Computational simulation of stent thrombosis induced by various degrees of stent malapposition.

Percutaneous coronary intervention with stent implantation is one of the most commonly used approaches to treat coronary artery stenosis. Stent malapposition (SM) can increase the incidence of stent thrombosis, but the quantitative association between SM distance and stent thrombosis is poorly clarified. The objective of this study is to determine the biomechanical reaction mechanisms underlying stent thrombosis induced by SM and to quantify the effect of different SM severity grades on thrombosis. The thrombus simulation was performed in a continuous model based on the diffusion-convection response of blood substance transport. Simulated models included well-apposed stents and malapposed stents with various severities where the detachment distances ranged from 0 to 400 µm. The abnormal shear stress induced by SM was considered a critical contributor affecting stent thrombosis, which was dependent on changing SM distances in the simulation. The results illustrate that the proportion of thrombus volume was 1.88% at a SM distance of 75 µm (mild), 3.46% at 150 µm, and 3.93% at 400 µm (severe), but that a slight drop (3.18%) appeared at the detachment distance of 225 µm (intermediate). The results indicate that when the SM distance was less than 150 µm, the thrombus rose notably as the gap distance increased, whereas the progression of thrombogenicity weakened when it exceeded 150 µm. Therefore, more attention should be paid when SM is present at a gap distance of 150 µm. Moreover, when the SM length of stents are the same, thrombus tends to accumulate downstream towards the distal end of the stent as the SM distance increases.

Effect of Valve Height on the Opening and Closing Performance of the Aortic Valve Under Aortic Root Dilatation.

Patients with aortic valve disease can suffer from valve insufficiency after valve repair surgery due to aortic root dilatation. The paper investigates the effect of valve height (Hv) on the aortic valve opening and closing in order to select the appropriate range of Hv for smoother blood flow through the aortic valve and valve closure completely in the case of continuous aortic root dilatation. A total of 20 parameterized three-dimensional models of the aortic root were constructed following clinical surgical guidance. Aortic annulus diameter (DAA) was separately set to 26, 27, 28, 29, and 30 mm to simulate aortic root dilatation. HV value was separately set to 13.5, 14, 14.5, and 15 mm to simulate aortic valve alterations in surgery. Time-varying pressure loads were applied to the valve, vessel wall of the ascending aorta, and left ventricle. Then, finite element analysis software was employed to simulate the movement and mechanics of the aortic root. The feasible design range of the valve size was evaluated using maximum stress, geometric orifice area (GOA), and leaflet contact force. The results show that the valve was incompletely closed when HV was 13.5 mm and DAA was 29 or 30 mm. The GOA of the valve was small when HV was 15 mm and DAA was 26 or 27 mm. The corresponding values of the other models were within the normal range. Compared with the model with an HV of 14 mm, the model with an HV of 14.5 mm could effectively reduce maximum stress and had relatively larger GOA and less change in contact force. As a result, valve height affects the performance of aortic valve opening and closing. Smaller HV is adapted to smaller DAA and vice versa. When HV is 14.5 mm, the valve is well adapted to the dilatation of the aortic root to enhance repair durability. Therefore, more attention should be paid to HV in surgical planning.

Research on the Method of Predicting Fractional Flow Reserve Based on Multiple Independent Risk Factors.

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.