progression. Methods Time-homogeneous Markov models were fitted, one on CD4 cell count monitoring and the other on HIV RNA monitoring. Effects of covariates; gender, age, CD4 baseline, HIV RNA baseline and adherence to treatment were assessed for each of the fitted models. Assessment of the fitted models was done using prevalence plots and the likelihood ratio tests. The analysis was done using the “msm” package in R. Results Results from the analysis show that viral load monitoring predicts deaths of HIV/AIDS patients better than CD4 cell count monitoring. Assessment of the fitted models shows that viral load monitoring is a better predictor of HIV/AIDS progression than CD4 cell count. Conclusion From this study one can conclude that although patients take more time to achieve a normal CD4 cell count and less time to achieve an undetectable viral load, once the CD4 cell count is normal, mortality risks are reduced. Therefore, both viral load monitoring and CD4 count monitoring can be used to provide useful information which can be used to improve life expectance of patients living with HIV. However, viral load monitoring is a better predictor of HIV/AIDS progression than CD4 cell count and hence viral load is deemed superior. Electronic supplementary material The online version of this article (10.1186/s12879-019-3781-1) contains supplementary material, which is available to authorized users.

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“…Generally younger patients experienced higher rates of immune recovery than immune deterioration compared to patients who are over 45 y and this concurs with findings from previous studies [14]. This is in agreement with previous study carried out in Tehran, India that showed that mean CD4 cell count increments after initiation of combination therapy are lower on older patients [15].…”

Section: Assessment Of the Fitted Modelssupporting

confidence: 92%

“…Lee et al [11] investigated the most vulnerable racial minority races (African Americans) in the United States and the Caucasians in order to predict the trends of the HIV/AIDS ISSN 0970-938X www.biomedres.info Biomedical Research 2018; 29 (15): 3090-3098 epidemic using a Markov chain analysis. They predicted from these races, the number of people living with HIV, and mortality count due to HIV/AIDS.…”

Section: Introductionmentioning

confidence: 99%

A Markov model to estimate mortality due to HIV/AIDS using CD4 cell counts based states and viral load: a principal component analysis approach

Chikobvu¹,

Shoko²

2018

biomedicalresearch

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Background: Improvement of health in HIV/AIDS patients on Highly Active Antiretroviral Therapy (HAART) is characterised by an increase in CD4 cell counts and a decrease in viral load to undetectable levels. In modelling HIV/AIDS progression in patients, researchers mostly deal with either viral load only or CD4 cell counts only as they expect these two variables to be collinear. Methods: In this study, a cohort of 320 HIV/AIDS patients under HAART follow-up from a wellness clinic in Bela-Bela, South Africa is used. A time homogeneous Markov model is developed to explain and predict probability of death from HIV/AIDS. Principal component variables are created by fitting a regression model of viral load on CD4 cell counts. Results: Inclusion of a viral load principal component improves the efficiency of the model. The new viral load covariate helps to explain the component of mortality/transition, which could not be explained by the CD4 cell counts alone. CD4 cell counts are categorised to define the states for the Markov model. Results show that the expected percentage prevalence gives almost a perfect fit of the observed data. Conclusion: The orthogonal viral load covariate along with CD4 baseline, gender, non-adherence to treatment and age in years (y) variables play a significant role in modelling HIV/AIDS progression based on both CD4 cell counts and viral load monitoring.

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Impact of Age on CD4 Response to Combination Antiretroviral Therapy: A Study in Tehran, Iran

Cited by 5 publications

References 12 publications

Time-homogeneous Markov process for HIV/AIDS progression under a combination treatment therapy: cohort study, South Africa

Time-homogeneous Markov process for HIV/AIDS progression under a combination treatment therapy: cohort study, South Africa

A superiority of viral load over CD4 cell count when predicting mortality in HIV patients on therapy

A Markov model to estimate mortality due to HIV/AIDS using CD4 cell counts based states and viral load: a principal component analysis approach

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