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

https://doi.org/10.1016/j.cmpb.2022.106872Get rights and content

Highlights

  • General level of time-averaged wall shear stress (TAWSS) was relatively low in type III aortic arch models.

  • Traditional WSS-based indicators (TAWSS, OSI, RRT) show insignificant difference among different types aortic arch.

  • Cross flow index (CFI) has a significant and positive relationship with type III aortic arch at segment of proximal descending aorta.

  • The CFI, as a multidirectional WSS-based indicator, might be promising as a parameter for predicting the onset of type B aortic dissection (TBD).

Abstract

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.

Introduction

In the past decades, several studies have proposed some specific and reliable anatomical risk factors to identify the risk of Stanford type B aortic dissection (AD) in hypertensive patients. The representative anatomical factors include aortic angulation [1], [2], [3], [4], aortic tortuosity [5] and aortic elongation [6], [7], [8]. However, those anatomic factors are limited by different definitions in previous studies and lack of consistent threshold values to quantitatively evaluate the risk of type B dissection (TBD).

Recently, Marrocco-Trischitta MM et al. found a significantly higher prevalence of type III arch in patients with TBD and type B intramural haematoma (IMH-B), and proposed that type III arch configuration may have an association with TBD [9]. This finding provides a useful and readily intuitive tool for surgeons to stratify patients without requiring any specific software.

Nevertheless, type III arch configuration is a well-established radiological feature and originally used to predict the difficulty of cannulation of supra-aortic vessels for carotid stenting [10]. The geometric features of type III arch are usually age-dependent [11], [12], [13], [14] and are common in octogenarians [15,16], who typically have more significant arch tortuosity, angulation and elongation [1,17,18]. Therefore, the mechanism of high prevalence of TBD in patients with type III aortic arch needs further clarity.

It is well-recognized that the evaluation of hemodynamic performance has become an important tool for understanding and analyzing the mechanism of AD disease [19], [20], [21]. For example, several studies have reported that wall shear stress (WSS) distribution can affect the endothelial behavior [22] and extremely high WSS (larger than 10 Pa) can trigger endothelial cells express unique transcriptional profiles to maintain the stability of the fluid transport system, which may result in expansive remodeling of aortic vessel wall [23], [24], [25]. Furthermore, Doyle et al. proposed that the tear regions of aortic dissection was highly coincident with high WSS regions [26], suggesting accurate estimation of WSS has an advantage to predict the tear expanding and specific tear positions [1,[26], [27], [28]]. Therefore, hemodynamic analysis in different types of aortic arches may be useful to find out the mechanism of prevalence of TBD in patients with type III aortic arch.

Although Marrocco-trischitta et al. have reported that a specific, consistent and abnormal secondary HF pattern could be observed in type III arch, the wall shear distribution was not mentioned in their study or other literatures [29]. Thus, the main purpose of this study was to numerically investigate the distribution of WSS-based indicators, including time-averaged WSS (TAWSS), oscillating wall shear index (OSI), relative residence time (RRT) and two multidirectional WSS indicators, including transverse WSS (TransWSS) and cross flow index (CFI) with respect to different types of aortic arch models.

Thirty anonymous patient-specific models which were randomly chosen and classified into three groups (type I: n = 10; type II: n = 10; type III: n = 10) were adopted to carry out numerical and statistical investigation. This study was a complementary investigation of aforementioned studies, and can be used to explore the mechanism of prevalence of TBD in patients with type III arch with respect to hemodynamic point of view.

Section snippets

Study population

Ten (n = 10) anonymized groups of CTA images of the thoracic aortas were selected per types of arch I, II, III for the purpose of the present study, as shown in Fig. 1A. The different arch types were defined based on the vertical distance from the origin of the innominate artery (IA) to the top of the arch [9].

These 30 groups of CTA images provided by the West China Hospital of Sichuan University (Chengdu, Sichuan, China) were randomly retrieved from a cohort of individuals with a healthy

Traditional WSS-based indicators analysis

Fig. 3, Fig. 4, Fig. 5 show the distribution contour maps of traditional WSS-based indicators, namely, TAWSS, OSI and RRT on those aortic arches, respectively. First, as for the TAWSS, extremely high TAWSS (maximum value > 10pa) can be observed in all three types of aortic arches, such as the v10 in type I, v18 in type II and v25 in type III. However, the global wall shear stress level in type III arch was relatively lower than that in the other two types. Specifically, those low WSS

Discussion

So far, although the treatment of TBD is relatively mature, the potential mechanism of AD is still unclear, and onset of AD in a normal aorta remains unpredictable [9]. According to the literature, one-third of patients suffering AD do not have any congenital tissue defect or aortic aneurysmal dilation [2]. Therefore, providing an efficient and accurate method for predicting onset of AD is an important scientific question in clinical practice.

In order to clarify the mechanism of TBD effected by

Conclusion

As it is well known that analysis based on medical imaging has become a very important method for understanding and analyzing AD, which can help surgeons to make initial and further assessments of their patients [44], [45], [46], [47], [48]. This study qualitatively and quantitatively evaluated the distribution of different WSS-based indicators in three types of aortic arches reconstructed from thirty patient-specific medical images and can be concluded that: 1) traditional WSS-based indicators

Limitation

As a preliminary study, this study has several limitations. First, the number of aortic models in this study is relatively small. However, as a complementary study of Marrocco-Trischitta et al. [29], this study enlarged the numbers of each type of aortic arches (n = 10 for each type), which may be sufficient to carry out a statistical hemodynamic study. Secondly, we did not perform a follow-up study to validate the efficacy of CFI in predicting the development of TBD in real world, and this

Disclosure statement

No potential conflict of interest was reported by the authors.

Declaration of Competing Interest

The authors declare no conflict of interest for this paper.

Acknowledgments

This study was supported in part by the National Natural Science Foundation of China [No. 11802253, 81601462, 81771927], the scholarship from China Scholarship Council (CSC) under the Grant CSC [No. 201908510047] and the Key Research & Development Project of Science and Technology of Sichuan Province [2021YFS0142].

References (48)

  • E.S. Di Martino et al.

    Fluid-structure interaction within realistic three-dimensional models of the aneurysmatic aorta as a guidance to assess the risk of rupture of the aneurysm

    Med. Eng. Phys.

    (2001)
  • B.M. Johnston et al.

    Non-Newtonian blood flow in human right coronary arteries: transient simulations

    J. Biomech.

    (2006)
  • J. Wen et al.

    Effect of anastomosis angles on retrograde perfusion and hemodynamics of hybrid treatment for thoracoabdominal aortic aneurysm

    Ann. Vasc. Surg.

    (2022)
  • F.J.H. Gijsen et al.

    A new imaging technique to study 3-D plaque and shear stress distribution in human coronary artery bifurcations in vivo

    J. Biomech.

    (2007)
  • V. Peiffer et al.

    Effect of aortic taper on patterns of blood flow and wall shear stress in rabbits: association with age

    Atherosclerosis

    (2012)
  • Y. Mohamied et al.

    Understanding the fluid mechanics behind transverse wall shear stress

    J. Biomech.

    (2017)
  • A. Osswald et al.

    Elevated wall shear stress in aortic type B dissection may relate to retrograde aortic type a dissection: a computational fluid dynamics pilot study

    Eur. J. Vasc. Endovasc. Surg. : Off. J. Eur. Soc. Vasc. Surg.

    (2017)
  • J.M.-T. Wu et al.

    Applying an ensemble convolutional neural network with Savitzky–Golay filter to construct a phonocardiogram prediction model

    Appl. Soft Comput.

    (2019)
  • R. Chatterjee et al.

    A novel machine learning based feature selection for motor imagery EEG signal classification in Internet of medical things environment

    Fut. Gen. Comput. Syst.

    (2019)
  • K.K.L. Wong et al.

    Deep learning-based cardiovascular image diagnosis: a promising challenge

    Fut. Gen. Comput. Syst.

    (2020)
  • F. Piccialli et al.

    A survey on deep learning in medicine: why, how and when?

    Inf. Fusion

    (2021)
  • M.P. Poullis et al.

    Ascending aortic curvature as an independent risk factor for type A dissection, and ascending aortic aneurysm formation: a mathematical model

    Eur. J. Cardio-Thoracic Surg. : Off. J. Eur. Assoc. Cardio-Thoracic Surg.

    (2008)
  • S. Heuts et al.

    Aortic elongation part II: the risk of acute type A aortic dissection

    Heart

    (2018)
  • I. Akin et al.

    Age-dependent aortic elongation: a new predictor for type A aortic dissection?

    Heart

    (2018)
  • Cited by (0)

    1

    The authors contributed equally to this work.

    View full text