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BRIEF RESEARCH REPORT
published: 08 September 2020
doi: 10.3389/fphys.2020.574211
Edited by:
Boyce Griffith,
University of North Carolina at Chapel
Hill, United States
Reviewed by:
Renee Miller,
King’s College London,
United Kingdom
Hao Gao,
University of Glasgow,
United Kingdom
*Correspondence:
Julius M. Guccione
Julius.Guccione@ucsf.edu
†Present address:
Kevin L. Sack,
Division of Biomedical Engineering,
Department of Human Biology,
University of Cape Town, Cape Town,
South Africa
Specialty section:
This article was submitted to
Computational Physiology
and Medicine,
a section of the journal
Frontiers in Physiology
Received: 19 June 2020
Accepted: 19 August 2020
Published: 08 September 2020
Citation:
Wisneski AD, Wang Y, Deuse T,
Hill AC, Pasta S, Sack KL, Yao J and
Guccione JM (2020) Impact of Aortic
Stenosis on Myofiber Stress:
Translational Application of Left
Ventricle-Aortic Coupling Simulation.
Front. Physiol. 11:574211.
doi: 10.3389/fphys.2020.574211
Impact of Aortic Stenosis on
Myofiber Stress: Translational
Application of Left Ventricle-Aortic
Coupling Simulation
Andrew D. Wisneski1, Yunjie Wang2, Tobias Deuse1, Arthur C. Hill1, Salvatore Pasta3,
Kevin L. Sack4†, Jiang Yao5and Julius M. Guccione1*
1Department of Surgery, University of California, San Francisco, San Francisco, CA, United States, 2Thornton Tomassetti
Lifesciences Division, Santa Clara, CA, United States, 3Department of Engineering, Universita degli Studi di Palermo,
Palermo, Italy, 4Cardiovascular Research Division, Medtronic Inc., Minneapolis, MN, United States, 5Dassault Systèmes
Simulia, Johnston, RI, United States
The severity of aortic stenosis (AS) has traditionally been graded by measuring
hemodynamic parameters of transvalvular pressure gradient, ejection jet velocity, or
estimating valve orifice area. Recent research has highlighted limitations of these
criteria at effectively grading AS in presence of left ventricle (LV) dysfunction. We
hypothesized that simulations coupling the aorta and LV could provide meaningful
insight into myocardial biomechanical derangements that accompany AS. A realistic
finite element model of the human heart with a coupled lumped-parameter circulatory
system was used to simulate AS. Finite element analysis was performed with Abaqus
FEA. An anisotropic hyperelastic model was assigned to LV passive properties, and a
time-varying elastance function governed the LV active response. Global LV myofiber
peak systolic stress (mean ±standard deviation) was 9.31 ±10.33 kPa at baseline,
13.13 ±10.29 kPa for moderate AS, and 16.18 ±10.59 kPa for severe AS. Mean
LV myofiber peak systolic strains were −22.40 ±8.73%, −22.24 ±8.91%, and
−21.97 ±9.18%, respectively. Stress was significantly elevated compared to baseline
for moderate (p<0.01) and severe AS (p<0.001), and when compared to each
other (p<0.01). Ventricular regions that experienced the greatest systolic stress were
(severe AS vs. baseline) basal inferior (39.87 vs. 30.02 kPa; p<0.01), mid-anteroseptal
(32.29 vs. 24.79 kPa; p<0.001), and apex (27.99 vs. 23.52 kPa; p<0.001). This data
serves as a reference for future studies that will incorporate patient-specific ventricular
geometries and material parameters, aiming to correlate LV biomechanics to AS severity.
Keywords: aortic stenosis, finite element method, myofiber stress, ventricular function, realistic simulation,
ventricle-aortic coupling
INTRODUCTION
Aortic stenosis (AS) is the most prevalent valvular heart disease in the developed world (Lindman
et al., 2013;Go et al., 2014;Miura et al., 2015). Without treatment by surgical aortic valve
replacement or transcatheter aortic valve replacement, AS leads to irreversible left ventricle (LV)
remodeling and congestive heart failure, which have a poor prognosis (Lindman et al., 2013;
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Wisneski et al. Aortic Stenosis and Myofiber Stress
Go et al., 2014;Miura et al., 2015). Traditional means of
grading AS severity have predominantly relied on measuring
hemodynamically derived parameters such as transvalvular
pressure gradient, effective valve orifice area, and ejection blood
jet velocity (Nishimura et al., 2014;Baumgartner et al., 2017).
Recent research has brought to light limitations of these criteria
at effectively grading AS in the presence of LV dysfunction
coupled with decreased systemic arterial compliance, termed
“low-flow, low-gradient” AS (Hachicha et al., 2007;Pibarot and
Dumesnil, 2012;Tribouilloy et al., 2015). Additionally, the best
way to manage severe AS in asymptomatic patients remains
unclear, with only limited guiding evidence for the best course
of treatment (Wachtell, 2008;Nishimura et al., 2014;Carter-
Storch et al., 2019). Discordant outcomes have been reported
on the role of aortic valve replacement for these groups of
patients, highlighting the need to better diagnose and select
appropriate patients for treatment (Hachicha et al., 2007;Pibarot
and Dumesnil, 2012;Pibarot and Clavel, 2015;Tribouilloy et al.,
2015;Chadha et al., 2019).
Rather than being viewed as an isolated valve disease,
AS warrants a renewed understanding of its complex
pathophysiology wherein its detrimental effects on the
cardiovascular system are considered as derangements of
the LV, the aortic valve, and systemic vasculature together
(Briand et al., 2005). Advances in computational modeling
techniques now enable AS to be studied from a cardiovascular
systems perspective. LV-aortic coupling is a concept that
describes the inter-dependency of the LV and the aorta/systemic
blood vessels that impact cardiovascular function (Karabelas
et al., 2018;Shavik et al., 2018;Ikonomidis et al., 2019). Multi-
domain models of the human heart and circulatory system now
offer a complete mechanistic model of the ventricles, aortic
valve, and vasculature, making it ideally suited to investigate AS
(Baillargeon et al., 2014;Genet et al., 2014, 2016;Dabiri et al.,
2018;Sack et al., 2018b;Ghosh et al., 2020).
We believe that simulations of LV-aortic coupling with AS
will enable us to identify LV biomechanical parameters that are
prognostically significant markers of AS. Accordingly, as a first
step, we created isolated AS in an idealized human heart model
with the goal of gaining meaningful insight into the biomechanics
of the actual end-organ, the LV, which AS treatment seeks to
preserve. This investigation is one of the first of its kind to explore
this critical aspect of AS pathophysiology with the aid of powerful
computational simulation techniques.
METHODS
An idealized human heart model was generated from methods
described in Baillargeon et al. (2014) comprising solid
components, a finite element model, and a muscle fiber
model. It represents an average heart in a middle-aged individual
and can be altered to create diseased states.
Solid Model of the Ventricle
The solid model of the human heart portrays realistic anatomy of
four chambers, four valves, trabeculae in the ventricles, and great
vessels including the ascending aorta, the aortic arch, pulmonary
artery, and superior vena cava (Figure 1A). The LV finite element
model comprises approximately 120,000 tetrahedral elements.
Cardiac fiber orientation follows a rule-based approach from −60
degrees from epicardium to +60 degrees at the endocardium
(Dabiri et al., 2018;Sack et al., 2018a). Fiber and sheet directions
are interpolated and assigned to integration points of the finite
element model (Wong and Kuhl, 2014).
Constitutive Model Passive Material
Description
The ventricular material model passive response uses the
anisotropic hyperelastic formulation developed by Holzapfel and
Ogden, and has been widely published in many cardiac modeling
studies (Holzapfel and Ogden, 2009;Carrick et al., 2012;Sack
et al., 2016, 2018a). The deviatoric response is governed by the
following strain energy potential:
9dev =a
2bexp[b¯
I1−3)+X
i=f,s
ai
2biexp bi(¯
I4i−1)2−1
+afs
2bfs
[exp(bfs¯
I2
8fs −1](1)
Eight material parameters a, b, af, bf,as, bs, afs, bfs, and four
strain invariants (¯
I1,¯
I4f,¯
I4s¯
I2
8fs)define Equation (1). For these
simulations, a= 3.354 kPa, b= 7.08, af= 2.501 kPa, bf= 5.34,
while the remaining parameters were set to null. The strain
invariants are derived from the isochoric right Cauchy-Green
tensor: ¯
C=¯
FT¯
F=J−2/3C=J−2/3FTF(2)
Fis the deformation gradient, Jis the determinant of the
deformation gradient, J=det (F)and ¯
Fis the isochoric part of
the deformation gradient where ¯
F=J−1/3Fand det ¯
F=1.
The strain invariants can now be defined as:
¯
I1=tr ¯
C,¯
I4f=f0·¯
Cf0,¯
I4s=s0·¯
Cs0,¯
I8fs =f0·(¯
Cs0)
(3)
Terms f0and s0are orthogonal vectors in the fiber and sheet
direction in the reference configuration. The volumetric response
is governed by:
¯
9vol =1
D(J2−1)
2−ln (J)(4)
where Jis the third deformation gradient invariant, and Dis the
multiple of the bulk modulus D=2
K.K was set to 1000 kPa.
This material model has been validated by Genet et al. for the
purposes of ventricular computational modeling (Baillargeon
et al., 2014;Genet et al., 2014).
Active Material Description
The active myocardial tissue response is represented as a
time-varying elastance model (Guccione and McCulloch, 1993;
Walker et al., 2005):
σaf t,Eff=Tmax
2
Ca2
0
Ca2
0+ECa2
50 Eff(1−cos(ω(t,Eff))) (5)
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FIGURE 1 | (A) Solid finite element model of the human heart model. (B) Schematic representation of lumped parameter model of circulatory system fluid links and
parameters. The term Raorta represents the aortic valve resistance parameter. Rmitral , mitral valve resistance; Carterial, systemic arterial compliance; Rsystem, systemic
arterial resistance; Cvenous, venous compliance; Rvenous , systemic venous compliance; Rtricuspid, tricuspid valve resistance; Rpulmonary, pulmonary valve resistance;
Cpulmonary, pulmonary vascular compliance; Rpulmonary–system , pulmonary vascular resistance. (C) Left ventricle pressure-volume loops for baseline, moderate aortic
stenosis, and severe aortic stenosis conditions. (D) Left ventricle-aortic time pressure curves over one cardiac cycle. The systolic gradient between the left ventricle
and aorta can be appreciated and serves as a marker of aortic stenosis severity.
with functions defined as:
ECa50 Eff=Ca0max
qeB(lEff−l0−1
(6)
ωt,Eff=πt
t0
when 0 ≤t<t0(7a)
ωt,Eff=π
t−t0+tr(lEff)
tr
when t0≤t≤t0+tr(lEff)(7b)
ωt,Eff=0 when t>t0+tr(lEff)(7c)
trl=ml +b(7d)
lEff=lrq2Eff+1 (7e)
Tmax is the maximum allowable active tension and is
multiplied by terms regulating calcium concentration and the
time course of the contraction. These two terms are dependent on
the sarcomere length l. This law has been used extensively in prior
published studies on ventricular mechanics (Walker et al., 2005;
Carrick et al., 2012;Sack et al., 2016, 2018b). Parameters were set
as follows: Tmax = 135.7 kPa, Ca0= 4.35 µmol/l, Ca0max = 4.35
µmol/l, m= 1.0489 s µm−1,b= -1.429 s, B= 4.750 µm−1,
l0= 1.58 µm. lris the sarcomere length in the unloaded state, and
was assumed to vary linearly from 1.78 µm at the endocardium
to 1.91 µm at the epicardium (Guccione et al., 1993;Rodriguez
et al., 1993;Walker et al., 2005).
The total stress (scalar form) in the sheet direction of the fiber
is represented by:
σs=σps +n∗σaf (8)
where active stress in the sheet direction, σs, is the sum of passive
stress, σps, and a portion of fiber direction stress, n∗σaf . The
parameter nis a scalar value less than 1.0 and represents the
interaction between adjacent muscle fibers; a value of n= 0.4 was
used (Walker et al., 2005).
Circulatory System, Aortic Stenosis, and
the Cardiac Cycle
The finite element model of the ventricles is coupled to lumped-
parameter models of the pulmonary and systemic circulatory
systems. This arrangement has been effective in other studies
to link the ventricles and systemic circulation (Sack et al., 2016,
2018a,b). A schematic of the connections is represented in
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Wisneski et al. Aortic Stenosis and Myofiber Stress
Figure 1B. Three simulation conditions were created: baseline
with a normal aortic valve, moderate aortic stenosis, and severe
aortic stenosis. AS was simulated by increasing the aortic valve
resistance parameter, confirmed by presence of the desired mean
systolic pressure gradient between the LV and the ascending
aorta. During the portion of systole when LV pressure exceeded
aortic pressure, the difference between the two was calculated at
each simulation timestep, and the average was taken to determine
the mean gradient across the aortic valve. The baseline aortic
valve resistance parameter was 1.0e-9 MPa∗s/mm3with a mean
gradient of <2 mmHg. Moderate aortic stenosis was achieved
with resistance of 5.0e-9 MPa∗s/mm3producing gradient of
20 mmHg, and severe aortic stenosis was created with 1.0e-
8 MPa∗s/mm3with gradient of 40 mmHg.
The unloaded heart was initialized in a zero-stress state
obtained from iterative methods described by Sellier (2011)
and Rausch et al. (2017), based on loaded in vivo images.
At the start of the simulation, pressures within each cavity
were ramped from zero to physiologic values at 70% of
the diastole phase: right atrium 2 mmHg, right ventricle
2 mmHg, pulmonary artery 8 mmHg, left atrium 4 mmHg, left
ventricle 4 mmHg, aorta 80 mmHg, systemic arterial chamber
80 mmHg, systemic venous chamber 2 mmHg. Results for
analysis were obtained from the third cardiac cycle. Additional
resistance and compliance parameters in the circuit were
defined: systemic arterial resistance 1.4e+02 MPa∗s/mm3,
systemic venous resistance 9.7e-1 MPa∗s/mm3, tricuspid valve
resistance 2.5e-1 MPa∗s/mm3, pulmonary valve resistance 9.7e-
1 MPa∗s/mm3, pulmonary vascular resistance 1.1e+1 MPa∗s/mm3,
the mitral valve resistance 2.3e+0 MPa∗s/mm3, pulmonary
compliance 7.5e+6 mm3/MPa, systemic venous compliance
4.5e+7 mm3/MPa, and systemic arterial compliance
2.25e+6 mm3/MPa.
Cardiac cycles were simulated with Abaqus FEA (SimuliaTM,
Johnston, Rhode Island, United States) with an LV ejection
fraction (LVEF) of 60% for all three simulation conditions, with
end-diastolic volume of 136–138 ml. The same LV geometry
and mass were used across all three simulations for control
and represented a non-remodeled heart. A complete cardiac
cycle occurred over 0.7 s. The heart model is constrained in
space by fixed node sets at the cut planes of the aortic arch,
pulmonary trunk, and superior vena cava. An acceptable steady
state was achieved after running three consecutive cardiac cycles,
with further cycles producing <5% variation in the model’s
chamber pressures. LV myofiber stress and strain values were
obtained at end-diastole and peak systole (defined as the point
at which greatest LV pressure was generated). Data are expressed
as mean ±standard deviation. T-tests were used for statistical
comparison of continuous variables.
RESULTS
LV pressure-volume loops and LV-aorta pressure-time curves for
the different degrees of AS are shown in Figures 1C,D. Aortic
systolic pressure was 112–114 mmHg while diastolic pressure was
56–57 mmHg, representing a normal human physiologic range.
A mean systolic gradient of 20 mmHg between the LV and aorta
represented moderate AS. Conditions producing a mean systolic
gradient of 40 mmHg represented severe AS, and clinically would
serve as an indication for aortic valve replacement (Nishimura
et al., 2014;Baumgartner et al., 2017).
Global mean LV myofiber stress and strain values at end-
diastole and peak systole for each simulation condition are listed
in Table 1. Long-axis LV cross-sectional myofiber distribution
at end-diastole and peak systole are seen in Figure 2. The
endocardial regions harbor predominantly negative myofiber
stress at peak systole, a product of LV contraction physiology that
has been observed in other LV models (Genet et al., 2014;Sack
et al., 2016). Peak systolic myofiber stress increased progressively
with increasing degree of AS, whereas end-diastolic stress across
all conditions varied minimally with values <1.0 kPa. The mean
global LV myofiber stress was significantly different between
moderate AS and baseline (p<0.01) as well as between severe
AS and baseline (p<0.001).
Regional segmentation of the LV was performed in accordance
to the American Heart Association standardized myocardial
regions, creating 17 segments of LV myocardium based on
anatomic location and coronary perfusion territories (Cerqueira
et al., 2002). The mean myofiber stress of each of these segments
is shown in Figure 3. The range of peak systolic stress was 3.89
to 30.03 kPa for the baseline simulation, 4.81 to 34.79 kPa for
moderate AS, and 5.50 to 39.87 kPa for severe AS. Segments
that had the greatest peak myofiber stress from the basal, mid,
and apical regions were segment 3 (basal inferoseptal), segment 8
(mid-anteroseptal), and segment 17 (apex), and this finding was
consistent across all three simulation conditions.
DISCUSSION
Aortic stenosis has typically been graded by hemodynamic
parameters that can be measured on echocardiography: mean
pressure gradient across the valve, effective orifice area, and
peak blood jet velocity. However, a big limitation is that all
TABLE 1 | Global myofiber stress and strain in the left ventricle.
Simulation condition Myofiber stress Myofiber strain
End-diastole (kPa) Peak systole (kPa) End-diastole (%) Peak systole (%)
Baseline (no aortic stenosis) 0.31 ±1.36 9.31 ±10.33 +5.80 ±4.74 −22.40 ±8.73
Moderate aortic stenosis 0.32 ±1.39 13.13 ±10.29 +5.94 ±4.82 −22.24 ±8.91
Severe aortic stenosis 0.35 ±1.44 16.18 ±10.59 +6.16 ±4.90 −21.97 ±9.18
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Wisneski et al. Aortic Stenosis and Myofiber Stress
FIGURE 2 | Long-axis cross-sectional views of the left ventricle demonstrating myofiber stress at end diastole (upper row) and peak systole (lower row) for each of
the three simulation conditions: baseline, moderate aortic stenosis, and severe aortic stenosis.
FIGURE 3 | Systolic myofiber stress for each of the 17 standardized
myocardial regions for each simulation condition: baseline, moderate aortic
stenosis, and severe aortic stenosis.
of these are derived from other specific parameters and are
flow-dependent. To better understand disease pathophysiology
and find new clinically useful markers, we created a realistic
human LV model with coupled circulatory system parameters,
and isolated moderate and severe AS. This is one of the first
computational studies to specifically investigate the impact of
AS on LV myofiber stress. In this model, moderate stenosis
provided peak LV myofiber stress 1.4 times greater than baseline,
and severe stenosis yielded peak stress of 1.7 times greater than
baseline. With all other parameters being held nearly equal
(LVEF, aortic blood pressure, LV geometry), it is an anticipated
physiologic response that higher degrees of afterload will increase
the amount of stress experienced by the LV.
LV systolic performance and ventricular stress have been
investigated as prognostic markers for AS patients (Wachtell,
2008). After all, chronic exposure to increased afterload triggers
remodeling, leading to LV hypertrophy and eventual dysfunction,
culminating in congestive heart failure. However, determining
stress values from clinical imaging alone presents substantial
limitations, such as not accounting for the myocardial material
properties, or permitting prediction about how wall stress
may change under different physiologic conditions or after
treatment of AS. One study calculated end-systolic wall stress
in 78 symptomatic and 91 asymptomatic patients and defined
severe AS by aortic valve area ≤1 cm2(Carter-Storch et al.,
2019). End-systolic wall stress was estimated by measuring LV
wall thickness from cardiac magnetic resonance images, and
LV end-systolic pressure from echocardiogram-derived mean
gradients. The results indicated that end-systolic wall stress was
significantly greater in symptomatic patients at 9.6 kPa than in the
asymptomatic patients at 7.6 kPa. Symptomatic patients also had
markers of more severe AS and LV dysfunction, with lower LVEF
and smaller indexed aortic valve areas. These results illustrate
the potential clinical utility of LV stress as a marker of disease
severity. Even with the aforementioned limitations, the authors
found significant differences in the stresses, indicative of the
critical role it has in the disease state. Differences between that
study and ours in terms of patient population and methods of
calculating wall stress make direct comparison of the numeric
results challenging, but theirs are in range of our computationally
derived myofiber stress results.
In a study of severe AS patients in which LV contractility
was correlated with overall survival, end-systolic wall stress
was greater in patients with LVEF <60% than in those with
LVEF ≥60% (9.48 kPa vs. 7.91 kPa; p<0.001) at the time
severe AS was diagnosed (Ito et al., 2020). Furthermore, patients
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in the LVEF <60% group had significantly worse survival than
those in the LVEF ≥60% group. Classification of the patient
cohort by an end-systolic wall-stress threshold of 14.5 kPa, a
value estimated two standard deviations above the mean in
a population study by Aurigemma et al. (1994) also yielded
significantly worse survival in patients with >14.5 kPa end-
systolic wall stress. Six-year follow-up indicated cumulative
survivals of 20% (>14.5 kPa group) vs. 45% (≤14.5 kPa group).
This provides additional support for wall stress in the clinical
evaluation and prognostication of patients with AS (Aurigemma
et al., 1994;Ito et al., 2020).
The aforementioned studies were based on routine clinical
data and imaging available, with wall stress calculation techniques
based on Laplace’s Law. However, results derived from Laplace’s
Law are hindered by assumptions of uniform chamber geometry,
a relatively thin chamber thickness relative to radius, and do
not account for the myocardial material property. Additionally,
results derived using Laplace’s Law cannot provide detailed
information on transmural distribution of wall stress nor
local variations if focal ventricular pathology were to exist
(Zhang et al., 2011).
The ability to create patient-specific finite element models
would offer more accurate wall- stress results, as well as more
insight into the ventricular pathophysiology. One of the earliest
published finite element studies exploring the link between
aortic valve pathology and LV stress was done on aortic
insufficiency patients (Wollmuth et al., 2006). Patient-specific
LV geometries were obtained from cardiac magnetic resonance
imaging in patients with moderate-severe aortic insufficiency
before and after aortic valve replacement, as well as in control
volunteers without aortic valve disease. Maximum principal LV
end systolic stress was significantly elevated in patients with
aortic insufficiency before aortic valve replacement, relative to
controls (10.6 vs. 9.12 kPa; p<0.026). After aortic valve
replacement, LV end systolic stress decreased to 7.08 kPa. These
results help link the clinical benefit of aortic valve replacement
with LV biomechanics data, but are limited by the use of finite
element models with fairly few elements and assigning a linearly
elastic, isotropic material model to the myocardium. Aside from
the difference in the aortic valve disease studied by Wollmuth
et al., our LV model uses a highly refined mesh for the finite
element model, and a sophisticated myocardial material model
that accounts for microstructure, active, and passive properties.
The complexities of the myocardium need to be accounted for in
the material law in order to obtain the most accurate results.
Analysis of regional myocardial biomechanics is facilitated
with a solid, finite-element model. Our data from each of the
17 standardized myocardial segments reveals a range of stress
at peak systole, with anatomic regions each harboring regions
of localized peak stress. Examining how this ventricular stress
profile may vary among patients, or shift as disease progresses,
can offer a unique biomechanical profile for each patient. For
example, Jung et al. classified cardiac computed tomography
images of normal control patients and those with severe AS by
physical parameters associated with the 17 segments (Jung et al.,
2016). Two and three-dimensional parameters were found to
discriminate between the two patient groups, with the severe
AS group having greater LV wall thickness, segment mass, and
surface area. Among the segments, a range of values exists for
each parameter being evaluated. For instance, segment thickness
in AS patients ranged from 11.4 to 16.4 mm, whereas area ranged
8.3–13.7 cm2. Although differences in ventricular topography
and dimensions between the two groups are not surprising, the
study illustrates the added benefit of using regional segments to
perform more in-depth analysis of the LV.
In another study, the 17 segments were used to map
the distribution of myocardial fibrosis in AS patients by
analyzing gadolinium-contrast cardiac magnetic resonance
imaging (Weidemann et al., 2009). Patients with more severe
disease had fibrosis predominantly localized to the basal
segments. Although the impact of myocardial fibrosis on LV
myofiber stress remains a topic for further study, regional
segmentation offers better understanding of the pathologic
derangements occurring within the ventricle that are not
necessarily homogenously distributed. Global LV markers of
performance in conjunction with regional analysis can offer the
most in-depth understanding of LV derangements.
With advances in computational modeling techniques,
cardiovascular imaging, and a detailed understanding of
myocardial material properties, patient-specific models of clinical
utility are within reach and poised for more translational research
roles. Non-invasive means of determining parameters to tune
patient-specific models exist or are being perfected (Gray and
Pathmanathan, 2018;Dabiri et al., 2019;Mineroff et al., 2019;
Namasivayam et al., 2020). Enhanced constitutive myocardial
material models have been developed to account for presence
of fibrosis (Wang et al., 2016;Hasaballa et al., 2019). Ways to
integrate this data into clinical practice will first require larger
scale studies that have many patient-specific models. Clinical
and computational data needs to be correlated with patient
outcome, for example, whether the patient ultimately required
aortic valve replacement and how AS impacted LV function over
time. This correlation will help with the clinical validation of
computationally derived data and establish its place in clinical
decision-making algorithms.
Limitations
The primary limitation of this study is that our model represents
an idealized human heart geometry and simulates AS without
any LV remodeling. The material properties were based on
established, comprehensive models that describe normal
myocardial physiology, whereas AS is often a progressive
condition accompanied by a degree of LV remodeling
and fibrosis. Future studies with computational modeling
should incorporate patient-specific ventricular geometries,
myocardial material properties, and should address all possible
cardiovascular derangements in presence of AS.
CONCLUSION
In this study, we used a realistic human heart model with coupled
circulatory system to simulate AS and quantify the LV myofiber
stresses. This preliminary investigation used computational
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Wisneski et al. Aortic Stenosis and Myofiber Stress
methods to better assess the role of LV-aortic coupling in
the pathophysiology of aortic stenosis. Our goal is to apply
computationally derived data toward patient-specific assessment
of AS to guide management and intervention before irreversible
LV remodeling occurs.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation, to any
qualified researcher.
AUTHOR CONTRIBUTIONS
AW, YW, SP, and JG were involved in the conception, design
of the study, and analysis of the results. YW ran simulations
and provided data for analysis. JY and KS offered guidance on
the conditions for the computational simulations and assisted in
conducting the data analysis. AW, AH, TD, and JG were involved
in data analysis, interpretation of results, and the clinical concepts
of this study. All authors contributed to the article and approved
the submitted version.
FUNDING
This work was supported by the American Heart Association
post-doctoral fellowship, 18POST33960169 (AW).
ACKNOWLEDGMENTS
We thank Pamela Derish in the Department of Surgery,
University of California, San Francisco for proofreading
this manuscript.
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Conflict of Interest: YW was employed by the company Thornton Tomassetti
Lifesciences Division. KS is currently employed by the company Medtronic Inc. JY
was employed by the company Dassault Systemes Simulia Corp.
The remaining authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a potential
conflict of interest.
The reviewer HG declared a past co-authorship with one of the authors JY to the
handling editor.
Copyright © 2020 Wisneski, Wang, Deuse, Hill, Pasta, Sack, Yao and Guccione.
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