Zelalem G Dessie1,2, Temesgen Zewotir3, Henry Mwambi3, Delia North3. 1. School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa. zelalem_getahune@yahoo.com. 2. College of Science, Bahir Dar University, Bahir Dar, Ethiopia. zelalem_getahune@yahoo.com. 3. School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa.
Abstract
BACKGROUND: 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. 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. METHODS: The 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. RESULTS: Viral 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. CONCLUSION: Overall, 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.
BACKGROUND: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. 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. METHODS: The data is from an ongoing prospective cohort study conducted among antiretroviral treatment (ART) naïve HIV-infectedwomen 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. RESULTS: Viral 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. CONCLUSION: Overall, 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.
Entities:
Keywords:
Factor analysis; Latent variables; Mixed effect model; Multistate model; Quality of life
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