Geometric Pattern of Proximal Landing Zones for Thoracic Endovascular Aortic Repair in the Bovine Arch Variant

Fast Approximate Quantification of Endovascular Stent Graft Displacement Forces in the Bovine Aortic Arch Variant

PURPOSE

Displacement forces (DFs) identify hostile landing zones for stent graft deployment in thoracic endovascular aortic repair (TEVAR). However, their use in TEVAR planning is hampered by the need for time-expensive computational fluid dynamics (CFD). We propose a novel fast-approximate computation of DFs merely exploiting aortic arch anatomy, as derived from the computed tomography (CT) and a measure of central aortic pressure.

MATERIALS AND METHODS

We tested the fast-approximate approach against CFD gold-standard in 34 subjects with the "bovine" aortic arch variant. For each dataset, a 3-dimensional (3D) model of the aortic arch lumen was reconstructed from computed tomography angiography and CFD then employed to compute DFs within the aortic proximal landing zones. To quantify fast-approximate DFs, the wall shear stress contribution to the DF was neglected and blood pressure space-distribution was averaged on the entire aortic wall to reliably approximate the patient-specific central blood pressure. Also, DF values were normalized on the corresponding proximal landing zone area to obtain the equivalent surface traction (EST).

RESULTS

Fast-approximate approach consistently reflected (r2=0.99, p<0.0001) the DF pattern obtained by CFD, with a -1.1% and 0.7° bias in DFs magnitude and orientation, respectively. The normalized EST progressively increased (p<0.0001) from zone 0 to zone 3 regardless of the type of arch, with proximal landing zone 3 showing significantly greater forces than zone 2 (p<0.0001). Upon DF normalization to the corresponding aortic surface, fast-approximate EST was decoupled in blood pressure and a dimensionless shape vector (S) reflecting aortic arch morphology. S showed a zone-specific pattern of orientation and proved a valid biomechanical blueprint of DF impact on the thoracic aortic wall.

CONCLUSION

Requiring only a few seconds and quantifying clinically relevant biomechanical parameters of proximal landing zones for arch TEVAR, our method suits the real preoperative decision-making process. It paves the way toward analyzing large population of patients and hence to define threshold values for a future patient-specific preoperative TEVAR planning.

Teaching vascular anatomy: the anatomy we know, the anatomy we see or the anatomy we need?

PURPOSE

This article presents the evolution of the subject of vascular anatomy and discusses the associated clinical applicability.

METHODS

Clinically-driven surgical examples met in our everyday practice were used to depict characteristic anatomical paradoxes raised by the inconsistencies between classical anatomical perception, current imaging modalities and modern surgical techniques.

RESULTS

Consequent anatomy-driven modifications of medical devices comprise a characteristic example of the vivid, modern and meaningful role that anatomy can have on clinical decision-making and improvement of technical and clinical success. Clinical experience provides a feedback that shifts the focus of anatomic research towards new fields of interest, such as the role of arterial collateral networks as therapeutic targets. The clinical feedback brings into light queries and issues where traditional anatomical answers may be vague and inadequate to apply, thereby necessitating further research, refinement and reevaluation.

CONCLUSION

While the traditional teaching of vascular anatomy is based on information and illustrations derived from dissection and prosection courses, the development of modern imaging technologies applied in large numbers of living patients and application of minimally invasive techniques challenge our understanding of what should be perceived as fixed and permanent. Moreover, the recently introduced evidence-based philosophy in anatomy elaborate more robust data which not only update, validate and enrich the existing knowledge of anatomical variations but also enable subgroup analyses with respect to race, age and sex, identifying specific anatomic features associated with a significant impact on patient treatment.

Blunt Thoracic Aortic Injury

Blunt thoracic aortic injury (BTAI) is a potentially fatal condition that needs prompt recognition and expedited management. Clinical manifestations of BTAI are not straight forwarding and may be misdiagnosed. The grade of aortic injury is an important determinant of perioperative mortality and morbidity, as well as the indication of treatment, along with the presence of concomitant lesions of other involved organs. The mainstay of treatment nowadays for hemodynamically stable patients who survive the trauma scene is represented by delayed endovascular repair whenever anatomically and clinically feasible. Endovascular repair, in fact, is burdened by lower perioperative mortality and morbidity rates if compared to open surgical repair, but concerns remain about the need for long-term surveillance and radiation exposure in patients who are at a younger age than patients treated for the aneurysmal disease. The aim of the paper is to provide an update on the diagnostic modalities and strategies of treatment for patients affected by BTAI.

Prevalence of common origin of the innominate and left carotid artery (CILCA) or bovine arch configuration in patients with blunt aortic injury

BACKGROUND

The common origin of the innominate and left carotid artery (CILCA) have been described as a risk factor for thoracic aortic diseases (dissections and aneurysms), but its relationship with traumatic pathology of the thoracic aorta is not so well known. The aim of the present study is to describe the prevalence of CILCA among patients admitted to the hospital for high-energy polytrauma with aortic injury (BTAI) compared with a control group.

METHODS

Retrospective unicenter case-control study. Cases included all patients treated with BTAI between 1999-2020. The group of controls was patients admitted in our center for high-energy polytrauma between 2012-2017. Primary endpoint was to define the prevalence of CILCA among both groups and secondary endpoint was to measure the distance between brachiocephalic trunk (BCT) or left common carotid artery (LCCA) and left subclavian artery (LSA). Results were retrospectively reviewed by two investigators.

RESULTS

49 patients in BTAI group and 248 patients in control group. With a good concordance between investigators, 21 patients with CILCA (42.9%) in the BTAI group versus 61 CILCA (24.6%) in the control group (p=0.009). The mean distance between BCT/LCCA and LSA among the cases with CILCA was 10.09 mm (SD = 2.89) and 7.48 mm (SD = 3.65) among cases with standard aortic arch (p = 0.010).

CONCLUSIONS

In the present study we found that CILCA configuration is more prevalent in patients with BTAI and the distance to left subclavian artery is longer.

A Deep Learning-Based and Fully Automated Pipeline for Thoracic Aorta Geometric Analysis and Planning for Endovascular Repair from Computed Tomography

Feasibility assessment and planning of thoracic endovascular aortic repair (TEVAR) require computed tomography (CT)-based analysis of geometric aortic features to identify adequate landing zones (LZs) for endograft deployment. However, no consensus exists on how to take the necessary measurements from CT image data. We trained and applied a fully automated pipeline embedding a convolutional neural network (CNN), which feeds on 3D CT images to automatically segment the thoracic aorta, detects proximal landing zones (PLZs), and quantifies geometric features that are relevant for TEVAR planning. For 465 CT scans, the thoracic aorta and pulmonary arteries were manually segmented; 395 randomly selected scans with the corresponding ground truth segmentations were used to train a CNN with a 3D U-Net architecture. The remaining 70 scans were used for testing. The trained CNN was embedded within computational geometry processing pipeline which provides aortic metrics of interest for TEVAR planning. The resulting metrics included aortic arch centerline radius of curvature, proximal landing zones (PLZs) maximum diameters, angulation, and tortuosity. These parameters were statistically analyzed to compare standard arches vs. arches with a common origin of the innominate and left carotid artery (CILCA). The trained CNN yielded a mean Dice score of 0.95 and was able to generalize to 9 pathological cases of thoracic aortic aneurysm, providing accurate segmentations. CILCA arches were characterized by significantly greater angulation (p = 0.015) and tortuosity (p = 0.048) in PLZ 3 vs. standard arches. For both arch configurations, comparisons among PLZs revealed statistically significant differences in maximum zone diameters (p < 0.0001), angulation (p < 0.0001), and tortuosity (p < 0.0001). Our tool allows clinicians to obtain objective and repeatable PLZs mapping, and a range of automatically derived complex aortic metrics.

Feasibility and Safety of Cerebral Embolic Protection Device Insertion in Bovine Aortic Arch Anatomy

Background: Cerebral embolic protection devices (CEPDs) have emerged as a mechanical barrier to prevent debris from reaching the cerebral vasculature, potentially reducing stroke incidence. Bovine aortic arch (BAA) is the most common arch variant and represents challenge anatomy for CEPD insertion during transcatheter aortic valve replacement (TAVR). Methods: Cohort study reporting the Sentinel^TM Cerebral Protection System insertion’s feasibility and safety in 165 adult patients submitted to a transfemoral TAVR procedure from April 2019 to April 2020. Patients were divided into 2 groups: (1) BAA; (2) non-BAA. Results: Median age, EuroScore II, and STS score were 79 years (74–84), 2.9% (1.7–6.2), and 2.2% (1.6–3.2), respectively. BAA was present in 12% of cases. Successful two-filter insertion was 86.6% (89% non-BAA vs. 65% BAA; p = 0.002), and debris was captured in 95% (94% non-BAA vs. 95% BAA; p = 0.594). No procedural or vascular complications associated with Sentinel insertion and no intraprocedural strokes were reported. There were two postprocedural non-disabling strokes, both in non-BAA. Conclusion: This study demonstrated Sentinel insertion feasibility and safety in BAA. No procedural and access complications related to Sentinel deployment were reported. Being aware of the bovine arch prevalence and having the techniques to navigate through it allows operators to successfully use CEPDs in this anatomy.

Computational Fluid Dynamics Modeling of Proximal Landing Zones for Thoracic Endovascular Aortic Repair in the Bovine Arch Variant

BACKGROUND

To assess the endograft displacement forces (DF), which quantify the forces exerted by the pulsatile blood flow on the vessel wall and transmitted on the terminal fixation site of the endograft after its deployment in proximal landing zones (PLZs) of the bovine aortic arch variant.

METHODS

Thirty healthy aortic computed tomographic angiographies of subjects with bovine arch configuration (10 per type of arch, I-III) were selected for the purpose of the study. A 3-dimensional model of the aortic arch lumen was reconstructed. Computational fluid dynamics modeling was then used to compute DF magnitude and orientation (i.e., x, y, and z axes) in PLZs of each case. DF values were normalized to the corresponding aortic wall area to estimate equivalent surface traction (EST).

RESULTS

DF were highest in zone 0, consistently with the greater surface area. DF in zone 3 were much greater than in zone 2 because of a 3-fold greater upward component (z axis) (P < 0.001), being therefore mainly oriented orthogonally to the aortic blood flow and to the vessel longitudinal axis in that zone. EST progressively increased from zone 0 toward more distal PLZs, with EST in zone 3 being much greater than that in zone 2 (P < 0.001). The same pattern was observed after stratification by type of arch.

CONCLUSIONS

The bovine arch is associated with a consistent fluid dynamic pattern, which identifies in zone 3 an unfavorable biomechanical environment for endograft deployment.