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

Abstract 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 o...

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Main Authors: Claris Shoko, Delson Chikobvu
Format: Article
Language:English
Published: BMC 2019-02-01
Series:BMC Infectious Diseases
Subjects:
Online Access:http://link.springer.com/article/10.1186/s12879-019-3781-1
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spelling doaj-31cbed51f6de473a925fb201227d3cc42020-11-25T03:29:32ZengBMCBMC Infectious Diseases1471-23342019-02-0119111010.1186/s12879-019-3781-1A superiority of viral load over CD4 cell count when predicting mortality in HIV patients on therapyClaris Shoko0Delson Chikobvu1Department of Mathematical Statistics and Actuarial Sciences, University of the Free StateDepartment of Mathematical Statistics and Actuarial Sciences, University of the Free StateAbstract 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.http://link.springer.com/article/10.1186/s12879-019-3781-1CD4 cell countViral loadHIV/AIDS progressionMultistate modelling
collection DOAJ
language English
format Article
sources DOAJ
author Claris Shoko
Delson Chikobvu
spellingShingle Claris Shoko
Delson Chikobvu
A superiority of viral load over CD4 cell count when predicting mortality in HIV patients on therapy
BMC Infectious Diseases
CD4 cell count
Viral load
HIV/AIDS progression
Multistate modelling
author_facet Claris Shoko
Delson Chikobvu
author_sort Claris Shoko
title A superiority of viral load over CD4 cell count when predicting mortality in HIV patients on therapy
title_short A superiority of viral load over CD4 cell count when predicting mortality in HIV patients on therapy
title_full A superiority of viral load over CD4 cell count when predicting mortality in HIV patients on therapy
title_fullStr A superiority of viral load over CD4 cell count when predicting mortality in HIV patients on therapy
title_full_unstemmed A superiority of viral load over CD4 cell count when predicting mortality in HIV patients on therapy
title_sort superiority of viral load over cd4 cell count when predicting mortality in hiv patients on therapy
publisher BMC
series BMC Infectious Diseases
issn 1471-2334
publishDate 2019-02-01
description Abstract 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.
topic CD4 cell count
Viral load
HIV/AIDS progression
Multistate modelling
url http://link.springer.com/article/10.1186/s12879-019-3781-1
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