Changes in aortic arch geometry and the risk for Stanford B dissection

Three-dimensional aortic geometry: clinical correlates, prognostic value and genetic architecture

Aortic structure and function impact cardiovascular health through multiple mechanisms. Aortic structural degeneration increases left ventricular afterload, pulse pressure and promotes target organ damage. Despite the impact of aortic structure on cardiovascular health, aortic 3D-geometry has yet to be comprehensively assessed. Using a convolutional neural network (U-Net) combined with morphological operations, we quantified aortic 3D-geometric phenotypes (AGPs) from 53,612 participants in the UK Biobank and 8,066 participants in the Penn Medicine Biobank. AGPs reflective of structural aortic degeneration, characterized by arch unfolding, descending aortic lengthening and luminal dilation exhibited cross-sectional associations with hypertension and cardiac diseases, and were predictive for new-onset hypertension, heart failure, cardiomyopathy, and atrial fibrillation. We identified 237 novel genetic loci associated with 3D-AGPs. Fibrillin-2 gene polymorphisms were identified as key determinants of aortic arch-3D structure. Mendelian randomization identified putative causal effects of aortic geometry on the risk of chronic kidney disease and stroke.

Establishing and Validating a Morphological Prediction Model Based on CTA to Evaluate the Incidence of Type-B Dissection

Background: Many patients with Type B aortic dissection (TBAD) may not show noticeable symptoms until they become intervention and help prevent critically ill, which can result in fatal outcomes. Thus, it is crucial to screen people at high risk of TBAD and initiate the necessary preventive and therapeutic measures before irreversible harm occurs. By developing a prediction model for aortic arch morphology, it is possible to accurately identify those at high risk and take prompt action to prevent the adverse consequences of TBAD. This approach can facilitate timely the development of serious illnesses. Method: The predictive model was established in a primary population consisting of 173 patients diagnosed with acute Stanford TBAD, with data collected from January 2017 and December 2018, as well as 534 patients with healthy aortas, with data collected from April 2018 and December 2018. Explicitly, the data were randomly separated into the derivation set and validation set in a 7:3 ratio. Geometric and anatomical features were extracted from a three-dimensional multiplanar reconstruction of the aortic arch. The LASSO regression model was utilized to minimize the data dimension and choose relevant features. Multivariable logistic regression analysis and backward stepwise selection were employed for predictive model generation, combining demographic and clinical features as well as geometric and anatomical features. The predictive model's performance was evaluated by examining its calibration, discrimination, and clinical benefit. Finally, we also conducted internal verification. Results: After applying LASSO logistic regression and backward stepwise selection, 12 features were entered into the prediction model. Age, aortic arch angle, total thoracic aorta distance, ascending aorta tortuosity, aortic arch tortuosity, distal descending aorta tortuosity, and type III arch were protective factors, while male sex, hypertension, aortic arch height, and aortic arch distance were risk factors. The model exhibited satisfactory discrimination (AUC, 0.917 [95% CI, 0.890-0.945]) and good calibration in the derivation set. Applying the predictive model to the validation set also provided satisfactory discrimination (AUC, 0.909 [95% CI, 0.864-0.953]) and good calibration. The TBAD nomogram for clinical use was established. Conclusions: This study demonstrates that a multivariable logistic regression model can be used to predict TBAD patients.

The loss of helical flow in the thoracic aorta might be an identifying marker for the risk of acute type B aortic dissection

BACKGROUND AND OBJECTIVE

The occurrence of acute type B aortic dissection (TBAD) remained unclear. This study aimed to investigate the association between flow features and hemodynamic parameters in aortas that demonstrated the risk of TBAD occurrence.

METHODS

The geometries of 15 hyperacute TBAD and 12 control patients (with healthy aorta) were reconstructed from computed tomography angiography images. Pre-TBAD models were then obtained by eliminating the dissection flaps. Flow features and hemodynamic parameters, including wall shear stress-related parameters and helicities, were compared between pre-TBAD and control models using computational fluid dynamics.

RESULTS

There were no significant differences in baseline characteristics and anatomical parameters between the two groups. Significant contralateral helical blood flow was present in the healthy thoracic aorta, while almost no helical flow was observed in the pre-TBAD group. In addition, the mean normal transverse wall shear stress (NtransWSS) was significantly higher in the pre-TBAD group (aortic arch 0.49±0.09 vs. 0.40±0.05, P = 0.04; descending aorta: 0.46±0.05 vs. 0.33±0.02, P<0.01). Moreover, a significantly negative correlation was found between helicity and NtransWSS in the descending aorta. Moreover, the location of primary tears in 12 pre-TABD subjects matched well with regions of high NtransWSS.

CONCLUSIONS

Loss of helical flow in the aortic arch and descending aorta may be a major flow feature in patients with underlying TBAD, resulting in increased flow disturbance and wall lesions.

Identification of high-risk patients for development of type B aortic dissection based on novel morphological parameters

Background: Predicting the development of sporadic type B aortic dissection (TBAD) always remains a difficult issue. This study aimed to identify high-risk patients for development of TBAD based on morphological parameters. Methods: This propensity-score-matched case-control study collected and reconstructed the computed tomography angiography of acute TBAD patients and hospital-based control participants without aortic dissection from January 2013 to December 2016. Multivariate regression analysis was used to calculate the adjusted odds ratio (aOR) and 95% confidence interval (CI). Discriminant and reclassification abilities were compared between our model and a previously established model. Results: Our study included 76 acute TBAD patients and 79 control patients (48 cases and 48 controls after propensity-score matching). The degree of question mark (aOR 1.07, 95% CI 1.04-1.11), brachiocephalic trunk diameter (aOR 1.49, 95% CI 1.20-1.85), brachiocephalic trunk angle (aOR 0.97, 95% CI 0.94-0.99), aortic root diameter (aOR 1.31, 95% CI 1.15-1.48), and aortic width (aOR 1.12, 95% CI 1.07-1.17) were associated with a significantly increased risk of TBAD formation. Similar findings were observed in the propensity-score matching and sensitivity analysis only including hyperacute TBAD patients. A novel prediction model was established based on the aforementioned parameters. The new model showed significantly improved discriminant ability compared with the previously established model (c-index 0.78 [95% CI 0.71-0.85] vs. 0.67 [95% CI 0.58-0.75], p = .03), driven by increased reclassification ability in identifying TBAD patients (NRI for events 0.16, 95% CI 0.02-0.30, p = .02). Conclusion: Morphological predictors, including the degree of question mark, aortic width, aortic root diameter, brachiocephalic trunk angle, and brachiocephalic trunk diameter, may be used to identify patients at high risk of TBAD.

A stable and quantitative method for dimensionality reduction of aortic centerline

Aortic dissection (AD) is a fatal aortic disease with high mortality. Assessing the morphology of the aorta is critical for diagnostic and surgical decisions. Aortic centerline projection methods have been used to evaluate the morphology of the aorta. However, there is a big difference between the current model of primary plane projection (PPP) and the actual shape of individuals, which is not conducive to morphological statistical analysis. Finding a method to compress the three-dimensional information of the aorta into two dimensions is helpful to clinical decision-making. In this paper, the evaluation parameters, including contour length (CL), enclosure area, and the sum of absolute residuals (SAR), were introduced to objectively evaluate the optimal projection plane rather than artificial subjective judgment. Our results showed that the optimal projection plane could be objectively characterized by the three evaluation parameters. As the morphological criterion, SAR is optimal among the three parameters. Compared to the optimal projection plane selected by traditional PPP, our method has better AD discrimination in the analysis of aortic tortuosity, and is conducive to the clinical operation of AD. Thus, it has application prospects for the preprocessing techniques for the geometric morphology analysis of AD.

A Combined Deep Learning System for Automatic Detection of “Bovine” Aortic Arch on Computed Tomography Scans

The “bovine” aortic arch is an anatomic variant consisting in a common origin of the innominate and left carotid artery (CILCA), associated with a greater risk of thoracic aortic diseases (aneurysms and dissections), stroke, and complications after endovascular procedures. CILCA can be detected by visual assessment of computed tomography (CT) chest scans, but it is rarely reported. We developed a deep learning (DL) segmentation-plus-classification system to automatically detect CILCA based on 302 CT studies acquired at 2 centers. One model (3D U-Net) was trained from scratch (supervised by manual segmentation), validated, and tested for the automatic segmentation of the aortic arch and supra-aortic vessels. Three DL architectures (ResNet50, DenseNet-201, and SqueezeNet), pre-trained over millions of common images, were trained, validated, and tested for the automatic classification of CILCA versus non-CILCA, supervised by radiologist’s classification. The 3D U-Net-plus-DenseNet-201 was found to be the best system (Dice index 0.912); its classification performance obtained from internal, independent testing on 126 patients gave a receiver operating characteristic area under the curve of 87.0%, sensitivity 66.7%, specificity 90.5%, positive predictive value 87.5%, negative predictive value 73.1%, positive likelihood ratio 7.0, and negative likelihood ratio 0.4. In conclusion, a combined DL system applied to chest CT scans was developed and proven to be an effective tool to detect individuals with “bovine” aortic arch with a low rate of false-positive findings.

Potential Clinical Value of Biomarker-Guided Emergency Triage for Thoracic Aortic Dissection

Aim: Thoracic aortic dissection (TAD) is a high-risk vascular disease. The mortality rate of untreated TADs in 24 h was as high as 50%. Thus, rapid diagnosis of TAD in the emergency department would get patients to the right treatments to save their lives. Methods: We profiled the proteome of aortic tissues from TAD patients using a label-free quantification proteomics method. The differentially expressed proteins were screened and subjected to bioinformatics analysis. Candidate biomarkers were selected and validated in independent serum samples using enzyme-linked immunosorbent assays (ELISAs). The diagnostic values were further predicted via receiver operating characteristic (ROC) curve analysis. Results: A total of 1,141 differentially expressed proteins were identified in aortic tissues from 17 TAD patients and eight myocardial infarction (MI) patients. Six proteins were selected as candidate biomarkers for ELISAs in an independent training set of 20 serum samples (TAD = 10, MI = 10). Of these proteins, four with a P-value < 0.01 were further validated in another independent set of 64 serum samples (TAD = 32, MI = 32) via ELISAs. ITGA2, COL2A1, and MIF had P-values < 0.0001, and their areas under the curve (AUCs) were 0.801 (95% CI: 0.691-0.911), 0.773 (95% CI: 0.660-0.887), and 0.701 (95% CI: 0.574-0.828), respectively. Conclusion: ITGA2, COL2A1, and MIF were identified as promising biomarkers for discriminating TAD from emergency patients with severe chest pain. Biomarker-guided emergency triage could further shorten the time for patients to get more effective treatments.