Jamthikar Ankush, Gupta Deep, Cuadrado-Godia Elisa, Puvvula Anudeep, Khanna Narendra N, Saba Luca, Viskovic Klaudija, Mavrogeni Sophie, Turk Monika, Laird John R, Pareek Gyan, Miner Martin, Sfikakis Petros P, Protogerou Athanasios, Kitas George D, Shankar Chithra, Nicolaides Andrew, Viswanathan Vijay, Sharma Aditya, Suri Jasjit S
Department of Electronics and Communications Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India.
Department of Neurology, IMIM - Hospital del Mar, Barcelona, Spain.
Cardiovasc Diagn Ther. 2020 Aug;10(4):939-954. doi: 10.21037/cdt.2020.01.16.
Vascular age (VA) has recently emerged for CVD risk assessment and can either be computed using conventional risk factors (CRF) or by using carotid intima-media thickness (cIMT) derived from carotid ultrasound (CUS). This study investigates a novel method of integrating both CRF and cIMT for estimating VA [so-called integrated VA (IVA)]. Further, the study analyzes and compares CVD/stroke risk using the Framingham Risk Score (FRS)-based risk calculator when adapting IVA against VA.
The system follows a four-step process: (I) VA using cIMT based using linear-regression (LR) model and its coefficients; (II) VA prediction using ten CRF using a multivariate linear regression (MLR)-based model with gender adjustment; (III) coefficients from the LR-based model and MLR-based model are combined using a linear model to predict the final IVA; (IV) the final step consists of FRS-based risk stratification with IVA as inputs and benchmarked against FRS using conventional method of CA. Area-under-the-curve (AUC) is computed using IVA and benchmarked against CA while taking the response variable as a standardized combination of cIMT and glycated hemoglobin.
The study recruited 648 patients, 202 were Japanese, 314 were Asian Indian, and 132 were Caucasians. Both left and right common carotid arteries (CCA) of all the population were scanned, thus a total of 1,287 ultrasound scans. The 10-year FRS using IVA reported higher AUC (AUC =0.78) compared with 10-year FRS using CA (AUC =0.66) by ~18%.
IVA is an efficient biomarker for risk stratifications for patients in routine practice.
血管年龄(VA)最近已用于心血管疾病(CVD)风险评估,可通过传统风险因素(CRF)计算得出,也可通过颈动脉超声(CUS)测量的颈动脉内膜中层厚度(cIMT)得出。本研究探讨了一种整合CRF和cIMT以估计VA的新方法[即所谓的整合血管年龄(IVA)]。此外,该研究在将IVA与VA进行对比时,使用基于弗雷明汉风险评分(FRS)的风险计算器分析并比较了CVD/中风风险。
该系统遵循四个步骤:(I)使用基于线性回归(LR)模型及其系数的cIMT计算VA;(II)使用基于多变量线性回归(MLR)且经性别调整的模型,通过十个CRF预测VA;(III)使用线性模型合并基于LR的模型和基于MLR的模型的系数,以预测最终的IVA;(IV)最后一步是将IVA作为输入进行基于FRS的风险分层,并使用传统的CA方法与FRS进行对比。使用IVA计算曲线下面积(AUC),并以CA为基准,同时将反应变量作为cIMT和糖化血红蛋白的标准化组合。
该研究招募了648名患者,其中202名是日本人,314名是亚洲印度人,132名是白种人。对所有人群的左右颈总动脉(CCA)进行了扫描,因此总共进行了1287次超声扫描。与使用CA的10年FRS(AUC = 0.66)相比,使用IVA的10年FRS报告的AUC更高(AUC = 0.78),高出约18%。
IVA是日常实践中对患者进行风险分层的有效生物标志物。