Continuous-time Markov modelling of the effects of treatment regimens on HIV/AIDS immunology and virology
| dc.contributor.advisor | Chikobvu, Delson | en_ZA |
| dc.contributor.author | Shoko, Claris | en_ZA |
| dc.date.accessioned | 2020-02-19T10:40:14Z | |
| dc.date.available | 2020-02-19T10:40:14Z | |
| dc.date.issued | 2019 | en_ZA |
| dc.description | Thesis (Ph.D.(Mathematical Statistics))--University of the Free State, 2019 | en_ZA |
| dc.description.abstract | As the Human immunodeficiency virus (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, thus compromising the immune system and resulting in opportunistic infections, for example tuberculosis (TB). Combination Anti-retroviral therapy (cART) has become the standard of care for patients with HIV infection and has led to the reduction in acquired immunodeficiency syndrome (AIDS) related morbidity and mortality, an increase in CD4+ cell counts and a decrease in viral load count to undetectable levels. In modelling HIV/AIDS progression in patients, researchers mostly deal with either viral load only or CD4+cellcountsonly, as they expect these two variables to be collinear. The purpose of this study is to fit a continuous-time Markov model that best describes mortality of HIV infected patients on cART by eventually including both CD4+ cell counts monitoring and viral load monitoring in a single model after treating for collinearity of these variables using the Principal Component approach. Acohortof320HIVinfectedpatientsoncARTfollowedupat a Wellness Clinic in Bela Bela, South Africa, is used in this thesis. These patients are administered with a triple therapy of two nucleoside reverse transcriptase inhibitor (NRTIs) and one non-nucleoside reverse transcriptase inhibitor (NNRTI). The thesis is divided into five sections. In the first section, a continuous-time homogeneous Markov model based on CD4+ cell count states is fitted. The model is used to analyse the effects of tuberculosis (TB) co-infection on the immunologic progression of HIV/AIDS patients on cART. TB co-infection was of interest because it is an opportunistic infection that takes advantage of the compromised immune system. Results from this section showed that once TB is diagnosed prior to treatment initiation and managed, mortality rates are reduced. However, if TB is diagnosed during the course of treatment, it increases the rates of immune deterioration in patients, leading to high rates of mortality. Therefore, this section proposes the need for routine TB screening before treatment initiation and a tevery stage of the follow up period, to avoid loss of lives. The goal of cART is not only to boost the immune system but also to suppress the viral load to undetectable levels. Thus, in the second section, a non-homogeneous continuous-time Markov model based on viral load states is fitted. This model helped in revealing possibilities of viral rebound among patient son cART. Although there were no significant gender differences on HIV/AIDS virology, the model explained the progression of patients better than the model based on CD4+ cell count fitted in the first section. In the third section, determinants of viral rebound are analysed. Viral rebound was notable mainly after patients had attained a viral load suppressed to the levels between 50 copies/mL and 10 000 copies/mL. The major attributes of viral rebound were non-adherence, lactic acid, resistance to treatment, and different combination therapy such as AZT-3TC-LPV/r and FTC-TDF-EFV. This section suggests the need to closely monitor HIV patients to ensure attainment of undetectable viral load (below 50 copies/mL) during the first six months of treatment uptake, as this reduces chances of viral rebound, leading to life gain by HIV/AIDS patients. The fourth section compares the use of viral load count and CD4+cell count in monitoring HIV/AIDS disease progression on patients receiving cART in order to establish the superiority of viral load over CD4+ cell count. This was done by fitting two separate models, one for CD4+ cell count states and the other one for viral load states. Comparison of the fitted models were based on percentage prevalence plots for the fitted model and for the observed data and likelihood ratio tests. The test confirmed that viral load monitoring is superior compared to CD4+cell count monitoring. Viral load monitoring is very good at detecting virologic failure, thereby avoiding unnecessary switches of treatment lines. However, this section suggests the use of both CD4+cellcount monitoring and viral load monitoring because CD4+ cell count monitoring helps in managing possibilities of the development of opportunistic infections. In the fifth section, continuous-time homogeneous Markov models are fitted, including both CD4+ cell count monitoring and viral load monitoring in one model. Since these variables are assumed to be collinear, principal component analysis was used to treat for the collinearity among these two variables. The models are fitted in such a way that when Markov states are based on CD4+ cell count, the principal component of viral load is included as a covariate, and when the Markov states are based on viral load, the principal component of CD4+cell count is included as a covariate. Results from the models show an improvement in the power of the continuous-time Markov model to explain and predict mortality when both CD4+cellcount and viral load routine monitoring are included in one model. | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11660/10404 | |
| dc.language.iso | en | en_ZA |
| dc.publisher | University of the Free State | en_ZA |
| dc.rights.holder | University of the Free State | en_ZA |
| dc.subject | HIV/AIDS progression | en_ZA |
| dc.subject | Virology | en_ZA |
| dc.subject | Immunology | en_ZA |
| dc.subject | Continuous-time Markov process | en_ZA |
| dc.subject | Principal component analysis | en_ZA |
| dc.subject | Viral rebound | en_ZA |
| dc.subject | Longitudinal data | en_ZA |
| dc.title | Continuous-time Markov modelling of the effects of treatment regimens on HIV/AIDS immunology and virology | en_ZA |
| dc.type | Thesis | en_ZA |