Abstract
BACKGROUND: The purpose of this study is to find the high-risk morphological features in type B aortic dissection (TBAD) population and to establish an early detection model. METHODS: From June 2018 to February 2022, 234 patients came to our hospital because of chest pain. After examination and definite diagnosis, we excluded people with previous cardiovascular surgery history, connective tissue disease, aortic arch variation, valve malformation, and traumatic dissection. Finally, we included 49 patients in the TBAD group and 57 in the control group. The imaging data were retrospectively analyzed by Endosize (Therevna 3.1.40) software. The aortic morphological parameters mainly include diameter, length, direct distance, and tortuosity index. Multivariable logistic regression models were performed and systolic blood pressure (SBP), aortic diameter at the left common carotid artery (D3), and length of ascending aorta (L1) were chosen to build a model. The predictive capacity of the models was evaluated through the receiver operating characteristic (ROC) curve analysis. RESULTS: The diameters in the ascending aorta and aortic arch are larger in the TBAD group (33.9 ± 5.9 vs. 37.8 ± 4.9 mm, p < 0.001; 28.2 ± 3.9 vs. 31.7 ± 3.0 mm, p < 0.001). The ascending aorta was significantly longer in the TBAD group (80.3 ± 11.7 vs. 92.3 ± 10.6 mm, p < 0.001). Besides, the direct distance and tortuosity index of the ascending aorta in the TBAD group increased significantly (69.8 ± 9.0 vs. 78.7 ± 8.8 mm, p < 0.001; 1.15 ± 0.05 vs. 1.17 ± 0.06, p < 0.05). Multivariable models demonstrated that SBP, aortic diameter at the left common carotid artery (D3), and length of ascending aorta (L1) were independent predictors of TBAD occurrence. Based on the ROC analysis, area under the ROC curve of the risk prediction models was 0.831. CONCLUSION: Morphological characteristic including diameter of total aorta, length of ascending aorta, direct distance of ascending aorta, and tortuosity index of ascending aorta are valuable geometric risk factors. Our model shows a good performance in predicting the incidence of TBAD.