Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang City, Liaoning, Province, 110819, China.
Department of Cardiology, the First Hospital of China Medical University, Shenyang City, Liaoning Province, 110001, China.
Sci Rep. 2017 Jul 19;7(1):5864. doi: 10.1038/s41598-017-06094-2.
Arterial stiffness is an important risk factor for cardiovascular events. Radial augmentation index (AI ) can be more conveniently measured compared with carotid-femoral pulse wave velocity (cfPWV). However, the performance of AI in assessing arterial stiffness is limited. This study proposes a novel index AI , a combination of AI and diastolic augmentation index (AI ) with a weight α, to achieve better performance over AI in assessing arterial stiffness. 120 subjects (43 ± 21 years old) were enrolled. The best-fit α is determined by the best correlation coefficient between AI and cfPWV. The performance of the method was tested using the 12-fold cross validation method. AI (r = 0.68, P < 0.001) shows a stronger correlation with cfPWV and a narrower prediction interval than AI (r = 0.61, P < 0.001), AI (r = -0.17, P = 0.06), the central augmentation index (AI ) (r = 0.61, P < 0.001) or AI normalized for heart rate of 75 bpm (r = 0.65, P < 0.001). Compared with AI (age, P < 0.001; gender, P < 0.001; heart rate, P < 0.001; diastolic blood pressure, P < 0.001; weight, P = 0.001), AI has fewer confounding factors (age, P < 0.001; gender, P < 0.001). In conclusion, AI derives performance improvement in assessing arterial stiffness, with a stronger correlation with cfPWV and fewer confounding factors.
动脉僵硬度是心血管事件的一个重要危险因素。与颈-股脉搏波速度(cfPWV)相比,桡动脉增强指数(AI)更便于测量。然而,AI 在评估动脉僵硬度方面的性能有限。本研究提出了一种新的指数 AI,它是 AI 与舒张期增强指数(AI)的组合,具有权重α,以实现比 AI 更好的评估动脉僵硬度的性能。共纳入 120 例受试者(43±21 岁)。通过 AI 与 cfPWV 之间最佳相关系数来确定最佳拟合α。使用 12 折交叉验证方法测试该方法的性能。AI(r=0.68,P<0.001)与 cfPWV 的相关性更强,预测区间更窄,优于 AI(r=0.61,P<0.001)、AI(r=-0.17,P=0.06)、中心增强指数(AI)(r=0.61,P<0.001)或心率为 75bpm 时 AI 归一化(r=0.65,P<0.001)。与 AI 相比(年龄,P<0.001;性别,P<0.001;心率,P<0.001;舒张压,P<0.001;体重,P=0.001),AI 的混杂因素更少(年龄,P<0.001;性别,P<0.001)。总之,AI 在评估动脉僵硬度方面的性能有所提高,与 cfPWV 的相关性更强,混杂因素更少。