School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, Private Bag X01, Scottsville, 3209, South Africa.
Institute of Human Virology, School of Medicine, University of Maryland, Baltimore, MD, 21201, USA.
BMC Infect Dis. 2022 Jan 4;22(1):20. doi: 10.1186/s12879-021-06942-7.
BACKGROUND: The CD4 cell count signifies the health of an individual's immune system. The use of data-driven models enables clinicians to accurately interpret potential information, examine the progression of CD4 count, and deal with patient heterogeneity due to patient-specific effects. Quantile-based regression models can be used to illustrate the entire conditional distribution of an outcome and identify various covariates effects at the respective location. METHODS: This study uses the quantile mixed-effects model that assumes an asymmetric Laplace distribution for the error term. The model also incorporated multiple random effects to consider the correlation among observations. The exact maximum likelihood estimation was implemented using the Stochastic Approximation of the Expectation-Maximization algorithm to estimate the parameters. This study used the Centre of the AIDS Programme of Research in South Africa (CAPRISA) 002 Acute Infection Study data. In this study, the response variable is the longitudinal CD4 count from HIV-infected patients who were initiated on Highly Active Antiretroviral Therapy (HAART), and the explanatory variables are relevant baseline characteristics of the patients. RESULTS: The analysis obtained robust parameters estimates at various locations of the conditional distribution. For instance, our result showed that baseline BMI (at [Formula: see text] 0.05: [Formula: see text]), baseline viral load (at [Formula: see text] 0.05: [Formula: see text] [Formula: see text]), and post-HAART initiation (at [Formula: see text] 0.05: [Formula: see text]) were major significant factors of CD4 count across fitted quantiles. CONCLUSIONS: CD4 cell recovery in response to post-HAART initiation across all fitted quantile levels was observed. Compared to HIV-infected patients with low viral load levels at baseline, HIV-infected patients enrolled in the treatment with a high viral load level at baseline showed a significant negative effect on CD4 cell counts at upper quantiles. HIV-infected patients registered with high BMI at baseline had improved CD4 cell count after treatment, but physicians should not ignore this group of patients clinically. It is also crucial for physicians to closely monitor patients with a low BMI before and after starting HAART.
背景：CD4 细胞计数标志着个体免疫系统的健康状况。数据驱动模型的使用使临床医生能够准确地解释潜在信息，检查 CD4 计数的进展，并处理由于患者个体效应而导致的患者异质性。分位数回归模型可用于说明结果的整个条件分布，并确定各自位置的各种协变量效应。
方法：本研究使用假设误差项为不对称拉普拉斯分布的分位数混合效应模型。该模型还纳入了多个随机效应，以考虑观测之间的相关性。使用随机逼近期望最大化算法进行精确最大似然估计来估计参数。本研究使用南非艾滋病规划署研究中心（CAPRISA）002 急性感染研究数据。在本研究中，因变量是接受高效抗逆转录病毒治疗（HAART）的 HIV 感染者的纵向 CD4 计数，解释变量是患者的相关基线特征。
结果：分析获得了条件分布各个位置的稳健参数估计。例如，我们的结果表明，基线 BMI（在 [Formula: see text] 0.05：[Formula: see text]）、基线病毒载量（在 [Formula: see text] 0.05：[Formula: see text] [Formula: see text]）和 HAART 启动后（在 [Formula: see text] 0.05：[Formula: see text]）是所有拟合分位数中 CD4 计数的主要显著因素。
结论：在所有拟合分位数水平下，观察到 HAART 启动后对 CD4 细胞的恢复。与基线病毒载量水平低的 HIV 感染者相比，基线病毒载量水平高的 HIV 感染者在高分位数上对 CD4 细胞计数的影响具有显著的负向作用。基线 BMI 较高的 HIV 感染者在治疗后 CD4 细胞计数有所改善，但临床医生不应忽视这组患者。医生在开始 HAART 之前和之后密切监测 BMI 较低的患者也至关重要。
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