Abstract
BackgroundCD4 cell and viral load count are highly correlated surrogate markers of human immunodeficiency virus (HIV) disease progression. In modelling the progression of HIV, previous studies mostly dealt with either CD4 cell counts or viral load alone. In this work, both biomarkers are in included one model, in order to study possible factors that affect the intensities of immune deterioration, immune recovery and state-specific duration of HIV-infected women.MethodsThe data is from an ongoing prospective cohort study conducted among antiretroviral treatment (ART) naïve HIV-infected women in the province of KwaZulu-Natal, South Africa. Participants were enrolled in the acute HIV infection phase, then followed-up during chronic infection up to ART initiation. Full-parametric and semi-parametric Markov models were applied. Furthermore, the effect of the inclusion and exclusion viral load in the model was assessed.ResultsInclusion of a viral load component improves the efficiency of the model. The analysis results showed that patients who reported a stable sexual partner, having a higher educational level, higher physical health score and having a high mononuclear component score are more likely to spend more time in a good HIV state (particularly normal disease state). Patients with TB co-infection, with anemia, having a high liver abnormality score and patients who reported many sexual partners, had a significant increase in the intensities of immunological deterioration transitions. On the other hand, having high weight, higher education level, higher quality of life score, having high RBC parameters, high granulocyte component scores and high mononuclear component scores, significantly increased the intensities of immunological recovery transitions.ConclusionInclusion of both CD4 cell count based disease progression states and viral load, in the time-homogeneous Markov model, assisted in modeling the complete disease progression of HIV/AIDS. Higher quality of life (QoL) domain scores, good clinical characteristics, stable sexual partner and higher educational level were found to be predictive factors for transition and length of stay in sequential adversity of HIV/AIDS.
Highlights
CD4 cell and viral load count are highly correlated surrogate markers of human immunodeficiency virus (HIV) disease progression
The overall goodness of fit of the models is presented in Fig. 4, which shows the estimates of full and semi-parametric models to be overlaid on the nonparametric Aalen-Johansen estimates
Among the different hematological parameters for HIV infected patients, as expected, a latent variable related to basophils counts and total lymphocytes was significantly associated with the intensities of immunological deterioration transitions, and this confirms that HIV infection targets T-cells
Summary
CD4 cell and viral load count are highly correlated surrogate markers of human immunodeficiency virus (HIV) disease progression. The main markers of HIV disease progression are both viral load and CD4 count, relatively few HIV/AIDS disease progression modelling studies include the longitudinal measurements of viral load biomarker along with clinical states of HIV/AIDS disease progression [6, 7]. This might be due to the unavailability of viral load data, from middle and low-income countries, because of the higher costs of viral load testing [8]. Researchers further argue that modeling of disease progression of HIV/AIDS is important: to understand HIV pathogenesis and in the development of treatment strategies [9]; to refine the management of treatment-naive patients [10]; to determine at what threshold it is most clinically effective and less costly to begin ART [10]; to improve the empirical basis for epidemiological and prognostic models of the impact and cost-effectiveness of ART [11]
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