Department of Mathematical & Statistical Sciences.
Department of Public Health Sciences.
AIDS. 2021 Sep 1;35(11):1785-1793. doi: 10.1097/QAD.0000000000002955.
Peripheral neuropathies (PNPs) in HIV-infected patients are highly debilitating because of neuropathic pain and physical disabilities. We defined prevalence and associated predictive variables for PNP subtypes in a cohort of persons living with HIV.
Adult persons living with HIV in clinical care were recruited to a longitudinal study examining neurological complications.
Each patient was assessed for symptoms and signs of PNP with demographic, laboratory, and clinical variables. Univariate, multiple logistic regression and machine learning analyses were performed by comparing patients with and without PNP.
Three patient groups were identified: PNP (n = 111) that included HIV-associated distal sensory polyneuropathy (n = 90) or mononeuropathy (n = 21), and non-neuropathy (n = 408). Univariate analyses showed multiple variables differed significantly between the non-neuropathy and PNP groups including age, estimated HIV type 1 (HIV-1) duration, education, employment, neuropathic pain, peak viral load, polypharmacy, diabetes, cardiovascular disorders, AIDS, and prior neurotoxic nucleoside antiretroviral drug exposure. Classification algorithms distinguished those with PNP, all with area under the receiver operating characteristic curve values of more than 0.80. Random forest models showed greater accuracy and area under the receiver operating characteristic curve values compared with the multiple logistic regression analysis. Relative importance plots showed that the foremost predictive variables of PNP were HIV-1 duration, peak plasma viral load, age, and low CD4+ T-cell levels.
PNP in HIV-1 infection remains common affecting 21.4% of patients in care. Machine-learning models uncovered variables related to PNP that were undetected by conventional analyses, emphasizing the importance of statistical algorithmic approaches to understanding complex neurological syndromes.
HIV 感染者的周围神经病变（PNP）会导致严重的神经病理性疼痛和身体残疾，极大地削弱患者的身体机能。我们定义了 PNP 亚型的患病率和相关预测变量，并在一组 HIV 感染者中进行了研究。
我们招募了正在接受临床护理的 HIV 感染者，开展一项纵向研究，以评估他们的神经并发症。
对每位患者进行 PNP 的症状和体征评估，并记录人口统计学、实验室和临床变量。通过比较有和无 PNP 的患者，我们进行了单变量、多变量逻辑回归和机器学习分析。
我们确定了三组患者：PNP（n=111），包括 HIV 相关的远端感觉性多发性神经病（n=90）或单神经病（n=21）和无神经病（n=408）。单变量分析显示，无神经病和 PNP 组之间有多个变量差异显著，包括年龄、估计的 HIV-1 持续时间、教育程度、就业、神经病理性疼痛、峰值病毒载量、多药治疗、糖尿病、心血管疾病、艾滋病和先前神经毒性核苷类抗逆转录病毒药物暴露。分类算法可以区分 PNP 患者，所有患者的受试者工作特征曲线下面积值均大于 0.80。随机森林模型显示出比多变量逻辑回归分析更高的准确性和受试者工作特征曲线下面积值。相对重要性图显示，预测 PNP 的最重要变量是 HIV-1 持续时间、峰值血浆病毒载量、年龄和低 CD4+T 细胞水平。
HIV-1 感染中的 PNP 仍然很常见，影响了 21.4%的在治患者。机器学习模型揭示了与 PNP 相关的变量，这些变量是常规分析无法检测到的，这强调了统计算法方法在理解复杂神经综合征方面的重要性。
