Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montréal, Québec, Canada.
Department of Internal Medicine, University of Montreal Hospital, Montréal, Québec, Canada.
J Acquir Immune Defic Syndr. 2022 Sep 1;91(1):91-100. doi: 10.1097/QAI.0000000000003016.
There is a need for a specific atherosclerotic risk assessment for people living with HIV (PLWH).
A machine learning classification model was applied to PLWH and control subjects with low-to-intermediate cardiovascular risks to identify associative predictors of diagnosed carotid artery plaques. Associations with plaques were made using strain elastography in normal sections of the common carotid artery and traditional cardiovascular risk factors.
One hundred two PLWH and 84 control subjects were recruited from the prospective Canadian HIV and Aging Cohort Study (57 ± 8 years; 159 men). Plaque presence was based on clinical ultrasound scans of left and right common carotid arteries and internal carotid arteries. A classification task for identifying subjects with plaque was defined using random forest (RF) and logistic regression models. Areas under the receiver operating characteristic curves (AUC-ROCs) were applied to select 5 among 50 combinations of 4 or less features yielding the highest AUC-ROCs.
To retrospectively classify individuals with and without plaques, the 5 most discriminant combinations of features had AUC-ROCs between 0.76 and 0.79. AUC-ROCs from RF were statistically significantly higher than those obtained with logistic regressions ( P = 0.0001). The most discriminant features of RF classifications in PLWH were age, smoking status, maximum axial strain and pulse pressure (equal weights), and sex and antiretroviral therapy exposure (equal weights). When considering the whole population, the HIV status was identified as a cofactor associated with carotid artery plaques.
Strain elastography adds to traditional cardiovascular risk factors for identifying individuals with carotid artery plaques.
需要为艾滋病毒感染者（PLWH）制定特定的动脉粥样硬化风险评估方法。
将机器学习分类模型应用于低至中度心血管风险的 PLWH 和对照受试者，以识别颈动脉斑块的关联预测因子。使用应变弹性成像在颈总动脉的正常节段和传统心血管危险因素来评估斑块与斑块的相关性。
从前瞻性加拿大艾滋病毒和老龄化队列研究（57±8 岁；159 名男性）中招募了 102 名 PLWH 和 84 名对照受试者。斑块的存在是基于左、右颈总动脉和颈内动脉的临床超声扫描。使用随机森林（RF）和逻辑回归模型定义了一个识别有斑块的受试者的分类任务。应用接收者操作特征曲线下面积（AUC-ROC）来选择 50 个特征中 4 个或更少特征的 5 个最佳组合，这些组合的 AUC-ROC 最高。
为了回顾性地对有斑块和无斑块的个体进行分类，5 个最佳特征组合的 AUC-ROC 在 0.76 至 0.79 之间。RF 的 AUC-ROC 明显高于逻辑回归（P=0.0001）。RF 分类中最具鉴别力的特征是年龄、吸烟状况、最大轴向应变和脉压（等权重）以及性别和抗逆转录病毒治疗暴露（等权重）。在考虑整个人群时，艾滋病毒状态被确定为与颈动脉斑块相关的一个伴随因素。
应变弹性成像增加了传统心血管危险因素，可用于识别颈动脉斑块患者。
