Abstract
BackgroundPatients infected with HIV may experience a succession of clinical stages before the disease diagnosis and their health status may be followed-up by tracking disease biomarkers. In this study, we present a joint multistate model for predicting the clinical progression of HIV infection which takes into account the viral load and CD4 count biomarkers.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. We presented a joint model that consists of two related submodels: a Markov multistate model for CD4 cell count transitions and a linear mixed effect model for longitudinal viral load dynamics.ResultsViral load dynamics significantly affect the transition intensities of HIV/AIDS disease progression. The analysis also showed that patients with relatively high educational levels (β = − 0.004; 95% confidence interval [CI]:-0.207, − 0.064), high RBC indices scores (β = − 0.01; 95%CI:-0.017, − 0.002) and high physical health scores (β = − 0.001; 95%CI:-0.026, − 0.003) were significantly were associated with a lower rate of viral load increase over time. Patients with TB co-infection (β = 0.002; 95%CI:0.001, 0.004), having many sex partners (β = 0.007; 95%CI:0.003, 0.011), being younger age (β = 0.008; 95%CI:0.003, 0.012) and high liver abnormality scores (β = 0.004; 95%CI:0.001, 0.01) were associated with a higher rate of viral load increase over time. Moreover, patients with many sex partners (β = − 0.61; 95%CI:-0.94, − 0.28) and with a high liver abnormality score (β = − 0.17; 95%CI:-0.30, − 0.05) showed significantly reduced intensities of immunological recovery transitions. Furthermore, a high weight, high education levels, high QoL scores, high RBC parameters and being of middle age significantly increased the intensities of immunological recovery transitions.ConclusionOverall, from a clinical perspective, QoL measurement items, being of a younger age, clinical attributes, marital status, and educational status are associated with the current state of the patient, and are an important contributing factor to extend survival of the patients and guide clinical interventions. From a methodological perspective, it can be concluded that a joint multistate model approach provides wide-ranging information about the progression and assists to provide specific dynamic predictions and increasingly precise knowledge of diseases.
Highlights
Patients infected with Human Immunodeficiency Virus (HIV) may experience a succession of clinical stages before the disease diagnosis and their health status may be followed-up by tracking disease biomarkers
Association parameters As one of our objectives is to examine the relationship between transient states defined by CD4 cell count progression and the longitudinal viral load dynamics, we followed the approach adopted by Ferrer et al [36], where the two sub-models are linked by Wmj(b, t)
In this article the joint model for multistate CD4 cell count progression and longitudinal viral load outcomes provides a complete model of HIV/Acquired Immune Deficiency Syndrome (AIDS) disease progression in an antiretroviral treatment (ART)-naive cohort, which takes into account longitudinal viral load dynamics, to study possible factors that affect time to transition between sequential adverse events of HIV/AIDS
Summary
Patients infected with HIV may experience a succession of clinical stages before the disease diagnosis and their health status may be followed-up by tracking disease biomarkers. We present a joint multistate model for predicting the clinical progression of HIV infection which takes into account the viral load and CD4 count biomarkers. AIDS, the last progress stage of HIV infection, leads to severe damage to the body’s immune system [3]. CD4 cell and viral load counts have remained the two strongest correlates and surrogate markers of HIV disease progression regularly used in the clinical setting to monitor the infection [5]. Some studies argue that CD4 cell count predicts clinical information (event time data) [7] whereas HIV viral load trajectories largely determine the time from initial infection to AIDS: high initial viral load is a marker for rapid progression [8]. Many studies report that there is a relationship between these biomarkers, often explaining the disease progression of one biomarker according to the other [9,10,11]
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