Markov modelling of viral load adjusting for CD4 orthogonal variable and multivariate conditional autoregressive mapping of the HIV immunological outcomes among ART patients in Zimbabwe.

Q1 Mathematics
Zvifadzo Matsena Zingoni, Tobias F Chirwa, Jim Todd, Eustasius Musenge
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引用次数: 1

Abstract

Background: This study aimed to jointly model HIV disease progression patterns based on viral load (VL) among adult ART patients adjusting for the time-varying "incremental transients states" variable, and the CD4 cell counts orthogonal variable in a single 5-stage time-homogenous multistate Markov model. We further jointly mapped the relative risks of HIV disease progression outcomes (detectable VL (VL ≥ 50copies/uL) and immune deterioration (CD4 < 350cells/uL) at the last observed visit) conditional not to have died or become loss to follow-up (LTFU).

Methods: Secondary data analysis of individual-level patients on ART was performed. Adjusted transition intensities, hazard ratios (HR) and regression coefficients were estimated from the joint multistate model of VL and CD4 cell counts. The mortality and LTFU transition rates defined the extent of patients' retention in care. Joint mapping of HIV disease progression outcomes after ART initiation was done using the Bayesian intrinsic Multivariate Conditional Autoregressive prior model.

Results: The viral rebound from the undetectable state was 1.78times more likely compared to viral suppression among patients with VL ranging from 50-1000copies/uL. Patients with CD4 cell counts lower than expected had a higher risk of viral increase above 1000copies/uL and death if their VL was above 1000copies/uL (state 2 to 3 (λ23): HR = 1.83 and (λ34): HR = 1.42 respectively). Regarding the time-varying effects of CD4 cell counts on the VL transition rates, as the VL increased, (λ12 and λ23) the transition rates increased with a decrease in the CD4 cell counts over time. Regardless of the individual's VL, the transition rates to become LTFU decreased with a decrease in CD4 cell counts. We observed a strong shared geographical pattern of 66% spatial correlation between the relative risks of detectable VL and immune deterioration after ART initiation, mainly in Matabeleland North.

Conclusion: With high rates of viral rebound, interventions which encourage ART adherence and continual educational support on the barriers to ART uptake are crucial to achieve and sustain viral suppression to undetectable levels. Area-specific interventions which focus on early ART screening through self-testing, behavioural change campaigns and social support strategies should be strengthened in heavily burdened regions to sustain the undetectable VL. Sustaining undetectable VL lowers HIV transmission in the general population and this is a step towards achieving zero HIV incidences by 2030.

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病毒载量调节CD4的马尔可夫模型正交变量和津巴布韦抗逆转录病毒治疗患者HIV免疫结果的多变量条件自回归映射
背景:本研究旨在共同模拟成人ART患者中基于病毒载量(VL)的HIV疾病进展模式,调整时变的“增量瞬态”变量和CD4细胞计数正交变量,在单一的5期时间同质多状态马尔可夫模型中。我们进一步联合绘制HIV疾病进展结局(可检测VL (VL≥50拷贝/uL))和免疫恶化(CD4)的相对风险图。方法:对接受抗逆转录病毒治疗的个体水平患者进行二次数据分析。根据VL和CD4细胞计数的联合多状态模型估计调整后的转移强度、风险比(HR)和回归系数。死亡率和LTFU转换率决定了患者在护理中的保留程度。使用贝叶斯内禀多变量条件自回归先验模型对抗逆转录病毒治疗开始后HIV疾病进展结果进行联合映射。结果:在VL范围为50-1000拷贝/uL的患者中,病毒从不可检测状态反弹的可能性是病毒抑制的1.78倍。CD4细胞计数低于预期的患者,如果其VL高于1000拷贝/uL,则病毒增加超过1000拷贝/uL的风险更高(状态2至3 (λ23): HR = 1.83和(λ34): HR = 1.42)。关于CD4细胞计数对VL转换率的时变效应,随着VL的增加,(λ12和λ23)转换率随着CD4细胞计数的减少而增加。无论个体的VL如何,转化为LTFU的速率随着CD4细胞计数的减少而降低。我们观察到,在抗逆转录病毒治疗开始后,可检测到的VL的相对风险与免疫退化之间存在66%的空间相关性,主要是在马塔贝莱兰北部。结论:在病毒反弹率很高的情况下,鼓励坚持抗逆转录病毒治疗的干预措施和对抗逆转录病毒治疗障碍的持续教育支持对于实现和维持病毒抑制到不可检测的水平至关重要。应在负担沉重的地区加强针对特定地区的干预措施,重点是通过自检进行早期抗逆转录病毒治疗筛查、行为改变运动和社会支持战略,以维持无法检测到的VL。维持无法检测到的VL可降低艾滋病毒在普通人群中的传播,这是到2030年实现艾滋病毒零发病率的一步。
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来源期刊
Theoretical Biology and Medical Modelling
Theoretical Biology and Medical Modelling MATHEMATICAL & COMPUTATIONAL BIOLOGY-
自引率
0.00%
发文量
0
审稿时长
6-12 weeks
期刊介绍: Theoretical Biology and Medical Modelling is an open access peer-reviewed journal adopting a broad definition of "biology" and focusing on theoretical ideas and models associated with developments in biology and medicine. Mathematicians, biologists and clinicians of various specialisms, philosophers and historians of science are all contributing to the emergence of novel concepts in an age of systems biology, bioinformatics and computer modelling. This is the field in which Theoretical Biology and Medical Modelling operates. We welcome submissions that are technically sound and offering either improved understanding in biology and medicine or progress in theory or method.
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