Modelling of viral load dynamics and CD4 cell count progression in an antiretroviral naive cohort: using a joint linear mixed and multistate Markov model

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.

Predictors of Current CD4+ T-Cell Count Among Women of Reproductive Age on Antiretroviral Therapy in Public Hospitals, Southwest Ethiopia

“…An increased current CD4 + T-cell count in response to ART was observed among women with educational status of certificate and above compared to illiterate women. Similarly, participants with a high educational level had good immunological recovery, and are less prone to comorbidities associated to HIV infection in the studies conducted in KwaZulu-Natal, South Africa, 48 and in India. 49 This might be due to the fact that education may help HIV positive women be aware of the importance of early identification of their status of HIV/AIDS.…”

Joint Modeling in Detecting Predictors of CD4 Cell Count and Status of Tuberculosis Among People Living with HIV/AIDS Under HAART at Felege Hiwot Teaching and Specialized Hospital, North-West Ethiopia

“…The result was also consistent with the previous research. 15 In joint analysis, AIDS clinical stage, weight, and hemoglobin level were significantly positively associated with CD4 cell count but had a negative association with TB status. In addition, one-way interaction terms (time * educational level) were also associated with both outcomes, and consistent with the previous study.…”

Multilevel ordinal model for CD4 count trends in seroconversion among South Africa women

“…In this study, patients who seroconverted during the HIV uninfected stage of CAPRISA 002 and other CAPRISA 004 and 008 trials [19,20], were enrolled into the acute HIV Infection phase, and then followed-up during chronic infection and on cART. Further information about these ongoing prospective HIV cohort studies, including women's eligibility criteria, were reported in [18,21,22]. Finally, two hundred and nineteen (219) participants were included in the study.…”

“…The prognostic factors include (1) cART initiation, (2) demographics: age, marital status, educational level, and sex under the influence of alcohol; (3) OI: hypertension and tuberculosis; (4) clinical parameters: WBC components (neutrophils, lymphocyte count, monocytes, eosinophils count, and leucocyte count), Blood chemistry (sodium, chloride, calcium, ALT, AST, total protein and LDH), RBC parameters (Hb, RDW, MCH, MCV, MCHC, and hematocrit) (5) QoL domain scores. Detailed information about the above-mentioned covariates was given in [21,22,24].…”