Modeling Viral Suppression, Viral Rebound and State-Specific Duration of HIV Patients with CD4 Count Adjustment: Parametric Multistate Frailty Model Approach

Abstract

Introduction: Combination antiretroviral therapy has become the standard care of human immunodeficiency virus (HIV)-infected patients and has further led to a dramatically decreased progression probability to acquired immune deficiency syndrome (AIDS) for patients under such a therapy. However, responses of the patients to this therapy have recorded heterogeneous complexity and high dynamism. In this paper, we simultaneously model long-term viral suppression, viral rebound, and statespecific duration of HIV-infected patients. Methods: Full-parametric and semi-parametric Markov multistate models were applied to assess the effects of covariates namely TB co-infection, educational status, marital status, age, quality of life (QoL) scores, white and red blood cell parameters, and liver enzyme abnormality on long-term viral suppression, viral rebound and state-specific duration for HIV-infected individuals before and after treatment. Furthermore, two models, one including and another excluding the effect of the frailty, were presented and compared in this study. Results: Results from the diagnostic plots, Akaike information criterion (AIC) and likelihood ratio test showed that the Weibull multistate frailty model fitted significantly better than the exponential and semi-parametric multistate models. Viral rebound was found to be significantly associated with many sex partners, higher eosinophils count, younger age, lower educational level, higher monocyte counts, having abnormal neutrophils count, and higher liver enzyme abnormality. Furthermore, viral suppression was also found to be significantly associated with higher QoL scores, and having a stable sex partner. The analysis result also showed that patients with a stable sex partner, higher educational levels, higher QoL scores, lower eosinophils count, lower monocyte counts, and higher RBC indices were more likely to spend more time in undetectable viral load state. Conclusions: To achieve and maintain the UNAIDS 90% suppression targets, additional interventions are required to optimize antiretroviral therapy outcomes, specifically targeting those with poor clinical characteristics, lower education, younger age, and those with many sex partners. From a methodological perspective, the parametric multistate approach Digital Features To view digital features for this article go to

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“…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].…”