Predicting Left Ventricular Adverse Remodeling After Transcatheter Aortic Valve Replacement: A Radiomics Approach.

RATIONALE AND OBJECTIVES: To develop a radiomics model based on cardiac computed tomography (CT) for predicting left ventricular adverse remodeling (LVAR) in patients with severe aortic stenosis (AS) who underwent transcatheter aortic valve replacement (TAVR). MATERIALS AND METHODS: Patients with severe AS who underwent TAVR from January 2019 to December 2022 were recruited. The cohort was divided into adverse remodeling group and non-adverse remodeling group based on LVAR occurrence, and further randomly divided into a training set and a validation set at an 8:2 ratio. Left ventricular radiomics features were extracted from cardiac CT. The least absolute shrinkage and selection operator regression was utilized to select the most relevant radiomics features and clinical features. The radiomics features were used to construct the Radscore, which was then combined with the selected clinical features to build a nomogram. The predictive performance of the models was evaluated using the area under the curve (AUC), while the clinical value of the models was assessed using calibration curves and decision curve analysis. RESULTS: A total of 273 patients were finally enrolled, including 71 with adverse remodeling and 202 with non-adverse remodeling. 12 radiomics features and five clinical features were extracted to construct the radiomics model, clinical model, and nomogram, respectively. The radiomics model outperformed the clinical model (training AUC: 0.799 vs. 0.760; validation AUC: 0.766 vs. 0.755). The nomogram showed highest accuracy (training AUC: 0.859, validation AUC: 0.837) and was deemed most clinically valuable by decision curve analysis. CONCLUSION: The cardiac CT-based radiomics features could predict LVAR after TAVR in patients with severe AS.

Predicting the Risk of Type B Aortic Dissection Using Hemodynamic Parameters in Aortic Arches: A Comparative Study between Healthy and Repaired Aortas.

BACKGROUND AND OBJECTIVE: The development of acute aortic dissection (AD) remains unpredictable due to the intricate nature of the AD mechanism and the varied patient-specific aortic anatomy. The aim of this study was to simulate the hemodynamic parameters in the aortas before the onset of TBAD with healthy controls. METHODS: This study numerically assessed the effectiveness of hemodynamic indicators in predicting the risk of type B AD (TBAD) by investigating the differences in hemodynamic parameters between healthy and repaired aortas (aortas before TBAD development). Four wall shear stress (WSS)-based indicators and three helicity-based indicators were adopted and analyzed. RESULTS: The results showed that more pathological anatomical feathers can be observed in the repaired aortas. For WSS-based indicators, only averaged cross flow index (CFI) and oscillatory shear index OSI (CFI, 1.03 ± 0.07 vs. 0.83 ± 0.10 and OSI, 0.12 ± 0.03 vs. 0.04 ± 0.02) (all p<0.001) were significantly higher in the repaired aortas than those in the healthy aortas. On the other hand, average helicity in the repaired aortas also showed a significant difference compared with that in healthy aortas (h1, 3.88 ± 5.55 vs. -8.03 ± 14.16) (p<0.05). Furthermore, the skewed helical structure and flow disturbance was found in the repaired aortas. CONCLUSION: 1) There are marked differences in pathological anatomical features, such as aortic dilation, elongation and tortuosity between the healthy aortas and repaired aortas, and the corresponding hemodynamic indicators also have also been significantly changed. 2) Compared with anatomical characteristics, hemodynamic indicators may be more accurate for predicting the risk and location of TBAD, such as the OSI and CFI index were significantly enhanced in the region where the entry tears have occurred. 3) In clinical practice, anatomical features remain important factors for assessing the risk for development of TBAD; however, hemodynamic analyses with quantitative data and more visualizing characteristics have showed promising potential in this aspect.

Risk evaluation of type B aortic dissection based on WSS-based indicators distribution in different types of aortic arch.

BACKGROUND AND OBJECTIVE: The underlying mechanism of aortic dissection (AD) remains unclear and the onset of AD is still unpredictable. Although clinical study with statistical analysis has reported that type III aortic arch may have strong correlation with type B AD (TBD), the effects of different arch types on the wall shear stress (WSS) have not been clarified. METHODS: As a complementary work, this study numerically investigated the distribution of five WSS-based indicators in thirty aortic arches without AD, which were classified into three groups based on the arch types. RESULTS: The distribution of most WSS indicators, such as time averaged WSS (TAWSS), oscillatory shear index (OSI) and relative residence time (RRT) had no significant difference among different types of aortic arches (P>0.05). However, a multidirectional WSS index, namely CFI, was found its maximum value was positively correlated with type III aortic arch in proximal descending aorta (p<0.001, r = 0.65). CONCLUSIONS: It can be concluded that the enhancement or oscillation of WSS may not be the main reason of TBD is prevalence in type III arches, while the multidirectional WSS distribution may be an important factor. It can be further referred that the CFI may have a potential to predict the onset of TBD.