Zhang Honghui, Li Gaoyang, Hou Qianwen, Yang Yinlong, Wei Hongge, Yang Yujia, Qu Zhuoran, Xie Jinjie, Qiao Aike
Faculty of Environment and Life, Beijing University of Technology, Beijing, China.
College of Engineering, Inner Mongolia University for Nationalities, Tongliao, China.
Front Physiol. 2021 Aug 13;12:716877. doi: 10.3389/fphys.2021.716877. eCollection 2021.
The use of diameter stenosis (DS), as revealed by coronary angiography, for predicting fractional flow reserve (FFR) usually results in a high error rate of detection. In this study, we investigated a method for predicting FFR in patients with coronary stenosis based on multiple independent risk factors. The aim of the study was to improve the accuracy of detection. First, we searched the existing literature to identify multiple independent risk factors and then calculated the corresponding odds ratios. The improved analytic hierarchy process (IAHP) was then used to determine the weighted value of each independent risk factor, based on the corresponding odds ratio. Next, we developed a novel method, based on the top seven independent risk factors with the highest weighted values, to predict FFR. This model was then used to predict the FFR of 253 patients with coronary stenosis, and the results were then compared with previous methods (DS alone and a simplified scoring system). In addition to DS, we identified a range of other independent risk factors, with the highest weighted values, for predicting FFR, including gender, body mass index, location of stenosis, type of coronary artery distribution, left ventricular ejection fraction, and left myocardial mass. The area under the receiver-operating characteristic curve (AUC) for the newly developed method was 84.3% (95% CI: 79.2-89.4%), which was larger than 65.3% (95% CI: 61.5-69.1%) of DS alone and 74.8% (95% CI: 68.4-81.2%) of the existing simplified scoring system. The newly developed method, based on multiple independent risk factors, effectively improves the prediction accuracy for FFR.
冠状动脉造影显示的直径狭窄（DS）用于预测血流储备分数（FFR）时，通常会导致较高的检测错误率。在本研究中，我们调查了一种基于多个独立危险因素预测冠状动脉狭窄患者FFR的方法。该研究的目的是提高检测准确性。首先，我们检索现有文献以确定多个独立危险因素，然后计算相应的比值比。接着，基于相应的比值比，使用改进的层次分析法（IAHP）确定每个独立危险因素的加权值。接下来，我们基于加权值最高的前七个独立危险因素开发了一种预测FFR的新方法。然后使用该模型预测253例冠状动脉狭窄患者的FFR，并将结果与先前方法（仅DS和简化评分系统）进行比较。除DS外，我们还确定了一系列其他加权值最高的独立危险因素用于预测FFR，包括性别、体重指数、狭窄部位、冠状动脉分布类型、左心室射血分数和左心肌质量。新开发方法的受试者工作特征曲线下面积（AUC）为84.3%（95%CI：79.2 - 89.4%），大于仅DS的65.3%（95%CI：61.5 - 69.1%）和现有简化评分系统的74.8%（95%CI：68.4 - 81.2%）。基于多个独立危险因素新开发的方法有效提高了FFR的预测准确性。
