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Assessment of Aortic Aneurysm Rupture Risk and Surgical Repair
37
the hyperelastic response of AAA tissue [He and Roach 1994, Raghavan et al. 1996,
Thubrikar et al. 2001a, Xiong et al. 2008]. Furthermore, while Raghavan et al. 1996 and
Raghavan and Vorp 2000 concluded that AAA tissue was essentially isotropic, other studies,
such as that of Thubrikar et al. 2001a (uniaxial tensile tests) and Vande Geest et al. 2006b
(biaxial tensile tests), demonstrated the heterogeneous, anisotropic behavior of AAAs, where
they found the longitudinal direction to be more distensible than the circumferential direction
and the posterior region to be more distensible than the anterior or lateral regions.
As with wall strength, the patient-specific AAA stress-strain behavior cannot be
determined without extracting tissue samples. Thus, like Vande Geest et al. 2006a, Reeps et
al. 2012 proposed using easily accessible in vivo parameters to determine individual AAA
properties. In a first step towards achieving this goal, they used data from 50 AAAs to
determine which of 43 chosen parameters could be used for this purpose. Of these, the
parameter which exhibited the most significant correlation with AAA wall properties was the
Standardized Uptake Value (SUV) which is a measure of the metabolic activity in the wall.
While such investigations have shown promise, much research needs to be done to find an
accurate patient-specific property model or a non-destructive in vivo measurement technique.
Until then, use of material models employing tensile test data of excised tissue remains the
best available method for specifying properties in wall stress analyses.
One such model is that of Raghavan and Vorp 2000 who proposed an isotropic,
hyperelastic AAA-wall material model which has become one of the most popular in AAA
biomechanics studies. In this work, they also demonstrated that the use of population-
averaged parameters provided sufficient accuracy for wall stress predictions, eliminating the
need for patient-specific wall properties. Using the uniaxial tensile test data of their previous
work [Raghavan et al. 1996], their strain-energy-function (SEF) is expressed as:
(2)
where = 0.174 MPa, = 1.881 MPa (the population-averaged parameters) and
is the first
invariant of the Left Cauchy-Green tensor. While this model is easier to implement, its
limitation is the assumption of isotropy. As a result, several anisotropic models have been
recommended.
One of the most cited anisotropic models is the one proposed by Holzapfel et al. 2000, in
modified form as:
(3)
where the parameters were determined using the uniaxial tensile data of Raghavan et al. 1996
in one case: c
10
= 110 kPa, k
1
= k
3
= 210 kPa, k
2
= k
4
2009]. A modified version of this model was later proposed by Rodriguez et al. 2009:
(4)
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where the parameters were found using the biaxial tensile data of Vande Geest et al. 2006b
(
,
,
, = 0.14,
,
). Alternatively, Vande Geest et al. 2006b, who were the first to perform biaxial tensile
tests on nonaneurysmal and aneurysmal abdominal aortic specimen, employed a modified
Fung model by Choi and Vito 1990 to characterize the anisotropic hyperelastic behavior that
they found for these tissues:
(5)
where for nonaneurysmal AAs:
= 0.320 kPa,
= 141.1,
= 143.3,
= 127.9; and, for
AAAs:
= 0.140 kPa,
= 477.0,
= 416.4,
= 408.3. Basciano and Kleinstreuer 2009
proposed an anisotropic model, based on the data by Vande Geest et al. 2006b, which can be
readily used in commercial solid-structure software (e.g., ANSYS
®
):
(6)
For nonaneurysmal AAs: = 74.345 kPa, = 3300 kPa,
AAAs: = 73.996 kPa, = 65500 kPa,
Although these SEFs are intended to capture the same material behavior, several studies
have shown that they can produce significantly different results [Vande Geest et al. 2008,
Rodriguez et al. 2008, Rissland et al. 2009, Rodriguez et al. 2009]. As an example, the
Raghavan-Vorp model has been shown to estimate lower stresses and displacements than its
anisotropic counterparts [Vande Geest et al. 2008, Rissland et al. 2009]. Furthermore, even
among the various anisotropic models, results are often quite different [Rodriguez et al.
2009]. Thus, more research is required to determine the best model for wall stress analyses.
2.3.4. Intraluminal Thrombus and Calcifications
The effects of intraluminal thrombus (ILT) and calcifications on AAA-rupture risk have
been the subject of much research and speculation over the last two decades. In some patient-
specific cases, thrombi can be quite beneficial by forming a new blood conduit (as depicted in
Figure 1) and cushioning the thinning AAA-wall. As one of the first of such studies, Inzoli et
al. 1993 measured ILT and calcifications from in vivo images of 30 AAAs and investigated
the effects of both pathologies using static stress analyses in idealized, axisymmetric AAA
models. From their computational work, they found that ILT can reduce wall stress but
calcifications or dissecting ILT can increase it. In Li et al. 2008, the effects of ILT and
calcifications on wall stress were investigated in 20 patient-specific AAAs by performing
static stress analyses in the actual configuration and for cases without calcifications or
without ILT. As in Inzoli et al. 1993, they found that calcifications increased wall stress while
ILT decreased it. While the amount of ILT affected the decrease in wall stress, the location
rather than the amount of calcifications was more influential on the wall stress. In contrast to
these findings, Schurink et al. 2000 did not observe decreased pressure near the wall with the
presence of ILT in 9 AAAs and, thus, concluded that the presence of thrombus does not
reduce rupture risk.
Of importance in these studies are the material properties of these pathologies. In Vorp et
al. 1996b, in vivo measurements demonstrated that the lumen (surrounded by ILT) was more
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Assessment of Aortic Aneurysm Rupture Risk and Surgical Repair
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distensible than the wall and that the ILT area did not change throughout the pulse. Thus, they
concluded that ILT is incompressible and that it could possibly act as a cushion to protect the
wall from the fluid pressures. Examining the ILT in more detail, Adolph et al. 1997 found a
network of canaliculi in which cells and macromolecules can penetrate the ILT. They also
found increased fibrin degradation from its luminal to abluminal surface. Later, Fontain et al.
2002 found that ILT traps platelets, cells, and plasma constituents and, thus, can emit
proteases which may play a role in AAA evolution. Basciano et al. 2011 developed a
validated computer simulation model for patient-specific ILT-onset, focusing on enhanced
platelet and WBC residence times as well as their elevated surface-shear loads in near-wall
regions of the AAA-sac.
Wang et al. 2001 studied the mechanical properties and composition of ILT specimens
from 14 AAAs and found ILT to be an isotropic, nonlinear material consisting of three layers
(luminal, medial, and abluminal) having distinct properties (i.e., luminal region is stiffer than
medial region). In Vande Geest et al. 2006c, biaxial tensile tests on ILT specimens from 9
AAAs confirmed the isotropy, and a second order, polynomial function was proposed to
model its behavior. Alternatively, van Dam et al. 2008 proposed a model to capture the
nonlinear, viscoelasticity of ILT. Because no significant differences or patterns in the
viscoelastic properties of the three layers of the ILT or between individual AAAs, they
concluded that one set of viscoelastic model parameters could be used. Recently, Tong et al.
2011 found that the luminal layer of the ILT can exhibit anisotropy and that the strength
decreased from the luminal to abluminal layers. On the other hand, fresh ILT were composed
of one layer which behaved similar to the abluminal layer of old ILT. The stiffness of the ILT
increased with its age, and increased ILT thickness was related to increased ILT age.
Similarly, studying the static and pulsatile failure properties of the three layers of ILT, Gasser
et al. 2008 found that the strength of the ILT was the greatest near the lumen and decreased
towards the wall. However, when pulsatile conditions were applied, ILT failed well below
these strengths. They postulated that, if this occurs, a stress concentration may develop in the
AAA wall behind the failed ILT, which can lead to rupture. This was demonstrated in Polzer
et al. 2011 who found that fissures in ILTs of 4 unruptured AAAs increased AAA wall stress
behind the crack by as much as 30%. However, due to the localization of this effect, peak
wall stress was not significantly increased.
Using static stress analyses in idealized, axisymmetric AAAs with ILT of various sizes,
shapes, and material properties, Mower et al. 1997 found that ILT does provide a cushioning
effect. As expected, increasing the ILT stiffness decreased this stress reduction while
increasing the ILT size increased it. Using the ILT material model proposed in Wang et al.
2001, Wang et al. 2002 performed static stress analyses in 4 patient-specific AAAs, and, as
Inzoli et al. 1993, they found that the ILT decreased wall stresses while also affecting its
distribution. Also using this model, Di Martino and Vorp 2003 studied the effects of ILT
material parameters on wall stress in idealized AAA-geometries and concluded that
population-averaged parameters could provide adequate stress predictions for individual
AAAs. As before, a reduction in wall stress was observed with the presence of an ILT. Based
on fluid-structure interaction (FSI) simulations, assuming steady blood flow in patient-
specific AAAs with and without ILT, Bluestein et al. 2009 also found the ILT to decrease
wall stress and alter the stress distribution. Also, as expected, they found that the ILT
significantly influenced the blood flow patterns and wall shear stress distributions. Reeps et
al. 2010 and Xenos et al. 2010a also demonstrated that the ILT provided a cushioning effect.
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In addition to the possible cushioning or stress concentration effects, another way that
ILT can affect AAA rupture risk is by weakening the underlying AAA wall. In the seminal
work by Vorp et al. 2001, evidence of local hypoxia and wall weakening were found in
regions of thick ILT. Thus, they suggested that the presence and size of ILT may increase
rupture risk. Further, Kazi et al. 2003 found that the AAA wall adjacent to ILT was thinner
and contained less elastin fibers and smooth muscle cells but more inflammatory cells than
AAA wall without ILT. In Tong et al. 2011, increased ILT age was also correlated with wall
weakening, and the ILT-covered AAA wall was found to be anisotropic with a mean strength
of 35.66 ± 5.47 N/cm
2
. From histological studies, they found that the ILT was composed of
red blood cells, homogenized proteins, and thin and thick fibrin network bundles. In the ILT-
covered AAA wall, elastic fibers and muscle cells were absent while more collagen fibers
were detected.
Other studies that suggest increased rupture risk with the presence of ILT include Wolf et
al. 1994 who found that the arc length of the wall covered by thrombus (TARC) was a good
predictor of aneurysm expansion and Stenbaek et al. 2000 who found that ILT growth was
significantly higher in those which experienced eventual rupture. Using the isotropic models
for AAA wall and ILT of Raghavan and Vorp 2000 and Vande Geest et al. 2006c,
respectively, and the non-invasive strength model of Vande Geest et al. 2006a, Gasser et al.
2010 performed static stress analyses in 20 ruptured and 30 unruptured AAAs. They
considered four cases in which peak wall stress and peak wall rupture risk (PWRR; same as
RPI) were compared: (1) constant wall thickness of 2.0 mm with no ILT, (2) constant wall
thickness considering p (MAP) with no ILT, (3) variable wall
thickness considering ILT locations with presence of ILT, and (4) variable wall thickness
ases which included ILT
provided better differentiation between the ruptured and unruptured groups. Like Maier et al.
2010a, they too found the PWRR to better distinguish between the ruptured and unruptured
groups. However, unlike previous studies, they found that increased ILT volume was
associated with increased PWS and PWRR.
Like ILT, calcifications can also affect the rupture-risk of AAAs. Zarins et al. 2001, who
studied 30 aortas from autopsy, found increased amounts of plaque in larger abdominal aortas
which were associated with decreased media thickness. From static stress analyses in 6
patient-specific AAAs, Speelman et al. 2007 found that calcifications increased wall stress in
the surrounding tissue and that stiffness of the calcifications had a significant effect on the
wall stress. Reeps et al. 2010 confirmed these findings. On the other hand, Maier et al. 2010b
found that calcifications reduced wall stresses in the surrounding tissue. This discrepancy
may be explained by the differences in the applied models or by the observation of Xenos et
al. 2010a. Based on FSI analyses of 2 patient-specific ruptured AAAs, they concluded that
small calcifications increased wall stresses while large ones decreased them.
In conclusion, the presence of ILT and calcification has the potential to significantly
affect AAA rupture-risk, i.e., either improving or worsening it, depending on the dominant
impact of AAA-wall cushioning or wall deterioration, associated with low or high wall-stress
levels. Thus, further studies should be performed to more accurately determine their effects as
well as how to model the impact. Regardless, the previous work has shown that both ILT and
calcification should be included when assessing AAA rupture potential.
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2.3.5. Geometric Parameters
From solid mechanics, we know that geometry can play a major role in the stresses of a
structure. Because of this and the ease in obtaining in vivo geometric parameters, several
studies have examined the correlation between AAA geometry and wall stresses, leading to
rupture. Some of the first of these were performed on idealized AAA geometries. For
example, Inzoli et al. 1993 explored the effects of maximum AAA diameter on wall stress by
performing finite element analyses in simple, axisymmetric AAA models. In Vorp et al. 1998,
static stress analyses were performed in ten idealized AAAs to study the effects of diameter
and asymmetry. They concluded that the level of AAA asymmetry had as much of an effect
on wall stress distribution and magnitude as the maximum diameter. Scotti et al. 2005, 2008
and Rodriguez et al. 2008 confirmed these findings in their fluid-structure interaction and
static stress analyses of idealized AAAs. This was further confirmed by Doyle et al. 2009
who performed static stress analyses in 15 asymmetric patient-specific AAAs and one
reconstructed symmetric AAA (Patient II) and found that the asymmetry of Patient II
increased the peak wall stress by 48%. In Raghavan et al. 2000, seven patient-specific AAAs
with diameters ranging from 5.2 to 6.4 cm and a healthy abdominal aorta (control) with a
diameter of 2.0 cm were investigated with static stress analyses. As expected, the unusual
shapes of the AAAs produced complex stress distributions and values of peak wall stresses
ranging from 29 to 45 N/cm
2
while the healthy abdominal aorta exhibited a more uniform
stress distribution with a peak wall stress of 12 N/cm
2
. They also found that AAA volume had
a better correlation with peak wall stress than the maximum diameter. Alternatively,
Giannoglou et al. 2006 found that the mean centerline curvature was the best geometric
predictor of PWS in their analyses of 39 patient-specific AAAs while Georgakarakos et al.
2010 concluded that centerline tortuosity and maximum diameter were the best predictors of
PWS in their analyses of 19 patient-specific AAAs. Considering fluid-structure interactions,
Xenos et al. 2010b assessed the effects of inlet and iliac angles on the wall stress of idealized
AAAs. They found that PWS was highest at an inlet angle around 20° and slightly decreased
with increasing inlet angle. For increasing iliac angles, PWS generally increased. Specifically,
the maximum wall shear stress peaked at an iliac bifurcation angle of 60° and decreased for
larger angles. Martufi et al. 2009 proposed some additional geometric parameters which may
be useful in predicting rupture. Comparing ruptured and unruptured AAAs, Fillinger et al.
2004 studied the differences in geometric characteristics and found the ruptured ones to be
less tortuous but more asymmetric than the unruptured ones. They also found the average
diameter of ruptured AAA for women was 5 mm smaller than that for men. Kleinstreuer and
Li 2006 developed a multicomponent model (with accompanying computer program) to
predict AAA-rupture risk, considering not only maximum diameter and annual growth rate
but also peak wall stress (estimated with a semi-empirical formula), change in wall stiffness,
ILT size, and aneurysm shape (see Figure 2). Comparing the model predictions to
computational and experimental studies from the open literature, they concluded that the
model provided a good tool for identifying whether small AAAs are at risk for rupture.
Focusing on AAA wall thickness, Thubrikar et al. 2001a found that the thickness
decreased from the posterior to the anterior regions, and, in a separate analysis, they found
that increasing wall thickness decreased the wall stress but kept its distribution relatively the
same [Thubrikar et al. 2001b]. This was confirmed in the fluid-structure interaction analyses
of Scotti et al. 2005, 2008 who found that decreasing wall thickness resulted in increasing
wall stresses. Raghavan et al. 2006a measured wall thickness in 4 patient-specific AAAs (3
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42
unruptured and 1 ruptured) and found that wall thickness decreased near the rupture site in the
ruptured AAA. Contrary to the findings of Thubrikar et al. 2001a, they found that the
posterior-left regions were thinner than the anterior-left regions. Also, they found that the
plaque regions were thicker than the non-plaque regions. As reported, the thickness varied
significantly throughout the AAAs, where the thickness of
program for rupture-risk prediction of abdominal aortic
aneurysms
Figure 2. Schematics for a Predictive AAA-Rupture Risk Model (see Kleinstreuer and Li (2006) for a
patient-specific computer program, evaluating risk factors based on maximum AAA diameter, annual
growth rate, peak wall stress, change in wall stiffness, ILT size, and aneurysm shape).
the neck was larger than that in the AAA. Experimentally measuring wall shear stress in a
flexible silicone model of a healthy aorta, Bonert et al. 2003 found that regions of intimal
thickening correlated with regions of low wall shear stress in 10 autopsied aortas. Astrand et
al. 2005 found that the intima-media thickness increased with increasing age. In addition to
proposing several geometric parameters for rupture-risk assessment, Martufi et al. 2009 also
validated a method for detecting patient-specific wall thickness, which was further validated
by Shum et al. 2010. Comparing 10 ruptured and 10 unruptured AAAs, Shum et al. 2010
found that the wall thickness was significantly increased in the ruptured ones. However,
Raghavan et al. 2011 found that the wall thicknesses of ruptured and unruptured AAAs were
not significantly different and there were no patterns in wall thickness around the
circumference of the AAA. Furthermore, the thickness did not decrease near the ruptured
region. Reeps et al. 2012 found that wall thickness increased with increasing FDG-metabolic
activity, thrombocyte counts, AAA maximum diameter, and 1
st
principle stress and in the
presence of diabetes and smoking, while thickness decreased with increasing local thrombus
thickness and in the case of kidney disease. Given the high variability in wall thickness, more
research needs to be accomplished to either further validate the method used in Martufi et al.
2009 and Shum et al. 2010 or propose another more accurate technique for detecting wall
thickness.
1250630
519850012 30
max
max
6801
0060
..
d.p..
βt
eα.
.[MPa]σ
AAA ,sys
Semi-empirical Peak Wall Stress Formula:
where,
sys
p
= systolic pressure [mmHg]
max,AAA
d
= maximum AAA diameter [cm]
a
aAAA
l
ld
max,
= symmetry index
max,
max,
AAA
ILT
A
A
= area ratio
a
l
= distance from center O to posterior
t
= thickness of AAA wall at
max,AAA
d
max,ILT
A
= transverse area of ILT at
max,AAA
d
max,AAA
A
= transverse area of AAA at
max,AAA
d
Onset of AAA
Renal artery
d
AAA,max
-level lumen area
d
AAA,max
- level intra-luminal
thrombus (ILT) area
H
t
Infrarenalartery (dashed line)
projected onto the d
AAA,max
plane
a
l
p
l
max,AAA
d
AAA neck
Iliac bifurcation
O
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2.3.6. Improving Wall Stress Analyses
Much of the latest AAA biomechanics research has focused on improving the accuracy of
wall stress predictions, particularly by applying more advanced boundary conditions and
including appropriate pre-stresses. For example, following the contributions by Scotti et al.
2008 and Liu et al. 2007 who demonstrated that external loads can significantly decrease the
resulting AAA wall stress, studies like Moireau et al. 2012 have proposed methods for more
accurately applying the external tissue support which enables the computational model to
better mimic the in vivo behavior.
Fluid-structure interaction (FSI) is another example of more advanced boundary
conditions. Some of the first FSI analyses in patient-specific AAAs were performed by Di
Martino et al. 2001 and Wolters et al. 2005. While these models are more realistic, the
computational cost can be quite high. Thus, several studies have compared FSI predictions to
less expensive static analyses. In a study by Leung et al. 2006, it was concluded that the
pressure differences that occurred throughout the cardiac cycle were not large enough to
produce enough stress variation to deem FSI simulations necessary. However, in a study by
-structure analysis in a patient specific model
was compared to a corresponding static analysis. They found that the difference in peak wall
stress was as much as 12.5%, making the inclusion of FSI necessary. However, the magnitude
and not the location was influenced by the modeling approach. Scotti and Finol 2007
confirmed these findings when they found that their FSI simulations produced 5-25% higher
peak wall stress than their solid stress analyses. Later, the same group confirmed that
inclusion of FSI increased wall stress by up to 20% [Scotti et al. 2008]. Similarly, Dorfmann
et al. 2010 used non-uniform pressure measurements from an experimental polymer AAA
model to apply to the corresponding computational model and found that the resulting
maximum principle stress was higher than when a uniform pressure was applied. More
recently, Crosetto et al. 2011 also validated the necessity of FSI when they noted the wall
shear stress differences between the FSI and rigid wall simulations in a model of an aorta.
Thus, the influence of FSI should be considered for most accurate AAA stress predictions.
Because the patient-specific AAA geometries obtained from in vivo imaging are already
in a stressed state, methods are needed to determine the zero-pressure geometry or pre-
stressed state of these configurations for realistic wall-stress predictions. Raghavan et al.
2006b proposed finding the AAA zero-pressure geometry by: (1) applying pressure to the in
vivo geometry and obtaining the displacements; (2) subtracting a scaled version of these
displacements; (3) reapplying the pressure and determining the difference in the resulting
geometry with the original in vivo geometry; and (4) adjusting the scale of the displacements
until the minimum difference between the geometries is achieved. Using this methodology in
3 patient-specific AAAs, they concluded that it can provide an adequate approximation. The
same group validated an alternative method using inverse elasto-statics that was proposed by
Govindjee and Mihalic 1996, 1998 in a patient-specific AAA and demonstrated that
consideration of the initial geometry decreased the wall stress prediction [Lu et al. 2007]. A
backward incremental method based on the work by van Oijen 2003, van de Vosse et al.
2003, and de Hart et al. 2003 to determine the initial geometry of 3 patient-specific AAAs
was validated by de Putter et al. 2007. Unlike Lu et al. 2007, they found that consideration of
the initial geometry increased the wall-stress prediction. Gee et al. 2009 and Gee et al. 2010
applied a modified updated Lagrangian formulation (MULF) which approximates the pre-
stressed state rather than the stress-free geometry, which was compared to the inverse elasto-
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statics method in patient specific AAAs. They concluded that the MULF method may be
better than the inverse method because it provides a unique solution and avoids buckling to
achieve good approximations. They too indicated that consideration of the initial geometry or
pre-stress configuration provides decreased wall stress and deformation, which was also
found by Reeps et al. 2010. Thus, these studies confirm that the zero-pressure geometry or
pre-stress should be included in AAA stress analyses.
2.3.7. Growth and Remodeling
In addition to their mechanical behavior, AAAs also exhibit mechano-biological
behavior. It is the biological response of the tissue constituents to mechanical loads which
leads to growth and remodeling of the biological structure. Watton et al. 2004 was one of the
first to propose a model for AAA growth. After initiation of elastin degradation, this model
accounts for collagen turnover and deposition with two linear differential remodeling
equations. Furthermore, the collagen recruitment and fiber density were included in the
material model by modifying the media and adventitia strain energy density functions of
Holzapfel et al. 2000 with 4 recruitment and 4 fiber density variables. Comparing their
computational analyses of idealized configurations to in vivo deformations, they found good
agreement. However, because the growth rates of AAAs are highly dependent on the model
parameters, much work still needs to be done before this model can be used on a patient-
specific basis [Watton and Hill 2009]. More recently, Figueroa et al. 2011 proposed a method
of incorporating both fluid-structure interaction as well as growth and remodeling in order to
account for the effects of wall-shear stress on turnover of collagen and smooth muscle, while
Sheidaei et al. 2011 used this technique in an AAA. Using growth and remodeling techniques,
Wilson et al. 2012 showed that the initial state of a non-aneurysmal aorta can be significant in
the progression of AAAs. Zenali-Davarani et al. 2012 used some of the most recent advances
in growth and remodeling techniques to study the effects of variable spatio-temporal elastin
degradation on the mechanics of AAAs while Marini et al. 2012 proposed a damage model
for AAAs which can act as a bridge between the mechanical and biological response of AAA
tissue.
3. AAA REPAIR OPTIONS
Repair options for patients deemed at risk for rupture include either traditional open
surgical repair or endovascular aneurysm repair. Open surgical repair, which has not changed
significantly in the past two decades, involves opening the abdomen and AAA sac, removing
any ILT, surgically implanting a graft to provide structural support, and closing the sac wall
tissue over the graft. Polyester-based bifurcation grafts are often used with a configuration
that provides a body length of at least 5 cm before the bifurcation into the limbs that provide
perfusion to the lower extremities. This allows for future secondary procedures that are
generally more complicated to be accomplished via endovascular means. Due to the highly
invasive nature of this procedure, a week-long hospital stay and a recovery period of several
months are often required.
In endovascular aneurysm repair (EVAR), a stent-graft is implanted in the AAA region
via catheter deployment. This procedure is minimally invasive and requires significantly less
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Assessment of Aortic Aneurysm Rupture Risk and Surgical Repair
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recovery time than open repair. Through computational simulations in an idealized AAA with
an implanted stent-graft, Li and Kleinstreuer 2005 demonstrated that implanted stent-grafts
can significantly decrease the AAA sac pressure and, consequently, the maximum wall stress
(see Figure3). Kleinstreuer et al. 2007 reviewed additional stented AAA results based on
fluid-structure interaction simulations. Kwon et al. 2011 later simulated growth and
remodeling in an idealized AAA with an implanted stent-graft and found that lower sac
pressure can cause the AAA to shrink.
Source: Li, Z., & Kleinstreuer, C. (2005). Blood flow and structure interactions in a stented abdominal
aortic aneurysm model. Med Eng Phys , 27, 369-382.
Figure 3. Treatment Effectiveness of Endovascular Aneurysm Repair.
However, lifelong surveillance is recommended due to the possibility of developing
endoleaks or stent-graft migration in the long-term [Figueroa and Zarins 2011, England et al.
2012]. Endoleaks occur when blood flow penetrates the aneurysm sac despite the presence of
the stent-graft. Such leaks are typically classified into four types depending on the location
and cause of the leak [White et al. 1997]:
Type I endoleaks are caused by improper fitting or contact with the wall at the
proximal or distal ends of the stent-graft;
Type II endoleaks are caused by flow reversal from side-branches;
Type III endoleaks are caused by stent-graft failure; and
Type IV endoleaks are caused by graft fabric porosity.
Using computational fluid-structure interaction simulations in an idealized, stented AAA,
Li and Kleinstreuer 2006a demonstrated that Type I endoleaks can expose the weakened
AAA wall to physiologic pressures that can lead to rupture. In a separate study, Li and
Kleinstreuer 2006b also studied Type II endoleaks for an idealized, stented AAA and found
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that the AAA sac pressure depended on the inlet pressure of the side-branch. Wolters et al.
2010 used lumped parameter models and fluid-structure interaction analyses to study the
effects of endoleak and stent-graft stiffness on sac pressure in an idealized AAA model. They
found that decreased endoleak resistance and stent-graft stiffness resulted in decreased sac
pressure; but, increased resistance with decreased stiffness resulted in increased pressures. As
demonstrated in the growth and remodeling simulation of Kwon et al. 2011, these higher
pressures can lead to continued AAA expansion. Computational studies have also been
performed to determine the risk factors for stent-graft migration. These studies have found
that the geometric characteristics of the AAA, such as neck and iliac bifurcation angle, the
stent- flow can affect the magnitude of the migration force
[Li and Kleinstreuer 2006c, Molony et al. 2010]. Recently, simulations have demonstrated the
three-dimensional stent-graft motion as a result of the migration force [Figueroa et al. 2010]
and the significant impact of the in vivo stent-graft configuration on the hemodynamics inside
the stent-graft [Shek et al. 2012].
Continual improvements are being made to endovascular devices to minimize endoleaks
and stent-graft migration. Current devices typically rely upon active fixation to prevent
migration and more often contain a fixation mechanism that secures the devices to the supra-
renal region of the aorta. The decision on which device to use in a particular patient is
. Certain
devices have been designed to better manage aortic tortuosity and angulation that can occur
from aortic elongation during AAA development. Other device designs have focused on
lower profile delivery systems so that patients with concomitant occlusive disease can
undergo repair with reduced risks of injury to the access vessels.
There have been several large randomized controlled trials comparing EVAR with open
surgical repair. The EVAR-1, DREAM, and OVER trials showed lower 30-day mortality in
patients undergoing EVAR [Greenhalgh et al. 2004, Prinssen et al. 2004, and Stroupe et al.
2012]. It should be noted that the first two studies began enrollment nearly a decade ago.
Since that time additional device advances, technique improvement, and understanding in
management of patients have occurred. The ACE trial, a prospective trial of low-to-medium
risk patients, showed equivocal peri-operative mortality [Becquemin et al. 2011]. One
significant criterion for involvement in the ACE trial was expertise in both EVAR and open
repair. Based upon several articles [Birkmeyer et al. 2002, Dimick et al. 2003, Holt et al.
2009], including the ACE trial manuscript, this is generally defined as > 30 cases/year.
Currently most vascular specialists outside of major aortic centers rarely perform this volume
of cases [Eidt et al. 2011]. It therefore calls into question the applicability of this approach to
low-volume centers. Most practicing physicians will only see a few young patients a year
afflicted with aneurysmal disease; therefore their outcomes would most likely not be the same
as those conducted in the ACE trial. The results of the ACE trial are only valid when
comparing outcomes at large aortic centers where greater than 30 cases per year are
performed. This is further supported by the fact that none of the aforementioned EVAR trials
showed a statistical difference in midterm to long-term all-cause mortality rates between
EVAR and open repair [Stroupe et al. 2012, Becquemin et al. 2011, United Kingdom EVAR
Trial Investigators 2010, De Bruin et al. 2010]. Thus it is beneficial and logical for the
practicing physician in low-volume centers to perform the procedure he/she is most familiar
and
experience paradigm [Eidt et al. 2011].
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