Shi Lingwei, Bi Dongsheng, Luo Jingchun, Chen Wei, Yang Cuiwei, Zheng Yan, Hao Ju, Chang Ke, Li Boyi, Liu Chengcheng, Ta Dean
Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, China.
Human Phenome Institute, Fudan University, Shanghai, China.
Front Physiol. 2022 Aug 8;13:976254. doi: 10.3389/fphys.2022.976254. eCollection 2022.
Electrocardiogram (ECG) and carotid ultrasound (CUS) are important tools for the diagnosis and prediction of cardiovascular disease (CVD). This study aimed to investigate the associations between ECG and CUS parameters and explore the feasibility of assessing carotid health with ECG. This cross-sectional cohort study enrolled 319 healthy Chinese subjects. Standard 12-lead ECG parameters (including the ST-segment amplitude [STA]), CUS parameters (intima-media thickness [IMT] and blood flow resistance index [RI]), and CVD risk factors (including sex, age, and systolic blood pressure [SBP]) were collected for analysis. Participants were divided into the high-level RI group (average RI ≥ 0.76, = 171) and the normal RI group (average RI < 0.76, = 148). Linear and stepwise multivariable regression models were performed to explore the associations between ECG and CUS parameters. Statistically significant differences in sex, age, SBP, STA and other ECG parameters were observed in the normal and the high-level RI group. The STA in lead V yielded stronger significant correlations (r = 0.27-0.42, < 0.001) with RI than STA in other leads, while ECG parameters yielded weak correlations with IMT (|r| ≤ 0.20, < 0.05). STA in lead V or V, sex, age, and SBP had independent contributions ( < 0.01) to predicting RI in the stepwise multivariable models, although the models for IMT had only CVD risk factors (age, body mass index, and triglyceride) as independent variables. The prediction model for RI in the left proximal common carotid artery (CCA) had higher adjusted R (adjusted R = 0.31) than the model for RI in the left middle CCA (adjusted R = 0.29) and the model for RI in the right proximal CCA (adjusted R = 0.20). In a cohort of healthy Chinese individuals, the STA was associated with the RI of CCA, which indicated that ECG could be utilized to assess carotid health. The utilization of ECG might contribute to a rapid screening of carotid health with convenient operations.
心电图(ECG)和颈动脉超声(CUS)是诊断和预测心血管疾病(CVD)的重要工具。本研究旨在调查ECG与CUS参数之间的关联,并探讨用ECG评估颈动脉健康状况的可行性。这项横断面队列研究纳入了319名健康的中国受试者。收集标准12导联心电图参数(包括ST段振幅[STA])、CUS参数(内膜中层厚度[IMT]和血流阻力指数[RI])以及CVD危险因素(包括性别、年龄和收缩压[SBP])进行分析。参与者被分为高RI组(平均RI≥0.76,n = 171)和正常RI组(平均RI<0.76,n = 148)。采用线性和逐步多变量回归模型来探讨ECG与CUS参数之间的关联。在正常组和高RI组中,观察到性别、年龄、SBP、STA和其他ECG参数存在统计学显著差异。V导联的STA与RI的相关性(r = 0.27 - 0.42,P<0.001)比其他导联的STA更强,而ECG参数与IMT的相关性较弱(|r|≤0.20,P<0.05)。在逐步多变量模型中,V或V导联的STA、性别、年龄和SBP对预测RI有独立贡献(P<0.01),尽管IMT模型仅将CVD危险因素(年龄、体重指数和甘油三酯)作为自变量。左颈总动脉(CCA)近端RI的预测模型的调整R²(调整R² = 0.31)高于左CCA中段RI的模型(调整R² = 0.29)和右CCA近端RI的模型(调整R² = 0.20)。在一组健康的中国个体中,STA与CCA的RI相关,这表明ECG可用于评估颈动脉健康状况。ECG的应用可能有助于通过便捷的操作快速筛查颈动脉健康状况。