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Modelling of viral load dynamics and CD4 cell count progression in an antiretroviral naive cohort: using a joint linear mixed and multistate Markov model.
BMC Infectious Diseases ( IF 3.7 ) Pub Date : 2020-03-26 , DOI: 10.1186/s12879-020-04972-1
Zelalem G Dessie 1, 2 , Temesgen Zewotir 1 , Henry Mwambi 1 , Delia North 1
Affiliation  

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. 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. 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. 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.

中文翻译:

在抗逆转录病毒天真的队列中模拟病毒负荷动力学和CD4细胞计数进程:使用联合线性混合和多状态马尔可夫模型。

感染艾滋病毒的患者可能在疾病诊断之前经历一系列临床阶段,并且可以通过跟踪疾病生物标记物来跟踪他们的健康状况。在这项研究中,我们提出了一个联合多状态模型来预测HIV感染的临床进展,其中考虑了病毒载量和CD4计数生物标志物。数据来自正在进行的一项前瞻性队列研究,该研究在南非夸祖鲁-纳塔尔省的一名接受抗逆转录病毒治疗(ART)的未感染HIV的女性中进行。我们提出了一个包含两个相关子模型的联合模型:一个用于CD4细胞计数转换的马尔可夫多状态模型和一个用于纵向病毒负荷动态的线性混合效应模型。病毒载量动态显着影响HIV / AIDS疾病进展的过渡强度。分析还显示,受教育水平较高的患者(β= − 0.004; 95%置信区间[CI]:-0.207,-0.064),RBC指数得分较高(β= − 0.01; 95%CI:-0.017,- 0.002)和较高的身体健康分数(β=-0.001; 95%CI:-0.026,-0.003)与病毒载量随时间增加的降低率显着相关。患有结核病合并感染的患者(β= 0.002; 95%CI:0.001,0.004),有很多性伴侣(β= 0.007; 95%CI:0.003,0.011),年龄较小(β= 0.008; 95%CI: 0.003,0.012)和较高的肝脏异常评分(β= 0.004; 95%CI:0.001,0.01)与病毒载量随时间增加的增加率相关。此外,患有许多性伴侣(β= − 0.61; 95%CI:-0.94,− 0.28)且肝异常评分较高(β= − 0.17; 95%CI:-0.30,− 0)的患者。05)显示免疫恢复转换的强度明显降低。此外,高体重,高学历,高QoL评分,高RBC参数和中年人显着增加了免疫恢复转变的强度。总体而言,从临床角度来看,较年轻的QoL测量项目,临床属性,婚姻状况和教育状况与患者的当前状态相关,并且是延长患者生存和指导的重要因素临床干预。从方法学的角度来看,可以得出结论,联合多状态模型方法可提供有关进展的广泛信息,并有助于提供特定的动态预测和对疾病的日益精确的了解。
更新日期:2020-04-22
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