Gandla Sindhuri, Nakka Raja, Khan Ruhul Ali, Bose Eliezer, Ghebremichael Musie
Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139, USA.
Department of Mathematics, University of Arizona, Tucson, AZ 85721, USA.
Trop Med Infect Dis. 2025 Jan 16;10(1):24. doi: 10.3390/tropicalmed10010024.
HIV remains a significant health issue, especially in sub-Saharan Africa. There are 39 million people living with HIV (PLWH) globally. Treatment with ART improves patient outcomes by suppressing the HIV RNA viral load. However, not all patients treated with ART suppress the HIV RNA viral load. This research paper explores the potential predictors of VL suppression in ART-treated PLWH. We used retrospective data from the 4820 ART-treated participants enrolled through population-based surveys conducted in Zambia and Malawi. We applied several machine learning (ML) classifiers and used the top classifiers to identify the predictors of VL suppression. The age of participants ranged from 15 to 64 years, with a majority being females. The predictive performance of the various ML classifiers ranged from 64% to 92%. In our data from both countries, the logistic classifier was among the top classifiers and was as follows: Malawi (AUC = 0.9255) and Zambia (AUC = 0.8095). Thus, logistic regression was used to identify the predictors of viral suppression. Our findings indicated that besides ART treatment status, older age, higher CD4 T-cell count, and longer duration of ART were identified as significant predictors of viral suppression. Though not statistically significant, ART initiation 12 months or more before the survey, urban residence, and wealth index were also associated with VL suppression. Our findings indicate that HIV prevention programs in the region should integrate education on early ART initiation and adherence in PLWH.
艾滋病毒仍然是一个重大的健康问题，尤其是在撒哈拉以南非洲地区。全球有3900万人感染艾滋病毒（PLWH）。抗逆转录病毒疗法（ART）治疗通过抑制艾滋病毒RNA病毒载量改善患者预后。然而，并非所有接受ART治疗的患者都能抑制艾滋病毒RNA病毒载量。这篇研究论文探讨了接受ART治疗的PLWH中病毒载量抑制的潜在预测因素。我们使用了通过在赞比亚和马拉维进行的基于人群的调查招募的4820名接受ART治疗参与者的回顾性数据。我们应用了几种机器学习（ML）分类器，并使用顶级分类器来确定病毒载量抑制的预测因素。参与者年龄在15岁至64岁之间，大多数为女性。各种ML分类器的预测性能在64%至92%之间。在我们来自两国的数据中，逻辑回归分类器是顶级分类器之一，具体如下：马拉维（AUC = 0.9255）和赞比亚（AUC = 0.8095）。因此，使用逻辑回归来确定病毒抑制的预测因素。我们的研究结果表明，除了ART治疗状态外，年龄较大、CD4 T细胞计数较高以及ART治疗时间较长被确定为病毒抑制的重要预测因素。虽然在统计学上不显著，但在调查前12个月或更长时间开始接受ART治疗、城市居住和财富指数也与病毒载量抑制有关。我们的研究结果表明，该地区的艾滋病毒预防计划应将对PLWH早期开始接受ART治疗和坚持治疗的教育纳入其中。
