一种新的外周内皮功能测量方法可改善对有症状冠状动脉疾病的预测。

A new peripheral endothelial function measurement improves prediction of symptomatic coronary artery disease.

作者信息

Du Beibei, Si Daoyuan, Zhao Dong, Zhao Yanan, Wagatsuma Kenji, He Yuquan, Yang Ping

机构信息

Department of Cardiology, The Third Hospital of Jilin University, Jilin Provincial Engineering Laboratory for Endothelial Function and Genetic Diagnosis of Cardiovascular Disease, Changchun, Jilin, China.

Tsukuba Heart Center, Tsukuba Memorial Hospital, Tsukuba, Japan.

出版信息

J Int Med Res. 2020 Jun;48(6):300060520932818. doi: 10.1177/0300060520932818.

DOI:10.1177/0300060520932818

PMID:32589086

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7323288/

Abstract

OBJECTIVE

This study aimed to determine whether a peripheral artery volume (PAV) test can improve the predictive value of the age-adjusted Framingham risk score (AFRS) for coronary artery disease (CAD) in symptomatic patients.

METHODS

A total of 317 consecutive patients who were referred for coronary angiography were prospectively enrolled. Before cardiac catheterization, a PAV test was performed to measure changes in pulsatile blood flow volume following reactive hyperemia.

RESULTS

PAV was significantly lower in patients with CAD than in those without CAD (1.21 ± 0.32 vs. 1.50 ± 0.45). Multivariate logistic regression analysis showed that PAV and the AFRS were independent predictors of CAD. Pairwise comparison of receiver operating characteristic curves showed that the predictive power for CAD increased when PAV was incorporated into the AFRS (area under the curve: from 0.76 to 0.80). The net reclassification index was also improved when PAV was added to the AFRS (0.65, 95% confidence interval: 0.44-0.85).

CONCLUSIONS

Digital endothelial function measurement is an independent predictor of CAD. PAV is potentially useful for identifying patients at high risk for CAD.

摘要

目的

本研究旨在确定外周动脉容积（PAV）检测能否提高症状性患者中年龄校正的弗明汉姆风险评分（AFRS）对冠状动脉疾病（CAD）的预测价值。

方法

前瞻性纳入了317例连续接受冠状动脉造影的患者。在心脏导管插入术前，进行PAV检测以测量反应性充血后搏动血流容积的变化。

结果

CAD患者的PAV显著低于无CAD患者（1.21±0.32 vs. 1.50±0.45）。多因素逻辑回归分析显示，PAV和AFRS是CAD的独立预测因素。对受试者工作特征曲线的两两比较显示，将PAV纳入AFRS时，对CAD的预测能力增强（曲线下面积：从0.76增至0.80）。将PAV添加到AFRS时，净重新分类指数也有所改善（0.65，95%置信区间：0.44 - 0.85）。