Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, China.
Department of Laboratory Medicine, Yunnan Provincial Institute of Infectious Diseases, Kunming, China.
Front Cell Infect Microbiol. 2022 May 12;12:867737. doi: 10.3389/fcimb.2022.867737. eCollection 2022.
To investigate trends in clinical monitoring indices in HIV/AIDS patients receiving antiretroviral therapy (ART) at baseline and after treatment in Yunnan Province, China and to provide the basis for guiding clinical treatment to obtain superior clinical outcomes.
A total of 96 HIV/AIDS patients who had started and persisted in highly active ART treatment from September 2009 to September 2019 were selected. Of these, 54 had a CD4 cell count < 200 cells/μl while 42 had a CD4 cell count ≥ 200 cells/μl. Routine blood tests, liver and renal function, and lipid levels were measured before and 3, 6, 9, and 12 months after treatment. Lymphocyte subset counts and viral load were measured once per year, and recorded for analysis and evaluation. Three machine learning models (support vector machine [SVM], random forest [RF], and multi-layer perceptron [MLP]) were constructed that used the clinical indicators above as parameters. Baseline and follow-up results of routine blood and organ function tests were used to analyze and predict CD4 T cell data after treatment during long-term follow-up. Predictions of the three models were preliminarily evaluated.
There were no statistical differences in gender, age, or HIV transmission route in either patient group. Married individuals were substantially more likely to have <200 CD4 cells/μl. There was a strong positive correlation between ALT and AST (r = 0.587) and a positive correlation between CD4 cell count and platelet count (r = 0.347). Platelet count was negatively correlated with ALT (r = -0.229), AST (r = -0.251), and positively correlated with WBCs (r = 0.280). Compared with the CD4 cell count < 200 cells/μl group, all three machine learning models exhibited a better predictive capability than for patients with a CD4 cell count ≥ 200 cells/μl. Of all indicators, the three models best predicted the CD4/CD8 ratio, with results that were highly consistent. In patients with a CD4 cell count < 200 cells/μl, the SVM model had the best performance for predicting the CD4/CD8 ratio, while the CD4/CD8 ratio was best predicted by the RF model in patients with a CD4 cell count ≥ 200 cells/μl.
By the incorporation of clinical indicators in SVM, RF, and MLP machine learning models, the immune function and recuperation of HIV/AIDS patients can be predicted and evaluated, thereby better guiding clinical treatment.
调查中国云南省接受抗逆转录病毒治疗（ART）的 HIV/AIDS 患者在基线和治疗后临床监测指标的变化趋势，为指导临床治疗获得更好的临床结果提供依据。
选取 2009 年 9 月至 2019 年 9 月期间开始并持续接受高效抗逆转录病毒治疗的 96 例 HIV/AIDS 患者，其中 54 例 CD4 细胞计数<200 个/μl，42 例 CD4 细胞计数≥200 个/μl。治疗前和治疗后 3、6、9 和 12 个月时检测血常规、肝肾功能和血脂水平。每年检测一次淋巴细胞亚群计数和病毒载量，并记录进行分析和评价。使用上述临床指标作为参数，构建支持向量机（SVM）、随机森林（RF）和多层感知机（MLP）三种机器学习模型。分析和预测长期随访中治疗后 CD4 T 细胞数据，使用基线和随访的常规血液和器官功能测试结果。初步评估了三种模型的预测结果。
两组患者在性别、年龄或 HIV 传播途径方面均无统计学差异。已婚者 CD4 细胞计数<200 个/μl 的可能性显著更高。ALT 和 AST 之间呈强正相关（r=0.587），CD4 细胞计数与血小板计数之间呈正相关（r=0.347）。血小板计数与 ALT（r=-0.229）、AST（r=-0.251）呈负相关，与白细胞计数（r=0.280）呈正相关。与 CD4 细胞计数<200 个/μl 组相比，所有三种机器学习模型对 CD4 细胞计数≥200 个/μl 患者的预测能力均优于传统模型。在所有指标中，三种模型对 CD4/CD8 比值的预测效果最佳，结果高度一致。在 CD4 细胞计数<200 个/μl 的患者中，SVM 模型对 CD4/CD8 比值的预测效果最佳，而在 CD4 细胞计数≥200 个/μl 的患者中，RF 模型对 CD4/CD8 比值的预测效果最佳。
通过将临床指标纳入 SVM、RF 和 MLP 机器学习模型中，可以预测和评估 HIV/AIDS 患者的免疫功能和恢复情况，从而更好地指导临床治疗。
Zotero 插件
