Published online Sep 26, 2019. doi: 10.12998/wjcc.v7.i18.2722
Peer-review started: May 21, 2019
First decision: July 30, 2019
Revised: August 5, 2019
Accepted: August 20, 2019
Article in press: August 20, 2019
Published online: September 26, 2019
Transient ischemic attack (TIA) is a common cause of acute ischemic stroke (AIS), and its early prediction is of great clinical significance for the prevention of AIS. Carotid atherosclerosis is known to be the most important cause of TIA. So far, some biochemical indexes related to carotid atherosclerosis, such as blood pressure, blood glucose, and Hcy and ultrasound imaging indicators including plaque property and lumen stenosis rate are the risk factors for TIA. However, the accuracy of their predictions of TIA is limited.
Wall shear stress (WSS) is defined as the tangential friction of blood flowing on the surface of blood vessel walls acting on arterial endothelial cells. Recent studies have found that increased WSS on the plaque surface is more prone to plaque rupture. After plaque rupture, the ulcer is likely to form a microthrombus, which may block distal blood vessels and cause TIA. Therefore, the higher the WSS of carotid plaques, the more likely it is to cause TIA. However, it is uncertain whether WSS can improve the accuracy of predicting the occurrence of TIA.
In this study, we analyzed the routine indicators and WSS data of patients with atherosclerosis. The aim of our study was to investigate the improving effect of combining WSS with conventional predictive indicators in TIA prediction.
A total of 250 patients with atherosclerosis were recruited. The laboratory indexes and imaging indexes of the patients were measured and recorded. The WSS distribution maps of the proximal and distal ends of the plaque shoulder were drawn using the shear stress quantitative analysis software, and the average values of WSS were recorded. The patients were followed for 4 years, and patients with TIA were included in a TIA group and the remaining patients were included in a control group. ROC curves were used to assess the accuracy of potential indicators in predicting TIA, and Logistic regression model was used to establish a combined prediction and explore its accuracy for predicting TIA.
The WSS between the proximal and distal ends of the plaque shoulder indicated an excellent agreement (ICC = 0.976 and 0.993, respectively). Besides, the WSS at the proximal end of the shoulder was significantly higher than that of the distal end (P < 0.05). According to the results of follow-up, patients with atherosclerosis were divided into a TIA group (n = 30) and a control group (n = 204). After COX multivariate analysis, hypertension, diabetes, homocysteine (Hcy), FBG, plaque property, lumen stenosis rate, and proximal end WSS were found to be independent factors influencing TIA during follow-up (P < 0.05). Among them, the proximal end WSS had the highest accuracy in predicting TIA, but its AUC was still less than 0.9. The Logistic regression results showed that the accuracy of the combination with WSS (AUC = 0.944) was significantly higher than that of the combination without WSS (AUC = 0.856) in predicting TIA (z = 2.177, P = 0.030). It is suggested that the traditional indexes combined with WSS can significantly improve the accuracy of TIA prediction.
WSS plays an important role in predicting TIA. WSS at plaque surface combined with hypertension, diabetes, Hcy, blood glucose, plaque properties, and stenosis rate can significantly improve the accuracy of TIA prediction.
In order to avoid the interference caused by the differences of individual and environmental factors among different patients, an animal experiment should be performed to explore if WSS can improve the accuracy of TIA prediction. Animal experiments are more controllable, which can reduce unnecessary external environmental interference and improve the accuracy. Therefore, the effect of the combination of the conventional prediction indexes and WSS in predicting TIA is expected to be further revealed by animal experiments.