Simulation-Driven Machine Learning for Predicting Stent Expansion in Calcified Coronary Artery

Load-sharing characteristics of stenting and post-dilation in heavily calcified coronary artery

In this work, stenting in non-calcified and heavily calcified coronary arteries was quantified in terms of diameter-pressure relationships and load transfer from the balloon to the artery. The efficacy of post-dilation in non-calcified and heavily calcified coronary arteries was also characterized in terms of load sharing and the changes in tissue mechanics. Our results have shown that stent expansion exhibits a cylindrical shape in non-calcified lesions, while it exhibits a dog bone shape in heavily calcified lesions. Load-sharing analysis has shown that only a small portion of the pressure load (1.4 N, 0.8% of total pressure load) was transferred to the non-calcified lesion, while a large amount of the pressure load (19 N, 12%) was transferred to the heavily calcified lesion. In addition, the increasing inflation pressure (from 10 to 20 atm) can effectively increase the minimal lumen diameter (from 1.48 mm to 2.82 mm) of the heavily calcified lesion, the stress (from 1.5 MPa to 8.4 MPa) the strain energy in the calcification (1.77 mJ to 26.5 mJ), which associated with the potential of calcification fracture. Results indicated that increasing inflation pressure can be an effective way to improve the stent expansion if a dog bone shape of the stenting profile is observed. Considering the risk of a balloon burst, our results support the design and application of the high-pressure balloon for post-dilation.

Carotid massive intraplaque hemorrhage, lipid-rich necrotic core, and heavy circumferential calcification were associated with new ipsilateral ischemic cerebral lesions after carotid artery stenting: high-resolution magnetic resonance vessel wall imaging study

Background

Following carotid artery stenting (CAS), new ipsilateral ischemic lesions (NIILs) in the brain are frequently seen using diffusion-weighted imaging (DWI). This study's goal was to identify the imaging characteristics associated with NIILs after CAS by high-resolution magnetic resonance vessel wall imaging (HR-VWI).

Methods

This was a case-control study. 109 patients who received CAS for atherosclerotic carotid stenosis were retrospectively collected and categorized into NIILs positive and NIILs negative groups. Based on the existence or absence of stroke symptoms after CAS, the NIILs positive group was split into two subgroups: the NIILs symptomatic group and the NIILs asymptomatic group. Patients underwent preoperative HR-VWI and brain magnetic resonance imaging (MRI) within 7 days preoperatively and within 3 days postoperatively. Quantitatively assess carotid plaque burden and components using HR-VWI. The baseline and HR-VWI imaging characteristics of all patients were retrospectively analyzed. To ascertain the imaging characteristics connected with NIILs after CAS, logistic regression analysis was carried out.

Results

Among 109 patients, 38 patients (34.9%) developed NIILs after CAS. Six patients (5.5%) developed symptomatic stroke with NIILs. The logistic regression analysis revealed that maximum wall thickness (Max WT) [odds ratio (OR), 1.53; 95% confidence interval (CI): 1.20-1.96; P=0.001], the maximum area percentage of lipid-rich necrotic core (LRNC) (OR, 1.05; 95% CI: 1.03-1.07; P<0.001), the volume of LRNC (OR, 1.004; 95% CI: 1.002-1.005; P<0.001), the maximum area percentage of intraplaque hemorrhage (IPH) (OR, 1.17; 95% CI: 1.11-1.24; P<0.001), the volume of IPH (OR, 1.06; 95% CI: 1.03-1.08; P<0.001), and maximum circumference score of calcification in a single slice (OR, 1.66; 95% CI: 1.04-2.63; P=0.03) were linked with NIILs following CAS.

Conclusions

The massive IPH, LRNC, and heavy circumferential calcification were associated with NIILs after CAS. Preoperative quantitative assessment of carotid plaque using HR-VWI may be useful for predicting NIILs following CAS.

A Machine-Learning Model for the Prognostic Role of C-Reactive Protein in Myocarditis

Aims: The role of inflammation markers in myocarditis is unclear. We assessed the diagnostic and prognostic correlates of C-reactive protein (CRP) at diagnosis in patients with myocarditis. Methods and results: We retrospectively enrolled patients with clinically suspected (CS) or biopsy-proven (BP) myocarditis, with available CRP at diagnosis. Clinical, laboratory and imaging data were collected at diagnosis and at follow-up visits. To evaluate predictors of death/heart transplant (Htx), a machine-learning approach based on random forest for survival data was employed. We included 409 patients (74% males, aged 37 ± 15, median follow-up 2.9 years). Abnormal CRP was reported in 288 patients, mainly with CS myocarditis (p < 0.001), recent viral infection, shorter symptoms duration (p = 0.001), chest pain (p < 0.001), better functional class at diagnosis (p = 0.018) and higher troponin I values (p < 0.001). Death/Htx was reported in 13 patients, of whom 10 had BP myocarditis (overall 10-year survival 94%). Survival rates did not differ according to CRP levels (p = 0.23). The strongest survival predictor was LVEF, followed by anti-nuclear auto-antibodies (ANA) and BP status. Conclusions: Raised CRP at diagnosis identifies patients with CS myocarditis and less severe clinical features, but does not contribute to predicting survival. Main death/Htx predictors are reduced LVEF, BP diagnosis and positive ANA.

Rapid Prediction of Retina Stress and Strain Patterns in Soccer-Related Ocular Injury: Integrating Finite Element Analysis with Machine Learning Approach

Soccer-related ocular injuries, especially retinal injuries, have attracted increasing attention. The mechanics of a flying soccer ball have induced abnormally higher retinal stresses and strains, and their correlation with retinal injuries has been characterized using the finite element (FE) method. However, FE simulations demand solid mechanical expertise and extensive computational time, both of which are difficult to adopt in clinical settings. This study proposes a framework that combines FE analysis with a machine learning (ML) approach for the fast prediction of retina mechanics. Different impact scenarios were simulated using the FE method to obtain the von Mises stress map and the maximum principal strain map in the posterior retina. These stress and strain patterns, along with their input parameters, were used to train and test a partial least squares regression (PLSR) model to predict the soccer-induced retina stress and strain in terms of distributions and peak magnitudes. The peak von Mises stress and maximum principal strain prediction errors were 3.03% and 9.94% for the frontal impact and were 9.08% and 16.40% for the diagonal impact, respectively. The average prediction error of von Mises stress and the maximum principal strain were 15.62% and 21.15% for frontal impacts and were 10.77% and 21.78% for diagonal impacts, respectively. This work provides a surrogate model of FE analysis for the fast prediction of the dynamic mechanics of the retina in response to the soccer impact, which could be further utilized for developing a diagnostic tool for soccer-related ocular trauma.