7klvhohfwurqlfwkhvlvru ......care; my brothers, aadil, aabid and aatif for cheering me up; my...
TRANSCRIPT
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Multimodality Imaging of the abdominal aortic aneurysm
Abbas, Abeera
Awarding institution:King's College London
Download date: 19. Sep. 2020
Emerging
multimodality imaging of
abdominal aortic aneurysms
Abeera Abbas
Thesis submitted for the degree of
Doctor of Medicine (research)
2014
Academic Department of Surgery
King’s College London
School of Medicine
Cardiovascular Division
St Thomas’ Hospital
1st Floor North Wing
Westminster Bridge Road
London, SE1 7EH
2
Acknowledgements
I would like to thank my supervisors Mr Matthew Waltham and Professor
Alberto Smith and Mr Bijan Modarai for encouraging me to undertake this
research. I will always value their support and guidance throughout my work.
I would like to thank all our collaborators, especially Professor Phil
Chowienzyck, Dr Marina Cecelja, Dr Mohammed T Hussain and Mr H Rashid. I
will always be indebted to Marina and Tarique for helping me understand the
complex imaging algorithms and especially, for their kindness and friendship. I
would also like to express my gratitude to our team within the Academic
Department of Surgery for their technical assistance and advice, especially to
secretaries Monica and Pauline for sending out letters to study participants.
I would like to acknowledge the financial support from the Department of Health
via the National Institute for Health Research (NIHR) comprehensive
Biomedical Research Centre award to Guy's & St Thomas' NHS Foundation
Trust in partnership with King's College London. This research forms part of the
ARTISTIC study programme (ARTerial Inflammation, STIffening and
Calcification). I will also like to thank the most valuable and important men in
this research ‘study participants’ as this work would have not been possible
without their willingness to volunteer.
Finally, I would thank my incredible family: my father, Abbas, for being so
devoted throughout my education; my mother, Nusrat, for her constant love and
care; my brothers, Aadil, Aabid and Aatif for cheering me up; my husband, Atif
for his unconditional support; and my children Abdullah and Maryam for being
so wonderful. Special thanks to my ever-supportive best friend Amina!
3
ABSTRACT
Age and hypertension lead to aortic remodeling and stiffness and are also
major risk factors for abdominal aortic aneurysms (AAA). This study aimed to
investigate: (i) the use of multimodal imaging to test the hypothesis that
aneurysmal disease of the abdominal aorta is a remodeling response to aortic
stiffness and systolic hypertension; (ii) the utility of a novel ultrasound-based
device (AortaScan) for detection of AAA in the community setting.
We used multimodality imaging tonometry and cardiovascular magnetic
resonance imaging (CMR) to quantify pulse wave velocity (PWV, a measure of
stiffness), in the aortic arch (Arch), thoracic aorta (TA) and the abdominal aorta
(AA). Stiffness was also correlated with measures of calcification and metabolic
activity measured on CT and PET/CT respectively.
The thoracic aortae of patients with AAA were stiffer than those of sex matched
controls. Although systolic hypertension was more common in AAA patients,
multivariate analysis revealed that aortic stiffness and mean arterial pressure
were associated with AAA disease. The likelihood of developing AAA disease
increases >3-fold for 1m/sec increase in PWV. This data suggests that
segmental stiffness is modified in the presence of AAA and provides further
evidence that aneurysm formation may be an adaptive remodeling response to
hypertension.
The AortaScan can detect AAA without the need for a trained operator and has
potential in a community-based screening programme. It would, however, need
further technical improvement to increase sensitivity before it could be
considered a replacement for trained screening personnel.
4
Table of Contents
Acknowledgements .......................................................................................... 2
ABSTRACT ........................................................................................................ 3
Table of Contents .............................................................................................. 4
Index of Figures ................................................................................................ 9
Index of Tables ................................................................................................ 10
List of Abbreviations ...................................................................................... 11
Statement of originality .................................................................................. 15
CHAPTER 1...................................................................................................... 16
General introduction ....................................................................................... 16
1.1 Abdominal aortic aneurysm ........................................................................ 16
1.1.1 Definition small/large AAA ....................................................................... 16
1.1.2 Pathogenesis of AAA .............................................................................. 17
1.1.3 Aortic remodelling and stiffening .............................................................. 18
1.2 Properties of the aortic wall ........................................................................ 20
1.3 Aneurysm surveillance ................................................................................ 21
1.3.1 Ultrasound ............................................................................................... 22
1.3.2 Computed tomography (CT) .................................................................... 24
1.3.3 Functional imaging of AAA by positron emission topography (PET) ........ 25
1.3.4 Functional imaging of AAA by MRI .......................................................... 27
1.3.5 Measurement of aorta distensibility and compliance ................................ 27
1.4 Aims .............................................................................................................. 31
CHAPTER 2...................................................................................................... 32
Recruitment, demographics and ultrasonography ...................................... 32
5
2.1 Background .................................................................................................. 32
2.2 Methods ........................................................................................................ 32
2.2.1 Recruitment ............................................................................................. 32
2.2.2 Demographic data and cross-sectional study .......................................... 34
2.2.3 Blood samples ......................................................................................... 34
2.2.4 Blood pressure, carotid and femoral duplex ultrasound101 ....................... 34
2.2.5 Ultrasound of aortic root and abdomen101 ................................................ 35
2.2.6 Statistics .................................................................................................. 36
2.3 Results .......................................................................................................... 36
2.3.1 Recruitment ............................................................................................. 36
2.3.2 Blood results ........................................................................................... 40
2.3.3 Blood pressure, plaque and CIMT measurements ................................... 40
2.4 Discussion .................................................................................................... 41
CHAPTER 3...................................................................................................... 43
Aortic Stiffness in presence of small AAA ................................................... 43
3.1 Background .................................................................................................. 43
3.1.1 Carotid-femoral pulse wave velocity (cfPWV) .......................................... 43
3.1.2 Brachial-ankle pulse wave velocity (baPWV) ........................................... 44
3.1.3 Role of MRI in assessment of AAA and regional aortic stiffness .............. 44
3.1.4 Aim .......................................................................................................... 45
3.2 Methods ........................................................................................................ 45
3.2.1 Carotid-femoral pulse wave velocity (cfPWV)101 ...................................... 45
3.2.2 Segmental PWV using PC-CMR ............................................................. 46
3.2.3 Statistics .................................................................................................. 50
3.3 Results .......................................................................................................... 52
3.3.1 Carotid-femoral pulse wave velocity (cfPWV) ............................................. 52
3.3.2 Correlation of cfPWV and aortic diameter ................................................... 52
3.3.1 Regression analysis ................................................................................ 53
6
3.3.2 AAA growth rate and cfPWV ................................................................... 55
3.3.5 PWV measurement using phase contrast cardiovascular magnetic
resonance imaging (CMR-PWV) ......................................................................... 56
3.3.6 Agreement between PWV measured by tonometry and MRI ................... 57
3.3.7 Segmental CMR-PWV measurement ...................................................... 59
3.3.8 Correlation of between pulse wave velocity and aortic diameter .............. 61
3.4 Discussion: .................................................................................................. 63
CHAPTER 4...................................................................................................... 67
Aortic calcification .......................................................................................... 67
4.1 Background: ................................................................................................. 67
4.1.1 Calcium scoring systems ......................................................................... 67
Aims .................................................................................................................... 70
4.2 Methods ........................................................................................................ 70
4.2.1 Calcification measurement using non-contrast CT ................................... 70
4.2.2 Statistics .................................................................................................. 71
4.3 Results .......................................................................................................... 72
4.3.1 Aortic calcification in AAA and control subjects ....................................... 72
4.3.2 Aortic calcification along the aortic segments .......................................... 73
4.3.3 Relationship between calcification and aortic stiffness (PWV) ................. 75
4.3.4 Fetuin-A analysis ..................................................................................... 78
4.4 DISCUSSION ................................................................................................. 80
CHAPTER 5...................................................................................................... 82
AAA imaging using FDG-PET/CT .................................................................. 82
5.1 Background .................................................................................................. 82
5.2 Methods: ....................................................................................................... 82
5.2.1 FDG-PET/ CT scan ................................................................................. 83
5.2.2 FDG-PET/CT Image Processing ............................................................. 84
7
5.2.3 FDG-PET/CT Image analysis .................................................................. 84
5.3 Results .......................................................................................................... 85
5.4 Discussion .................................................................................................... 86
CHAPTER 6...................................................................................................... 87
A novel ultrasound scanning device (AortaScan) for screening of AAA in
the community ................................................................................................ 87
6.1 Background .................................................................................................. 87
6.2 Aims .............................................................................................................. 88
6.3 Methods ........................................................................................................ 88
6.3.1 AortaScan ............................................................................................... 88
6.3.2 CT-Scan .................................................................................................. 91
6.3.3 Statistics .................................................................................................. 91
6.4 Results .......................................................................................................... 92
6.5 Discussion .................................................................................................... 94
CHAPTER 7...................................................................................................... 98
General discussion and future studies ......................................................... 98
7.1 Evaluation of tonometry and CMR to measure aortic stiffness ................ 98
7.2 Aortic stiffness and AAA ............................................................................. 99
7.3 Aortic calcification ..................................................................................... 102
7.4 AortaScan ................................................................................................... 104
7.5 Study limitations ........................................................................................ 105
7.6 Further studies ........................................................................................... 105
7.7 Summary .................................................................................................... 106
Appendix…….. .............................................................................................. 108
8.1 Non-contrast CT protocol .......................................................................... 108
8.2 PET protocol ............................................................................................... 111
8.3 Biomarkers ................................................................................................. 115
8
8.4 Data ............................................................................................................. 116
8.5 AortaScan images ...................................................................................... 146
8.6 Ethics Approval Document ....................................................................... 147
8.7 Publications, presentations and published abstracts ............................. 150
8.7.1 Publications ........................................................................................... 150
8.7.2 Submitted for publication ....................................................................... 150
8.7.3 Presentations ........................................................................................ 151
8.7.4 Published abstracts ............................................................................... 153
List of References: ........................................................................................ 154
9
Index of Figures
Fig 1-1: 3-dimensional reconstruction of CTA .................................................. 17
Fig 1-2: Hypothetical aortic remodeling hypothesis. . ........................................ 18
Fig 2-1: Flow diagram showing recruitment ...................................................... 38
Fig 3-1: SphygmoCor system and screen display of cfPWV ............................. 46
Fig 3-2: 2D-Scout image .................................................................................. 49
Fig 3-3: Multi-planar reconstruction (MPR) abdominal aorta. ............................ 50
Fig 3-4: cfPWV in subjects with AAA ............................................................... 52
Fig 3-5: Correlation between cfPWV and diameter ......................................... 53
Fig 3-6: A linear growth was observed in AAA ................................................. 56
Fig 3-7: CMR-PWV along the entire aorta in subjects with AAA ....................... 57
Fig 3-8: Agreement between cfPWV and PWV-CMR. ...................................... 58
Fig 3-9: PWV along the segments of aorta in AAA subjects ............................ 59
Fig 3-10: PWV along the segments of aorta in control subjects ........................ 60
Fig 4-1: Transverse section of abdomen illustrating calcium in the aortic wall . 71
Fig 5-1: Fused PET/CT ..................................................................................... 85
Fig 5-2: Image showing a FDG uptake of > 2SUVmax .................................... 86
Fig 6-1: AortaScan AMI 9700181 ........................................................................ 89
Fig 6-2: AortaScan screen display once aneurysm is detected181..................... 90
Fig 6-3: B-mode image on ScanPoint ............................................................... 91
Fig 6-4: BMI effect on AortaScan results ......................................................... 94
Fig 8-1: Technology: V-mode 181 .................................................................... 146
10
Index of Tables
Table 1-1: Characteristics of arterial wall and measurement technique……..30
Table 2-1: Subject demographics………………………………………………...38
Table 2-2:Renal functions, electrolytes and random lipid profile………….......39
Table 2-3: Blood pressure and ultrasound measurements…………………….39
Table 2-4: Carotid intima media thickness …………….……………………... ..40
Table 3-1: Correlation in cfPWV and diameter………………………………….52
Table 3-2: Linear regression model…………………………………..................53
Table 3-3: Logistic regression model……………………………………………..54
Table 3-4: Comparison of segmental PWV in AAA subjects…………………..58
Table 3-5 Comparison of segmental PWV in control subjects………..………59
Table 3-6: Comparison of segmental PWV AAA vs controls…………………..60
Table 3-7: Correlation in PWV and aortic diameter..……………………………61
Table 4-1: Comparison of segmental and total calcium………………………..72
Table 4-2: Correlation in PWV and calcium score………………………………76
Table 4-3: Vesicle and plasma Fetuin-A concentration……………………...…78
Table 6-1: Subject demographics…………………………………………………91
Table 6-2: Accuracy of AortaScan vs CT-scan…………………….…………....92
11
List of Abbreviations
3D Three-dimensional
2D Two-dimensional
18F 18-fluorine
AAA Abdominal aortic aneurysm
ABACUS Architecture-Based Analysis of Complex Systems
ARTISTIC ARTerial Inflammation, STIffening and Calcification
ACE Angiotensin converting enzyme
ADAM Aneurysm Detection and Management
BP Blood pressure
BMI Body mass index
BRC Biomedical research council
C Compliance
CABG Coronary artery bypass graft
CAD Coronary artery disease
CAESAR Comparison of surveillance vs. Aortic Endografting for Small
Aneurysm Repair
CCP Calciprotein particles
cfPWV Carotid femoral pulse wave velocity
CI Confidence interval
CIMT Carotid intima media thickness
CMR Cardiovascular magnetic resonance imaging
12
CNR Contrast to noise ratio
COPD Chronic obstructive pulmonary disease
CT Computed tomography
CV Cardiovascular
CVA Cerebrovascular accident
D Distensibility
Des Descending
DIR Dual inversion recovery
DM Diabetes Mellitus
ECG Electro cardiograph
eGFR Estimated glomerular filtration rate
Ep Pressure-strain modulus
EVAR Endovascular aneurysm repair
FEA Finite element analysis
FDG Flurodeoxyglucose
FIESTA Fast imaging employed steady-state acquisition
Fig Figure
FOV Field of view
GRE Gradient recalled echo
GSTTFT Guy’s and St Thomas’ NHS Foundation Trust
HDL High density lipoprotein
HPL Hyperlipedemia
HTN Hypertension
HU Hounsfield units
13
ILT Intra-luminal thrombus
IR Inversion recovery
LA Lumen area
LDL Low desity lipoprotein
MACE Major cardiovascular or cerebrovascular events
MAP Mean arterial pressure
MASS Multicentre Aneurysm Screening Study
MCMC Markov Chain Monte Carlo
MGP matrix-GLA-protein
MPR Multi-planar reconstruction
MRA Magnetic resonance angiogram
MRI Magnetic resonance imaging
NAAASP National AAA Screening Programme
NHS National Health Service
NWI Normalized wall index
OPG/RANKL Osteoprotegrin/rank ligand
PC-CMR Phase contrast cardiovascular magnetic resonance imaging
PET Positron emission tomography
PIVOTAL Positive Impact of endoVascular Options for Treating
Aneurysm earLy
PVD Peripheral vascular disease
PWI Pulse wave imaging
PWS Peak wall stress
PWV Pulse wave velocity
14
PWV-CMR Pulse wave velocity-Cardiovascular magnetic resonance
QIR Quadruple inversion recovery
REC Research Ethics Committee
ROI Region of interest
SEM Standard error of mean
SD Standard deviation
SNR Signal-to-noise ratio
SUV Standardized uptake value
TDI Tissue Doppler imaging
TE Echo time
TGL Triglyceride
Th Thoracic
TI Inversion time
TR Repition time
TSE Turbo spin echo
TVA Total vessel area
UKSAT United Kingdom Small Aneurysm Trial
US Ultrasound
VENC Velocity encoded
VSMC Vascular smooth muscle cell
VSMMPs Vascular smooth muscle micro-particles
WA Wall area
15
Statement of originality
The work contained in this thesis is my original work, except where
acknowledged in the text.
Statement of cojoining work
Dr Marina Cecelja and Dr Benyu Jiang performed the majority of ultrasound
measurements in Chapter 3.
The CT imaging was performed by Audrey Taylor in the Department of Nuclear
Medicine, Guy’s Hospital. The imaging protocol was developed in collaboration
with Dr. Kathryn Adamson and Dr. Michelle Frost.
The CMR imaging was performed by Dr. Tarique Hussain in the Department of
Radiology, Guy’s Hospital. The imaging protocol was developed in collaboration
with Dr. Tarique Hussain, Dr. Gerald Greil and Dr. Rene Botnar.
16
CHAPTER 1
General introduction
1.1 Abdominal aortic aneurysm
Abdominal aortic aneurysm (AAA) is found in 6% of men and rupture is
associated with a high morbidity and 80-90% mortality. Age and male sex are
the two well-known risk factors for AAA development. Currently the aortic
diameter is the only feature that is used to predict the risk of rupture. The other
recognised risk factors for continued expansion of the aneurysm include
smoking, female gender and higher mean blood pressure. More recently
developments in imaging have allowed other features of the AAA to be
proposed as predictors of rupture risk; these include arterial wall stiffness,
intraluminal thrombus (ILT), wall tension and peak wall stress1.
1.1.1 Definition small/large AAA
‘Aneurysm’ is derived from the Greek word ‘aneurusma’, meaning widening,
and can be defined as a localised dilatation of a vessel2. An artery is considered
aneurysmal if the size is >50 % of its original diameter. In the abdominal aorta,
it is defined as an aneurysm if the diameter is >3cm3. An infra-renal AAA is
called small if the size is < 5.5cm and large if size is > 5.5cm.
17
Fig 1-1: 3-dimensional reconstruction of CTA showing an abdominal aortic
aneurysm in 74 year old male (CTA-computed tomography angiogram)
1.1.2 Pathogenesis of AAA
Multiple factors are involved in the pathogenesis of AAA. The main
characteristic of an abdominal aortic aneurysm (AAA) is the loss of the medial
elastic fibers by connective tissue, with a net loss of elastic properties, increase
in diameter and, finally, wall rupture. There is destruction of medial and
adventitial collagen, medial smooth muscle cells, wall infiltration by lymphocytes
and macrophages with neovascularization4,5. Other factors, such as blood flow,
parietal calcification, intra-luminal thrombus and maximum diameter, may also
affect the behaviour of the aortic wall6,7.
18
Fig 1-2: Hypothetical aortic remodeling hypothesis. Mechanism of aortic stiffness is
complex, ageing and smoking cause stiffness leading to hypertension. Aorta remodels
to combat hypertension. Remodeling in early stages causes stiffness and later
dilatation as a beneficial effect.
1.1.3 Aortic remodelling and stiffening
Aorta is an elastic artery. AAA pathophysiology remains complex.
Atherosclerosis has some contribution to AAA pathogenesis8, but is more
prevalent in patients with atherothrombotic disease9. It has been suggested that
the mechanism that regulates the development of AAA is independent of
atherosclerosis and is a local manifestation of a systematic disease10. Age,
hypertension (HTN) and diabetes mellitus lead to reduction in elastic content of
the aorta11 and are associated with an increase in stiffness of elastic arteries12 .
HTN causing AAA is less defined13-14 but it contributes to aneurysm rupture 15.
Blood pressure in the abdominal aorta is higher than that of the thoracic aorta
19
as a result of reduced elastin content and a greater collagen content11,16,
contributes to a greater stiffness in this segment.
Aberrant arterial remodeling has a pivotal role in the development of AAA17.
Dilatation (which like stiffening is a strongly age related phenomenon) may
occur as a remodeling process in response to the intrinsic stiffening of the wall
offsetting the effects of intrinsic stiffening on systolic/pulse pressure by
increasing functional compliance (~ stiffness x diameter)18. An excessive
propensity to dilatation, in combination with additional local stimuli (e.g.
atherosclerotic plaque) may result in aneurysm formation. AAA enlargement is
related to remodeling of the extracellular matrix, particularly collagen and elastin
metabolism19 , whilst there is general consensus that ruptures are a multi-
factorial process involving a complex interaction between mechanical forces
and local cellular activity and remodeling2, 20.
The mechanism of arterial stiffening is complex and mainly affects elastic
arteries. Like AAA formation, stiffness is possibly caused by fracture of elastin
and deposition of collagen within the media of an artery21. Calcification further
complicates this process22. Muscular arteries are not affected by changes in
blood pressure and age23, 24. In a longitudinal study it has been shown that
brachial artery stiffness reduces as an adaptive response to progressive
increase in aortic stiffness25.
Mechanisms of calcification
This is relatively little detailed knowledge of the mechanisms that regulate
arterial stiffening and calcification. The type of calcium crystal that accumulates
in the blood vessel wall exists mostly in the form of calcium apatite, which is the
type of mineral found in bone26.
20
Micro-particles released from vascular smooth muscle cells (VSMCs) in
response to mechanical stimuli and inflammatory cytokines are thought to
induce matrix degradation and initiate an osteogenic process leading to
calcification27. Oxidative stress, bone morphogenic proteins (BMPs) or changes
in pyrophosphate levels promote VSMCs to undergo osteogenic
differentiation28. Calcification is regulated as an active process by inhibitors
(present in micro-particles) including fetuin-A, matrix Gla protein (MGP) and
osteoprotegrin/ rank ligand (OPG/RANKL)26-27, 29-30. The level of circulating OPG
is independently predictive of cardiovascular events in man31 and is also weakly
associated with aneurysm progression32. Fetuin-A is synthesized in liver and
secreted in blood. Its deficiency is associated with soft tissue calcification in
mice and humans30. The precise molecular mechanism of MGP function is not
known, although it was thought for some time to act by binding excess calcium
ions or small crystals in tissues and clearing them to the circulation26. It is found
to be a component of a serum complex consisting of hydroxyapatite, fetuin and
other proteins33.
Vascular calcification can occur in two sites in the arterial wall: the intima and
the media. Intimal calcification is associated with atherosclerosis, while medial
calcification exists independently of atherosclerosis and is associated with
elastin and vascular smooth muscle cells34.
1.2 Properties of the aortic wall
Diameter, pulse wave velocity (PWV), arterial stiffness, elasticity, peak wall
stress (PWS), compliance, distensibility and pressure-strain modulus (Ep)
describe a range of properties of the aortic wall that have been described to
predict aneurysm expansion or rupture (Table 1-1). Identification of an
21
individual’s aneurysm progression and rupture risk by non invasive imaging
could allow selection of patients with small AAA for early repair.
1.3 Aneurysm surveillance
Randomized control trials, the UK Small Aneurysm Trial (UKSAT) and the
Aneurysm Detection and Management (ADAM) study, have shown that patients
with small AAA can safely be kept on surveillance programmes13,35. There is
still, however, much debate as to whether surveillance is appropriate for all
patients with small AAA as some may be at increased risk of rupture36. In the
Multicentre Aneurysm Screening Study (MASS Trial) 5% of the patients
undergoing ultrasound surveillance died from aneurysm related causes; either
after rupture or symptom onset that led to urgent surgery37. Autopsy studies
show that 10-24% of all ruptured AAA have a diameter <5.5cm38-39. The UKSAT
trial showed that more than 60% of patients with small aneurysms required
surgery within six years35. This is predominantly for reaching size criteria but
also for symptom onset. This has led some to argue that it may be safer to
operate on some patients with small AAA. This is more the case now with the
widespread application of endovascular stent repair, with potentially lower
procedural risks. Two randomized control trials, Comparison of surveillance vs.
Aortic Endografting for Small Aneurysm Repair40 (CAESAR) and Positive
Impact of endoVascular Options for Treating Aneurysm early41 (PIVOTAL) have
failed to show any survival benefit of an early endovascular aneurysm repair
(EVAR) versus surveillance of subjects with small AAA. Although these trials
failed to demonstrate an overall benefit as a group, there appears to be a
clinical advantage in selecting patients at higher risk of early rupture at a small
size to be treated.
22
There are a number of techniques available to facilitate aneurysm surveillance:
(sections 1.3.1 to 1.3.5 are part of my published work42)
1.3.1 Ultrasound
Currently, the best determinant of the risk of AAA rupture is the maximum
diameter43. Ultrasound (US) has been used since the early 1960s to measure
AAA diameter and it has a sensitivity approaching 100% to detect AAA44.
Although ultrasound is operator dependent the measurements are reproducible.
It is non-invasive, easily available, cost effective and modern portable scanners
allow community based screening and surveillance. Recent advances include
devices like AortaScan that automatically detect the aortic diameter with 90%
sensitivity without the need for a trained operator45. Ultrasound tends to
measure AAA diameter slightly smaller than CT scan46.
The UKSAT showed that patterns of expansion are highly variable. Certain
patients having a uniform rate of aneurysm expansion, some aneurysms grow
in intermittent bursts whereas certain aneurysms remaining stable. Traditionally
expansion rates of greater than 1cm per year or 0.5cm per 6 months have been
used as indicators of increased rupture risk but this is not borne out in data from
the UKSAT study47. These criteria have therefore not been included as a
reason to refer a patient with a small AAA for vascular specialist assessment in
the National AAA Screening Programme (NAAASP).
Ultrasound has also been used to measure the compliance and distensibility of
AAA. Compliance is a measure that reflects mechanical properties of the aortic
wall and increases with AAA diameter48 (Table 1-1). AAA expansion is the
result of wall remodeling and this is reflected by changes in compliance. Tissue
Doppler imaging (TDI) measures compliance by utilizing the Doppler effect to
assess wall displacement at points along the aorta during the cardiac cycle.
23
Compliance is calculated from the amount of displacement of the vessel wall
and the measured blood pressure. Long et al demonstrated a correlation
between compliance and aortic size using TDI. There was significant positive
linear relationship between maximum diameter and segmental compliance, but
not with the pressure strain modulus or stiffness49. Further studies are now
required to assess whether compliance measured during routine AAA
ultrasound monitoring using TDI could be a predictor of expansion.
Distensibility is expressed as the relative change in vessel cross-sectional area
that occurs during the cardiac cycle, divided by the corresponding change in
blood pressure (Table 1). Pressure strain modulus (Ep) is a measure of stiffness
(β) or lack of elasticity of an artery20 . If Ep and β are higher then the artery is
less distensible and has lower arterial wall compliance48. These characteristics
differ from compliance in that they are measures of the amount of change in
diameter with pressure change, whereas compliance reflects the rate of that
change. Aortic distensibility decreases with age and can be measured non-
invasively by either an ultrasound scan-based echo tracking technique
(Diamove)48, ECG-gated CT50 or MRI51. All of these use an estimated central
blood pressure value from sphygmomanometer to calculate compliance; this
may be inaccurate in certain cases. The Diamove48, 52 device uses a 3.5-MHz
B-mode ultrasound probe to produce an image of the aorta or aneurysm. The
anterior and posterior walls are then tracked and the change of vessel diameter
during each cardiac cycle is measured. The acquired data is analyzed to
calculate the distensibility. Diamove can also measure diameter and compliance
with acceptable reproducibility53. A prospective multi-centre study showed that a
reduction in distensibility over time (increase in Ep) significantly reduced the
time to rupture independently of aneurysm diameter52. Baseline aortic wall
24
distensibility may provide an additional parameter for AAA to optimize the
indication and time for elective repair54.
Vibrometry is a novel use of ultrasound to estimate wall stress in rubber tube
models of aneurysm sac and may have potential to predict the risk of rupture in
AAA55. Ultrasound can also be used to map the propagation of pulse wave
along the aneurysm wall and this has been described as a tool that may predict
AAA rupture56. Wall stress and PWV are discussed in detail on sections on CT
and MRI respectively.
The above evidence suggests that ultrasound can be used to measure multiple
dynamic properties of the aortic wall in addition to its diameter. These
parameters may make US a better tool to predict AAA expansion or rupture.
1.3.2 Computed tomography (CT)
CT scanning accurately diagnoses abdominal aortic aneurysms and is
commonly used for assessment of anatomy prior to endovascular or open
aneurysm repair. Supplementary data regarding proximal and distal aneurysm
extension in relationship to branches, characteristics of intraluminal thrombus,
and aortic neck length, diameter and angulation can all be obtained by CT. In
addition to anatomical details CT also provides information on aortic wall
calcification. As discussed above CT can be used to measure distensibility 50 .
Distensibility is reduced within the aneurysmal segment compared to normal
proximal aorta in both small and large AAA. It has been suggested that changes
in distensibility might affect the risk of subsequent aneurysm formation57.
Nearly 75% of all AAAs have varying degrees of intraluminal thrombus (ILT)58.
The role of ILT in predicting expansion or rupture is much debated. In one study
25
it was found that aneurysm wall covered with thrombus is thinner and shows
signs of inflammation and apoptosis of smooth muscle cells. These findings
may be related to a reduced structural integrity and stability of the wall and an
increased risk of rupture59. The presence of ILT in AAA may affect intra-
aneurysmal pressure which may in-turn cause rupture. Schurink et al. showed
that ILT does not reduce the mean blood pressure or the pulse pressure near
the aneurysmal wall and therefore does not reduce aneurysm rupture risk 60 but
Stenbaek et al. support that thrombus and its growth rate are associated with
an increased risk of AAA rupture61. There is however no overwhelming
evidence related to the consequences of ILT.
1.3.3 Functional imaging of AAA by positron emission topography (PET)
PET offers the possibility of using radiotracers to image functional activity in the
wall of the aorta62-67. Currently one of the tracers with clinical application is the
glucose analogue 18-fluorine (18F) flurodeoxyglucose (FDG). It enables
detection of increased glucose metabolism. Increased uptake is characteristic of
many cancers and other non-malignant processes such as infection and
inflammation68. It could therefore potentially assess the metabolic activity in the
aneurysmal wall69. FDG uptake is expressed as standardized uptake values
(SUV), for tissue attenuation. The SUV is calculated either pixel-wise or over a
region of interest for each image.
Increase enzyme and cellular activity in the AAA wall are seen histologically at
the site of rupture and these ‘hot spots’ may be responsible for focal weakening
and rupture at relatively low levels of intraluminal pressure 70-71. FDG-PET/CT is
a promising technique that may identify such areas of activity and thus be able
to predict an increased risk of growth or rupture65,69-71. PET-CT imaging with 18-
26
fluorine (18F)-labeled nanoparticles allows quantification of macrophage content
in a mouse model of AAA72.
Maximum aortic FDG uptake correlates with both the histopathologic
characteristics of unstable aneurysm wall and clinical symptoms64. The
intensity of the FDG signal significantly correlates with tissue inflammation,
increased macrophage and T-cell accumulation and matrix metalloproteinase-9
(MMP-9), the activity of which associated with aneurysm rupture73. Patients with
AAA have a significantly increased FDG uptake compared with normal aortas66.
Similarly significantly higher uptake is observed in inflammatory aneurysms
compared with non-inflammatory aneurysms63. Furthermore, FDG uptake in
aortic wall seems to increase with age and is not related to calcification68. The
preliminary findings from an observational longitudinal pilot study suggest that
there is an inverse trend between FDG uptake on PET and future AAA
expansion, suggesting that aortic aneurysms with lower metabolic activity are
more likely to expand72.
As discussed above, aneurysm enlargement has been associated with growth
of intraluminal thrombus61. This thrombus is biologically active with constant
turnover due to platelet activation and phosphatidylserine exposure at the
interface with circulating blood74. 99mTc-annexin-V (ANX) is a tracer that binds
to phosphatidylserine on the surface of activated platelets demonstrated in
experimental aneurysms and ex vivo in human AAA thrombus samples
following open repair75. ANX imaging may become a non-invasive tool for
providing functional information on mural thrombus activity, which may be
relevant to subsequent AAA growth.
27
New PET/CT tracers that are more specific are being developed. These will be
targeted towards processes known to be important the aneurysm development
and rupture, for example to image MMP activity or specific cellular infiltration of
the wall, and may therefore become powerful in vivo tools to assess the
mechanisms and progress of the disease.
1.3.4 Functional imaging of AAA by MRI
Nano-particles, for example ultra-small super paramagnetic iron oxide particles
(USPIO), have a variety of applications in molecular and cellular imaging but so
far these have not been used in human AAA imaging. USPIO enhanced MR
imaging has been used in vivo to identify inflammation in human carotid
plaques with increased accumulation of USPIO within macrophages76. USPIO
may be useful as a surrogate for detecting the acute inflammatory activity
involved in the development of abdominal aneurysms77. In a pilot study, uptake
of USPIO in abdominal aortic aneurysms appears to distinguish those patients
with more rapidly progressive abdominal aortic aneurysm expansion78.
1.3.5 Measurement of aorta distensibility and compliance
Pulse wave velocity is a measure of the time taken by the pulse pressure wave
to travel over a specific distance of artery79. The stiffer the wall of the
artery, the faster the wave moves. It is calculated as the distance between two
measurement points (usually the common carotid and femoral arteries) divided
by the time shift to the waveforms from these two points. The measurement of
PWV is a simple, non-invasive and reproducible method to determine arterial
stiffness79. Stiffness of the arterial wall is mainly determined by the matrix
components of the wall, i.e., the elastin, collagen, and smooth muscle cells11. It
is discussed above how PWV is distinct from pressure strain modulus (Table 1-
28
1). A study on aneurysm specimens showed that aortic wall thickness or
stiffness may be a better predictor of rupture for large AAAs80.
Carotid femoral PWV (or aortic PWV) is considered the gold standard
measurement of arterial stiffness79. Alternatively brachial-ankle pulse wave
velocity (baPWV) can be measured and this provides qualitatively similar
information as aortic PWV and is a well-established index of central arterial
stiffness81. PWV can be measured by non-invasive methods based on pressure
sensors82 or Doppler probes79 and MRI83 . Aortic PWV is an independent
predictor of cardiovascular mortality in subjects older than 70years; a 1m/s
increase in PWV increases cardiovascular mortality by 19% 84.
The propagation of the pulse wave is inversely related to the distensibility of the
arterial tube. In a dilated vessel the PWV is decreased and this corresponds to
an increase in compliance. PWV is a more easily measured property than true
compliance, which requires an intra-arterial pressure measurement to relate to
the corresponding change in arterial cross sectional area and this cannot be
obtained non-invasively. Peripheral blood pressure can be used to calculate
compliance but this underestimates the true intra-aneurysmal systolic pressure
by 5% and overestimates diastolic blood pressure by 12%85.
A change in pulse wave velocity along the segments of aorta may detect
changes in local compliance that are a precursor of aneurysm formation even
before a change in aortic diameter or other anatomical changes occur 56. In one
small study, a high thoracic aortic compliance was related to faster growth of
the AAA and earlier rupture86. Phase contrast MRI can also be used to measure
29
compliance in aortic aneurysms87,88. The role of PWV, distension and
compliance measurements are yet to be validated in prediction models of AAA.
MRI however is a reliable tool in AAA evaluation, 3D contrast enhanced MR
angiography is accurate in defining the location, extent, diameter and exact
morphology of aneurysms and the relationship to aortic branch vessels and
surrounding structures. MRI also displays ILT as intermediate signal change on
standard T1-weighted images but not aortic calcification. However, slow
intraluminal flow may be difficult to differentiate from ILT. This may be over
come with the use of spin-echo with gradient recalled echo (GRE) cine MR or
MRA with gadolinium contrast89. Other recent MR techniques that may provide
enhanced resolution and flow information include fast imaging employed
steady-state acquisition (FIESTA) and balanced steady state free precession90.
The contrast enhancement is performed on the basis of T2 to T1 ratio rather
then inflow effects or gradient echo methods. MRI is beneficial in terms of lack
of ionizing radiation and this specially applies to patients who are severely
allergic to iodinated contrast media used for CT, or at a high risk of developing
renal failure due to contrast nephropathy. MRI also provides ideal image
modality in-terms of reproducibility with low inter-observer error for diagnosis
and monitoring AAA expansion overtime.
30
Table 1-1: Physical characteristics of arterial wall and measurement
technique42
Physical characteristics
Definitions Measurement
technique
Pulse wave velocity (PWV)
The velocity of the pressure wave to travel over a specific distance between two sites56,79.
PWV = ∆x /∆t
∆x, the distance between two recording
sites
∆t, the transit time between the arrival of
pressure wave at sites
Applanation tonometry79,81,
Phase
Contrast
MRI83.
Compliance (C) Is the fractional change in vessel cross sectional area divided by the change in distending pressure49.
C = ∆A/∆p
or
C= 1 / (PWV)2ρ
ρ= 1055.103[kg/m3] denotes the mass density of blood that is assumed to be constant.
Indirectly by PWV.
Directly from ECG gated
Phase Contrast MRI85,
Tissue Doppler imaging
(TDI)49.
Distensibility (D) The relative change in vessel cross-sectional area (A) that occurs during the cardiac cycle, divided by the corresponding change in blood pressure (∆p)50.
D = ∆A/Ao.∆p
Ao is minimum vessel area and ∆A difference in max and min area
ECG gated CT50, 57 and
MRI15,18, Diamove19,20
Pressure strain modulus (Ep)
Is a measure of the stiffness (β) or lack of elasticity of an artery52.
Ep = (Ps - Pd) [Dd / (Ds - Dd)]
Arterial blood pressures at peak systole (Ps) and end diastole (Pd) and the corresponding arterial diameters (Ds and Dd)
ECG gated CT50 and MRI57,
Diamove48, 52
Peak wall stress The maximal force per unit area within the AAA wall at systolic blood pressure.
FEA model using 3D CT
images91-93 and ABAQUS
software
31
1.4 Aims
This study aimed to investigate:
i) the relationship between aortic stiffness, calcification and aortic
dilatation (aneurysm) in the population;
ii) the relationship between putative biomarkers of calcification (soluble
and microvesicle Fetuin-A levels) and calcium score measured by
non-contrast CT in the assessment of aortic calcification;
iii) the utility of a novel ultrasound-based device (AortaScan) for
detection of AAA in the community,
32
CHAPTER 2
Recruitment, demographics and ultrasonography
2.1 Background
Age and smoking are well-known factors for AAA development 94,95.
Hypertension, hyperlipedemia (HPL), male sex, chronic obstructive pulmonary
disease and family history are other known risk factors for AAA disease 96-99.
AAA is less prevalent in individuals diagnosed with diabetes mellitus100.
Atherosclerosis commonly co-exists with AAA disease; multiple studies have
explored the theories of atherosclerosis contributing to AAA disease16,101 and
AAA formation as pathology independent of atherosclerosis102.
There were some differences in risk factors between our two study groups and
whilst these were fully adjusted for, we cannot exclude the possibility that they
may have influenced the findings. This research work forms part of larger
ARTISTIC study programme (ARTerial Inflammation, STIffening and
Calcification). Therefore, AAA subjects in addition to aortic stiffness analysis
also had plaque, intima media thickness and aortic root measurements etc.
Additional data obtained is presented only if relevant to this study.
2.2 Methods
2.2.1 Recruitment
Subjects were recruited from an established aneurysm-screening programme
(male, aged >65 years, n=6000 invitees/year) and males already under
33
ultrasound surveillance in GSTT and KCH (n=200). All subjects underwent B-
mode ultrasound to assess for the presence of carotid or femoral plaque,
carotid intima-media thickness (CIMT) as measures of a generalised
vasculopathy, and confirmation of the abdominal aortic size. Aortic root
diameter was measured. Carotid-femoral pulse wave velocity (cfPWV) was
measured using tonometry, segmental pulse wave velocity by phase contrast
cardiovascular magnetic resonance imaging (PC-CMR) and calcification by
non-contrast computed tomography (CT). These investigations were completed
on three separate visits. The aim was to recruit 50 patients with small
aneurysms (diameter 3.0-5.5cm, n = 50) and 50 age-matched control subjects
with normal diameter aorta to undergo tonometry, CMR, and US, non-contrast
CT. AAA subjects and controls were identified from the aneurysm surveillance
and screening program to form a case-control cohort.
Male patients known to have small AAA (3.0-5.5 cm) were identified from the
local AAA surveillance programme. Similarly male control subjects with age
>65years were recruited from National Health Service (NHS) AAA screening
programme. These subjects had undergone abdominal aortic ultrasound as part
of a local AAA screening programme and were found to have a maximum aortic
diameter <3cm.
All subjects completed written informed consent prior to enrolment. Scans were
performed at Guy’s and St Thomas’ NHS Foundation Trust (GSTTFT). The
study protocols for all investigations undertaken were approved by the
Research Ethics Committee (REC) and performed according to the declaration
of Helsinki.
34
Exclusion criteria
1. Patients/control subjects with a pacemaker or any inserted metal device like
intracranial aneurysm clip, cochlear implants, which is a contraindication for
MRI.
2. Patients/ control subjects who had significant radiation exposure at work or in
any other form.
3. Patients/ control subjects who suffered from heart failure or respiratory failure
and were unable to lie flat in scanner.
4. Patients/ control subjects who had poor renal function (estimated glomerular
filtration rate (eGFR) <40ml/min/1.73 m2).
2.2.2 Demographic data and cross-sectional study
The cross-sectional study was carried out by filling in a patient/volunteer
registration form. A demographic data sheet was completed for each subject
including medical history and current medications.
2.2.3 Blood samples
A blood sample from all patients/control subjects was taken to check
electrolytes, renal functions (sodium, potassium, creatinine and eGFR), random
lipid profile (cholesterol, low-density lipoproteins (LDL), high-density lipoproteins
(HDL) and triglycerides (TGL) and circulating markers of calcification
(microvesicle Fetuin assay).
2.2.4 Blood pressure, carotid and femoral duplex ultrasound103
Blood pressure
All subjects were seated in a quiet room for at least 10 min prior to
measurement of cfPWV. A validated oscillometric device (Omron 705CP,
35
Omron, Tokyo, Japan) was used to measure brachial blood pressure in
duplicate. Subjects were told to fast for an hour prior to examination.
Common carotid intima-media thickness (CIMT) measurement
B-mode ultrasound (Siemens CV70 with 13-MHz vascular probe) was used to
visualise left and right carotid and femoral arteries. An automated wall tracking
software (Medical Imaging Applications, Coralville, Iowa) was used to measure
the common carotid intima-media thickness (CIMT) in the near and far walls.
Four measurements were obtained from both carotid arteries two of which in the
near and two in the far wall104. The maximal measurement, of these 4
measurements, was used for data analysis. These measurements were taken
1-2cm proximal to the carotid bifurcation during diastole in an area free of overt
plaque. Common carotids, carotid bifurcations, origins of the internal and
external carotid arteries, common femoral arteries, femoral bifurcations and the
origins of the superficial and deep femoral arteries were also examined for
presence of plaque. Carotid plaque is defined as CIMT ≥1.5mm as suggested
by Mannheim CIMT consensus report105. Plaque was also graded according to
echogenicity into predominately echolucent/non-calcified (>50% of similar
echogenicity to blood) or echogenic/calcified (>50% of similar echogenicity to
the bright echo of the media-adventitia interface).
2.2.5 Ultrasound of aortic root and abdomen103
The aortic root was visualised using 2-D echocardiography from a long axis
para-sternal view using a Siemens CV70 with 4MHz cardiac transducer and its
diameter measured between the anterior and posterior aortic root wall at end
diastole. The epigastrium was scanned vertically at the xiphoid process using
the same probe in order to visualise the abdominal aorta. The diameter of the
36
abdominal aorta was measured 1-2cm below the diaphragm at end-diastole.
AAA diameter was measured 1-2cm above the aortic bifurcation.
2.2.6 Statistics
Data were analysed using SPSS version 19.0 statistical software (SPSS Inc.,
Chicago, IL, USA). Primary outcome measures were cfPWV and segmental
PWV. Differences between the control and AAA groups were analysed using 2
tests for categorical data and unpaired t-test for continuous data. Mann-Whitney
U test and Kruskal-Wallis test was used for non-parametric data. A P-value <
0.05 (two sided) was considered significant.
2.3 Results
2.3.1 Recruitment
One hundred and eighteen male subjects (aged >60 years) were recruited (AAA
n=62; control n=56). 62 non-consecutive male patients known to have a small
AAA (mean diameter = 3.9cm, range 3.0-5.6cm), with mean age 71years, range
62-84 years, were recruited from the local AAA surveillance programme. 56
male control subjects with an aortic diameter of 2.0cm, range 1.5-2.8cm, with
mean age 66.5yrs, range 65-68 years were recruited. These subjects had
undergone abdominal aortic ultrasound as part of a local AAA screening
programme and were found to have maximum aortic diameter <3cm.
Subject demographics are presented in Table 2.1 and blood results in Table
2.2. All subjects (n=118) completed visit one, which included blood pressure
measurement, applanation tonometry (cfPWV), ultrasound and blood tests.
Most of the subjects completed both CT and MRI scans and some were lost to
follow up or refused either CT or MRI after having one scan. Seven subjects
37
had a recent contrast-CT and were therefore excluded from aortic calcification
analysis. Sixteen MRI scans were excluded from PWV analysis because of
multiple technical reasons (Fig 2.1).
Figure 2-1: Flow diagram showing recruitment
Table 2-1: Subject demographics
HTN, hypertension; HPL, hyperlipidaemia; CAD, coronary artery disease;
PVD,vascular disease; CVA, cerebrovascular accident; COPD, chronic obstructive
pulmonary disease; DM, diabetes mellitus; CABG, coronary artery bypass graft; BMI,
body mass index; ACE, angiotensin converting enzyme.
Risk factor AAA (n=62) Control (n=56) P
Age (years; mean SD)
71.5 5.7 66.4 1 0.001
Aortic diameter [cm; mean
(range)]
3.9 (3-5.6) 2 (1.5-2.8) 0.001
BMI (kg/m²; mean SD) 27.02 4.17 25.99 3.2 0.139
Smoking 92%(56) 53.5%(30) 0.001
HTN 63%(39) 28%(16) 0.001
HPL 58%(36) 28.5%(16) 0.002
CAD 38%(24) 7.1%(4) 0.001
PVD 27%(17) 0%(0) 0.001
CVA 16%(10) 0%(0) 0.001
COPD 24%(15) 1.7%(1) 0.001
DM 24%(15) 8.9%(5) 0.030
CABG 16%(10) 1.7%(1) 0.009
Malignancy 24%(15) 10.7%(6) 0.090
Aspirin 63%(39) 23.2%(13) 0.001
Statins 74%(46) 26.7%(15) 0.001
-blockers 29%(18) 7.1%(4) 0.027
ACE-inhibitors 35%(22) 12.5%(7) 0.005
40
2.3.2 Blood results
Table 2-2:Renal functions, electrolytes and random lipid profile [mean SD,
unpaired t-test used except sodium and potassium (Mann-Whitney U test)
abbreviations: LDL, low density lipoprotein; HDL, High density lipoprotein; TGL,
triglycerides]
2.3.3 Blood pressure, plaque and CIMT measurements
All subjects had blood pressure taken in a quiet room followed by B-mode
ultrasound to assess for the presence of carotid or femoral plaque (Table 2-3)
and measurement of CIMT.
Table 2-3: Blood pressure and ultrasound measurements
(mean SD, abbreviations: BP, blood pressure; MAP, mean arterial pressure)
AAA (n=62) Control (n=56) P
Serum creatinine (mmol/L)
97.6 25.3 86.4 14.2 0.001
eGFR (ml/min/1.73m2) 69.56 20.25 80.86 15.13 0.001
Serum Sodium (mmol/L) 139.69 17.63 142.07 2.2 0.691
Serum Potasium (mmol/L)
(mmol/L)(mmol/L)(mmol/L)UNI
TS??
5.54 9.74 4.15 0.32 0.044
Cholesterol (mmol/L) 4.47 1.19 4.96 1.09 0.013
LDL (mmol/L) 2.38 1.14 2.81 1.01 0.019
HDL (mmol/L) 1.31 0.39 1.42 0.42 0.196
TGL (mmol/L) 1.66 1.08 1.53 0.86 0.595
AAA (n=62) Control (n=56) P
Systolic BP (mm of Hg) 138 19.1 129 15.3 0.010
Diastolic BP (mm of Hg) 76 9.5 73 8.4 0.248
MAP (mm of Hg) 96.6 11.8 91.1 11.2 0.151
Heart rate (beats/min) 64 12.5 64 10.5 0.507
Carotid plaque [%(n)] 84%(52) 55.3%(31) 0.001
Femoral plaque [%(n)] 92%(57) 76%(43) 0.038
41
Carotid plaque is defined as CIMT ≥ 1.5 mm as suggested by Mannheim CIMT
consensus report105. There were 6 subjects with CIMT ≥ 1.5 mm either in one or
both arteries (AAA-n=4; Control-n= 1).
Table 2-2: Carotid intima media thickness
(CIMT, mm; Median (Interquartile range-IQR). (* P=0.028 AAA vs Controls)
CIMT
Mean ± SD (mm) Median (IQR) 95%CI
All Patients (n=118) 0.91± 0.20 0.87 (0.57-1.5) 0.87-0.94
AAA (n=62) 0.95± 0.23* 0.90 (0.57-1.5) 0.89-1.01
Control (n=56) 0.86± 0.15 0.83 (0.67-1.5) 0.82-0.91
2.4 Discussion
As expected AAA subjects were vasculopaths and all known risk factors for
cardiovascular disease were common in this group. AAA subjects had worse
renal functions, while controls had marginally higher cholesterol and LDL level
than AAA subjects. The latter is likely the result of the 3-fold greater numbers of
AAA subjects using statins. AAA subjects had significantly higher systolic blood
pressure than controls, but there was no difference in diastolic pressure and
MAP between both groups. These findings are similar to those reported in most
epidemiological studies except that diabetes mellitus (DM) was more common
in our AAA group than controls9,106. There is an inverse relationship between
the presence of AAA and DM100. Our results also show that PVD was more
common in AAA patients than controls of the same age, a finding that is in
keeping with others107.
42
Atherosclerosis is present in patients with AAA16 and PVD. The prevalence of
carotid plaque in the older population is 67%108. Our results show that the
presence of carotid and femoral plaque was more common in AAA subjects
than controls. Studies have suggested that presence of carotid plaque rather
than CIMT thickness is better predictor of future CV events109-110. The mean
CIMT values in our AAA patients and controls are similar to those reported by
other studies patient cohorts of over 60yrs of age 111-113. CIMT in patients with
AAA is thicker than that in the general population of a similar age, but thinner
than patients with PVD 109,111. In our study CIMT in patients with AAA was a little
thicker than control subjects, which is not surprising, given that as AAA subjects
are generally vasculopaths and their mean age was higher than controls.
The CIMT data was also divided into quartiles for analysis and comparison with
published series114,116-117. Studies have shown that a higher CIMT quartile (Q4)
is associated with increased CV mortality, coronary atherosclerosis and thoracic
aortic calcification115, 116-118. Our data suggests that increasing CIMT is not
associated with the stiffness or diameter of the aorta, which is in keeping with
data from another study that also found no association between aortic diameter
and CIMT quartiles in the general population116. We observed that cfPWV and
diameter was greater in Q4 vs Q1 but this difference was not statistically
significant. This could be a type 2 error as our CIMT data is from just 118
subjects, whereas most CIMT studies report data from thousands of patients119.
43
CHAPTER 3
Aortic Stiffness in presence of small AAA
3.1 Background
The aorta becomes stiff with age and this can be quantified by measuring the
aortic pulse wave velocity (PWV). PWV is a measure of the time taken by the
pulse pressure wave to travel over a specific distance of artery79. The more stiff
the wall of the artery, the faster the wave moves. Aortic PWV is now known to
be an independent predictor of cardiovascular mortality in subjects older than
70years, with a 1m/s increase in PWV resulting in a 19% increase in
cardiovascular mortality84. Aortic wall stiffness may also be a better predictor of
rupture for large AAAs80.
There are direct and indirect methods to measure aortic PWV. Carotid femoral
PWV is considered the gold standard measurement of arterial stiffness79. PWV
can be measured non-invasively using pressure sensors82, Doppler probes79 or
by MRI83.
3.1.1 Carotid-femoral pulse wave velocity (cfPWV)
Carotid-femoral pulse wave velocity (cfPWV) or aortic PWV, the velocity of
propagation of pressure waves over the carotid-femoral region is a simple
robust measure of the intrinsic stiffness of the arterial wall and is emerging as
one of the strongest predictors of stroke and myocardial infarction120-123. PWV is
calculated as the distance between two measurement points (usually the
common carotid and femoral arteries) divided by the time shift to the waveforms
from these two points79. The propagation of the pulse wave is inversely related
to the distensibility of the arterial tube. In a dilated vessel the PWV is decreased
44
and this corresponds to an increase in compliance. PWV is more easily
measured property than true compliance, which requires an intra-arterial
pressure measurement to relate to the corresponding change in arterial cross
sectional area and this cannot be obtained non-invasively. Peripheral blood
pressure can be used to calculate compliance but this underestimates the true
intra-aneurysmal systolic pressure by 5% and overestimates diastolic blood
pressure by 12%85. cfPWV is measured using pressure sensors82 and Doppler
probes79 but these modalities do not provide any information on regional aortic
stiffness.
3.1.2 Brachial-ankle pulse wave velocity (baPWV)
Brachial-ankle pulse wave velocity (baPWV) can also be measured and this
provides qualitatively similar information as aortic PWV. BaPWV is a well-
established index of central arterial stiffness81 and is measured using pressure
sensors.
3.1.3 Role of MRI in assessment of AAA and regional aortic stiffness
3-Dimensional contrast-enhanced MR angiography can be used to accurately
define the location, extent, diameter and morphology of aneurysms. MRI is an
ideal imaging modality in terms of reproducibility, with low inter-observer error
for diagnosis and monitoring of AAA expansion over time. Phase contrast MRI
has been used to measure compliance in aortic aneurysms87-88 and to measure
the PWV along aortic segments in order to investigate regional aortic stiffness83.
This technique has not been used to investigate segmental stiffness in the
presence of an AAA.
45
3.1.4 Aim
To examine the relationship between AAA and aortic stiffness measured by
cfPWV and phase contrast cardiovascular magnetic resonance imaging (PC-
CMR).
3.2 Methods
All subjects (Fig 3-1) underwent applanation tonometry to measure cfPWV,
which provides a measure of total aortic stiffness. MRI was used to measure
segmental PWV along the entire length of aorta.
3.2.1 Carotid-femoral pulse wave velocity (cfPWV)103
SphygmoCor system (Atcor, West Ryde, Australia; Fig 3-1) was used to obtain
radial pulse waveforms and measurements of central arterial stiffness. These
measurements were obtained with the subject in a supine position. Applanation
tonometry of the radial artery with a high-fidelity transducer (Millar Instruments,
Houston, Texas) was used to obtain an ensemble averaged radial pulse. The
radial artery pressure waveform was calibrated to supine brachial blood
pressure. Same device and transducer was used to calculate cfPWV from
sequential recordings of electrocardiogram-referenced carotid and femoral
pressure waveforms obtained by tonometry. These measurements were made
in triplicate, and mean values were used for analysis. The path distance
between the carotid and femoral sites was estimated from the distance between
the sternal notch and femoral artery at the point of applanation. This reduces
errors introduced by multiple measurements and is closer to the true path length
than the carotid to femoral distance124. Waveforms were assessed for quality
prior to use in analysis by the automatic quality control criteria of the
SphygmoCor system.
46
Fig 3-1: SphygmoCor system and screen display of cfPWV
3.2.2 Segmental PWV using PC-CMR
Phase contrast cardiovascular magnetic resonance imaging (PC-CMR) was
performed on all participants using a 1.5T Achieva MR scanner (Philips
Healthcare, Best, NL) and a 32-element receiver coil. Regional PWV was
assessed along the 3 segments of aorta: the aortic arch between the aortic root
and proximal thoracic descending aorta (arch/ segment-1); between the
proximal and distal thoracic descending aorta (thoracic descending aorta/
segment-2); and in the abdominal segment between the diaphragm and the
aortic bifurcation (abdominal aorta/ segment-3). PWV of the total aorta (PWV-
CMR) was calculated from the combined datasets.
Aortic Flow measurements
Aortic flow measurements were performed perpendicular to the aorta at 4 aortic
levels: First flow measurement (F1), located at the level of aortic valve in
ascending aorta; Second flow measurement (F2), at same level as F1 but in
descending thoracic aorta; Third flow measurement (F3), at the level of
diaphragm and fourth flow measurement (F4), just above the aortic bifurcation
in abdominal aorta. An oblique-sagittal single-slice segmented gradient–echo
scout image was obtained to depict the course of thoracic aorta (Fig 3-2).
47
Parameters for acquisition of scout image were flip angle =30deg, echo time
(TE)/ repetition time (/TR) =1.3/ 4ms. Slice thickness =15mm,velocity encoding
limit (VENC)=150cm/s. Two transverse saturation slabs were applied
perpendicular to the aorta at the level of the aortic root and the diaphragm to
indicate the level of the sites for subsequent through-plane velocity encoded
magnetic resonance imaging (MRI) acquisition. One directional through-plane
non-segmented velocity-encoded (VE) MRI using free breathing with
retrospective ECG gating was applied perpendicular to the aorta at the levels of
the saturation-slabs in the scout image to assess the aortic flow at the three
measurement sites. Fourth aortic flow was obtained just above the aortic
bifurcation. Parameters for acquisition of electrocardiographically gated flow
sequences were FOV=220x79mm, flip angle (FA)=20deg, TE/TR=2.9/5ms.
Slice thickness =8mm,VENC=150cm/s. The temporal resolution was 4 to 7 ms.
2 signal averages were used and parallel imagining acceleration was not used.
The duration of each sequence was approximately 5mins, with a total
acquisition time of approximately 30 to 40mins. Patient position was head first
and supine. Once images were obtained data was analysed using a semi-
automatic cardiovascular flow-analysis software ((View Forum; Philips
Healthcare, Best, The Netherlands). Aortic contours were detected in each slice
location to obtain aortic flow-time curves through the cardiac cycle at the
specified sites.
Aortic Segment measurements
The distance between each flow level was measured. The 2D scout image (Fig
3-2) was used to measure segment one (arch) and segment two (descending
thoracic aorta above diaphragm) using a curved line along the center of the
aorta. The abdominal aortic segment was measured using a centerline plot and
48
multiplanar curved reformat (View Forum; Philips Healthcare, Best, The
Netherlands). This three dimensional measurement of abdominal/aneurysmal
segment was used for accuracy (Fig 3-3). 33 contiguous transverse slices
encompassing the abdominal aorta obtained using a previously described free-
breathing ECG-triggered, dual-inversion recovery black-blood 2D zoom-imaging
turbo spin echo (TSE) sequence125. Imaging parameters included: 33 slices,
slice thickness = 5mm, inversion slice thickness = 8mm, FOV = = 220mm x
67mm, spatial resolution=1x1mm, TSE factor = 25, repetition time of one
heartbeat, shortest trigger delay ~500 ms, TE = 5ms, 120ms acquisition
window, FA = 90°, low-high profile order, two signal averages, shortest trigger
delay. The inversion time (TI) was set according to the Fleckenstein formula126
to null blood.
Segmental PWV calculation:
The aortic flow data along with aortic segment measurements is used to obtain
segmental pulse wave velocity using MATLAB (Release 2009b, version 7.9,
Mathworks, Natick, MA, USA; Fig 3-4). The algorithm used to calculate PWV is
based on arrival of ‘foot’ of pulse wave, determined by intersecting tangent
method127.
PWV (m/sec) was calculated as:
vt = [d1 + d2 + d3] ÷ [d1/v1 + d2/v2 + d3/v3] (m/s)
(where dx is the aortic path length between the measurement sites and vx the
time for the pulse wave to travel along the aortic segment).
49
Fig 3-2: 2D-Scout image was used to measure segmental distance for PWV calculation.
50
Fig 3-3: Multi-planar reconstruction (MPR) was used to measure abdominal/aneurysmal
segment of aorta.
3.2.3 Statistics
Data were analysed using SPSS version 19.0 statistical software (SPSS Inc.,
Chicago, IL, USA) and Winbugs version 1.4.3. Primary outcome measures were
cfPWV and segmental PWV. Unpaired t-test was used to compare PWV
between groups. One-way ANOVA (Bonferroni correction) and paired t-test was
used when comparing PWV along the segments within a group. Wilcoxon
51
signed-rank, Mann-Whitney U and Kruskal-Wallis tests were used for non-
parametric data. A P-value < 0.05 (two sided) was considered significant.
Pearson’s correlation was used for parametric data and Spearman’s for non-
parametric data. Correlation and Bland- Altman plot128 was used to test for
agreement between PWV measured by tonometry and CMR. Stepwise multiple
linear regression models were used to examine association between cfPWV
and other known risk factors of cardiovascular disease (listed in table-2-1, 2-2
and 2-3). The significance level for variable entry into the model was a P value
< 0.1. Binary logistic models were used to test associations between AAA
disease and other risk factors including cfPWV.
WinBugs was used to calculate AAA growth rate and examine its relationship
with cfPWV and mean arterial pressure (MAP). Markov Chain Monte Carlo
(MCMC) methods were implemented using WinBugs and used to fit random
effects growth models to the data. First, a linear growth rate was assumed, and
then a quadratic model fitted and models compared using the Deviance
Information Criteria. As the quadratic model was not significantly better the
linear model was used. The intercept and growth terms were assumed to follow
a normal distribution and non-informative priors were used. Posterior
distributions were generated using 20000 MCMC samples after a run in of
10000. Average growth rate was determined using the median of the posterior
distribution of the linear component of the model and is presented with the
corresponding 95% Confidence interval (CI).
First models were fit to the data without covariates and average annual growth
rate calculated. Baseline AAA size, MAP and cfPWV were then added as fixed
terms and the influence of each on annual growth rate assessed by examining
the interaction between each fixed effect and time.
52
3.3 Results
3.3.1 Carotid-femoral pulse wave velocity (cfPWV)
cfPWV was measured in 62 AAA patients and 56 controls using tonometry.
cfPWV was significantly increased in subjects with AAA compared with controls
(mean±SEM, 12.9±0.39 vs 10.8±0.29, m/sec respectively, P0.0001; Fig 3-4 ).
Fig 3-4: cfPWV (mean±SEM) was significantly increased in subjects with AAA
compared with controls (unpaired t-test)
3.3.2 Correlation of carotid-femoral pulse wave velocity (cfPWV) and aortic
diameter
There was a significant weak correlation between cfPWV and aortic diameter
when analysing data from all subjects (Table 3-1; Fig 3-5), but no correlation
between these parameters when analysing AAA and control groups separately.
53
There was no correlation in AAA diameter and PWV in aneurysmal segments
measured by CMR .
Table 3-1: Correlation between cfPWV and aortic diameter.
Fig 3-5: Correlation between cfPWV and diameter (n=118; r=0.295, P=0.001)
3.3.1 Regression analysis
As the AAA and control groups were not matched for age (which would affect
PWV) we carried out regression analysis.
Pearson r P
AAA patients (n=62) -0.119 0.355
Control subjects (n=56) -0.055 0.688
54
Multivariate regression model Linear regression was used to determine
whether there was an association between cfPWV and AAA, independent of the
known risk factors for cardiovascular disease (listed in Table 2-1, Table 2-2 and
Table 2-3). The significance level for variable entry into the model was a P<0.1.
Multivariate regression analysis showed that only AAA disease, (B=1.47,
P=0.033),) and heart rate (B=0.05, P=0.018; Table3-2) were independently
correlated with cfPWV.
Table 3-2: Linear regression analysis of the relationship between cfPWV and AAA
R2=0.51. Total number of subjects, n=118. Variables entered in the model include: age,
HTN, diastolic blood pressure, MAP, AAA (y/n), smoking, HPL, COPD, CAD, CVA,
CABG, Cholesterol, LDL, HDL, TGL, ACEI, Statins, anti-platelets, b-blockers, presence
of carotid and femoral plaque, heart rate, major surgery, malignancy (abbreviations as
in Table 2-1,2-2 & 2-3; CI-confidence interval).
Logistic regression analysis In order to determine the best predictor of the
presence of an AAA the data was reanalysed using logistic regression. This
revealed that cfPWV, age and mean arterial pressure were independent
predictors of the presence of an AAA (Table 3-3); with the probability of having
AAA increases 3.1-fold for every 1m/s increase in cfPWV, 2.7fold for every one
year increase in age and 1.3 fold for every 1mmHg increase in MAP.
Variable
B
Lower 95%CI Upper 95%CI P Value
AAA (y/n) 1.39 0.004 2.781 0.049
Heart rate (HR) 0.05 0.006 0.096 0.028
MAP 0.12 0.045 0.194 0.002
55
Table 3-3: Logistic regression analysis summary
R2= 0.852, 0.63 (Cox and Snell). Model 2 (1)=119.95, P<0.0001. Variables entered:
Age, smoking, cfPWV, HTN, HPL, DM, COPD,CAD, systolic BP, diastolic BP, MAP,
Aspirin, b-blockers, statins, cholesterol, LDL, HDL, TGL, PVD, CVA, CABG, carotid
plaque, femoral plaque, heart rate, major surgery, malignancy (abbreviations as in
table 2-1,2-2 & 2-3;confidence interval-CI)
Variable
B (SE) Odds ratio Lower CI
CI95%CI
Upper CI
95%CI
P Value
cfPWV 1.14 (0.56) 3.1 1.04 9.51 0.042
Age 1.00 (0.41) 2.7 1.21 6.16 0.015
MAP 0.33 (0.15) 1.3 1.03 1.88 0.030
3.3.2 AAA growth rate and cfPWV
The mean baseline size of AAA was 3.9 cm (3-5.6 cm) and the mean growth
rate was 1.2mm/year. Ultrasound measurements of diameter were used to
calculate AAA growth rate (Fig 3-6). The growth rate of AAAs was related to
their initial size (with larger AAA having a faster growth rate). There was no
evidence of a relationship between AAA growth rate and cfPWV or MAP.
56
Fig 3-6: A linear growth was observed in AAA (n=62)
3.3.5 PWV measurement using phase contrast cardiovascular magnetic
resonance imaging (CMR-PWV)
CMR-PWV (meanSEM) was measured along 3 segments of aorta (arch,
thoracic descending aorta and abdominal aorta) in 88 subjects (n=46 AAA and
n=42 controls). The mean age of AAA patients was 71.3 5.7 and controls was
66.3 1 years. PWV was significantly increased in subjects with AAA compared
with controls (10.03±0.30 vs 8.4±0.24m/s, respectively, P0.0001, Fig 3-7).
57
Fig 3-7: CMR-PWV along the entire aorta was significantly higher in subjects with
AAA
3.3.6 Agreement between PWV measured by tonometry and MRI
There was a significant positive but fair correlation between PWV measured by
tonometry and MRI (r=0.519; n=88, P0.0001; Fig 3-8A). The mean (±SD) PWV
measured by MRI (9.2 ± 2m/s) was, however, significantly lower than that
measured by tonometry (11.8 ± 2.9m/s; P 0.0001). Bland Altman analysis
shows a poor agreement between both methods. From the graph we observe
that 35% (31/88) of the points lie beyond the ± 2SD lines (Fig3-8B).
58
(A)
(B)
Fig 3-8: (A) Correlation (Pearson) between PWV measured by tonometry (cfPWV)
and MRI (PWV-CMR). (B) Corresponding Bland-Altman plot showing agreement
between the two methods.
59
3.3.7 Segmental CMR-PWV measurement
Segmental aortic pulse wave velocity in AAA patients
There were no significant differences in PWV (Fig 3-9; one-way ANOVA; P=
0.640) along the three aortic segments of AAA subjects (Table 3-4; paired t-
test).
Table 3-4: Comparison of segmental PWV in AAA subjects (n=46).
Segmental comparison (AAA) PWV (m/s)
mean SEM
P
Arch vs thoracic descending aorta 9.70.56 vs 10.10.59 0.635
Arch vs abdominal aorta 9.70.56 vs 10.60.48 0.222
Thoracic descending aorta vs abdominal aorta
10.10.59 vs 10.60.48 0.525
Total Thoracic aorta vs abdominal aorta 9.90.40 vs 10.60.48 0.235
Fig 3-9: There is no change in PWV along the segments of aorta in AAA subjects (n=46; one-way ANOVA; P=0.640)
60
Segmental aortic pulse wave velocity in control subjects
The abdominal aorta in control subjects is stiffer than the thoracic aorta in the
same subject (Table 3-5; paired t-test, Fig 3-10; one-way ANOVA).
Table 3-5: Comparison of segmental PWV in control subjects (n=42)
Segmental comparison (Control) PWV (m/s)
mean SEM
P
Arch vs thoracic descending aorta 8.1 0.41 vs 8.0 0.64 0.875
Arch vs abdominal aorta 8.1 0.41 vs 10.0 0.51 0.010
Thoracic descending aorta vs abdominal aorta 8.0 0.64 vs 10.0 0.51 0.029
Thoracic aorta vs abdominal aorta 8.1 0.39 vs 10.0 0.51 0.008
Fig 3-10: PWV is greater in abdominal segment vs thoracic segment of aorta in
control subjects (n=42; one-way ANOVA; P=0.012)
61
Comparison of segmental CMR-PWV between AAA and control
CMR-PWV showed that the arch and descending thoracic segments were stiffer
in AAA patients compared with control subjects (P=0.002, Table 3-6); but no
difference was found in the abdominal segment (P=0.396).
Table 3-6: Comparison of segmental PWV between AAA subjects and controls
3.3.8 Correlation of between pulse wave velocity and aortic diameter
Overall there was a weak correlation (r=0.338, P=0.001) between total CMR-
PWV and aortic diameter (Fig 3.7), but this correlation disappeared when the
AAA and control groups were separated. There was no correlation between
AAA diameter and PWV in the aneurysmal segment measured by CMR (Table
3-6). PWV is expected to decrease with increasing diameter of conduit but this
phenomenon was not observed in subjects with AAA.
Segment AAA (n=46) Control (n=42) P
PWV1 (arch) 9.7 0.56 8.1 0.41 0.027
PWV2 (thoracic descending aorta) 10.1 0.59 8.0 0.64 0.017
PWV- Total Thoracic (PWV1+2) 9.9 0.38 8.1 0.37 0.002
PWV3 (abdominal aorta) 10.6 0.48 10.0 0.51 0.396
62
Figure 3-11: Correlation between total CMR-PWV and abdominal aortic diameter
(n=88; r=0.338;P=0.001)
Table 3-7: Correlation between PWV and diameter abdominal aorta/aneurysm
Pearson r P
cfPWV vs diameter (n=118) 0.295 0.001
cfPWV vs AAA diameter (n=62) -0.119 0.355
cfPWV vs control diameter (n=56) -0.055 0.688
CMR-PWV vs diameter (n=88) 0.338 0.001
Aneurysmal segment PWV vs AAA diameter (n=46) -0.139 0.358
63
3.4 Discussion:
Total aortic stiffness, measured by cfPWV, is an independent predictor of
cardiovascular events and is known to increase with age, while the two best-
known risk factors for AAA disease are age and male sex. We measured aortic
stiffness (cfPWV) using tonometry in men above the age of 65. Our results
show that subjects with AAA have a stiffer whole aorta than normal men of the
same age, which is not unexpected as AAA subjects have a greater vascular
disease burden. The likelihood of AAA being present increases 3.1-fold for
every 1m/sec increase in cfPWV.
We also used MRI129 to measure aortic stiffness in AAA and control subjects.
The application of CMR-PWV has also allowed the assessment of regional
changes in aortic stiffness. We found that thoracic aorta was stiffer in AAA
patients, and the relatively higher stiffness of the abdominal aorta found in
people with normal diameter aortas is lost in patients with AAA.
Tonometry and CMR have been both validated to measure total aortic stiffness
84, 121-123 and CMR has been used to measure segmental stiffness. We used
both methods to measure aortic stiffness and observed fair agreement between
methods as described previously130. Our finding that that PWV in the abdominal
segment was increased in subjects with normal sized aortas is in agreement
with previous studies, when measured by either MRI83,130 or invasive catheter
131.
We did not find any correlation between aneurysm diameter and PWV of
aneurysmal segment. We also carried out a linear regression and this showed
that thoracic PWV is significantly and positively correlated to abdominal aortic
64
diameter and systolic blood pressure. It has been shown that age related
increase in aortic diameter and tortuosity occurs in thoracic aorta particularly
aortic arch130.
Age can cause changes in regional aortic stiffness and is most strongly
associated with a stiffer abdominal segment83. We found the same in our
control group (men with a mean age of 66.5years). Measurement of segmental
aortic stiffness by CMR along 3 segments of the aorta in our study is in
agreement with the reported work83,130. Our use of this technique in assessing
segmental aortic stiffness in patients with AAA revealed that this cohort had a
stiffer thoracic aorta than controls. There was no difference in the stiffness of
the aneurysmal segment in AAA compared with the abdominal segment in
control subjects. Regression analysis of the cfPWV and thoracic CMR-PWV
shows that both are associated with AAA, independent of any other risk factor.
AAA growth rate in our group was 1.2mm/year, while others have reported the
AAA diameter increases of 3mm/year132. Our analysis suggests that there is no
relationship between cfPWV and growth rate of AAA. However, this analysis is
retrospective and limited by the small numbers (62 patients) and firm
conclusions regarding this relationship cannot be drawn.
Only a few studies have measured PWV in patients AAA. In one study following
infra-renal AAA repair; PWV in descending thoracic aorta was measured using
Doppler echocardiography. Their results suggested that subjects with ruptured
AAA had a low PWV (high compliance) in the thoracic aorta 86. They postulated
that high thoracic aortic PWV (low compliance) prevented AAA from rupture.
They also concluded that a higher thoracic compliance might be related to
faster growth of the AAA and earlier rupture. In another study total PWV was
65
measured in large aneurysm with tonometry before and after surgical repair and
again finding an increase in PWV post-operatively when a graft is in place133.
The stiffness of aortic wall samples taken at operation has also been related to
the risk of rupture of large AAA 80. Thoracic aortic stiffness may be of prognostic
value for the occurrence of aortic dilation or aneurysm.
Pulse wave imaging technique134,135 using B-mode ultrasound has been used to
measure aortic or aneurysm PWV in humans and mice. PWV was measured
initially in an elderly subject with AAA and it was no surprise that it was higher
than young healthy volunteers. The same technique was also used to show
differences in pulse wave propagation in normal, sham and Ang-II treated
aortas of mice56. The pulse wave moved significantly non-uniformly in Ang-II
treated aortas, but there was no difference in PWV between sham and Ang-II
treated aortas.
The most significant findings of the present study are that patients with AAA
have a significantly stiffer aorta and this is accounted for by an increase in
thoracic aortic stiffness. The normal ratio of relatively higher abdominal segment
stiffness found in control subjects, however, is lost in patients with AAA.
Patients with aneurysms have therefore a generalised increase in stiffness but
with a localised loss of stiffness associated with the dilated segment.
An expert consensus document on arterial stiffness has suggested that “a
change in pulse wave velocity along segments of the aorta may detect changes
in local compliance that are a precursor to AAA formation before a change in
aortic diameter is detected”79. Our study is the first to measure and compare
aortic stiffness (PWV) in patients with AAA and normal aorta. The data that we
present add further support to the notion that patients with AAA have a stiffer
66
abdominal aorta prior to development of an aneurysm. This leads us to
speculate that aneurysm development may be a remodeling response aimed at
reducing aortic stiffness.
67
CHAPTER 4
Aortic calcification
4.1 Background:
Calcification of the arterial wall is a marker of atherosclerosis136 and is
more common in patients with hypertension, diabetes mellitus and renal
failure137. It has been extensively quantified in coronary and carotid beds to
predict CV morbidity and mortality116,138-139. It is also a major factor contributing
to arterial stiffening140,141. Abdominal aortic calcification is also an independent
predictor of subsequent vascular morbidity and mortality142. Calcium score
provides an accurate estimation of the coronary atherosclerotic burden and is
powerful predictor of cardiac events in asymptomatic patients. CT Calcium
scoring is an established tool for initial risk assessment, but has currently limited
value as an endpoint in serial progression/regression trials143. Atherosclerosis
and arterial stiffness has a modest correlation144.
Autopsy analysis shows that 82% of AAA ruptures occur at the posterior
surface, where there is focal elevated stress and where there is most
calcification38. There is evidence that a calcified aneurysm has a 3-times
increased risk of rupture than a non calcified aneurysm145.
4.1.1 Calcium scoring systems
There are a number of scoring systems for measuring the calcium burden in the
vasculature. Most methods were developed to score calcium in coronary
arteries using electron beam CT images. The Agatston calcium score146 is the
68
most commonly used and validated system, that scores calcium levels using CT
images. Score obtained by this method is also called traditional calcium score
(TCC). In this method computer software multiplies the area of calcification with
an assigned coefficient selected on the basis of the highest attenuation
measured in the area. However, this system has its limitations. There are
problems with noise artefacts and variations in the scanning protocol, which can
lead to variations in reproducibility147. The other commonly used scoring
system is ‘ calcium volumetric score’ (CVS) and has excellent reproducibility148.
This system is based on a value that represents volume calculated with
isotropic interpolation technique149. Aortic calcification has also been measured
using lateral lumbar radiographs and calcium quantified using a validated rating
scale142. Another method that uses FDG-PET and CT, describes a combined
scoring system, which divides patients in 4-groups on the basis of presence of
calcification and uptake in the thoracic aorta68.
Vascular calcification
Calcium apatite is the type of calcium crystal that mostly accumulates in the
blood vessel wall26. Vascular calcification is a complex mechanism involving
deranged mineral and lipid metabolism, inflammation, apoptosis, osteogenesis
and matrix degradation. Calcification mechanisms have similarities with
mechanism of bone formation150. It has been suggested that main target of
calcification is vascular smooth muscle cells (VSMCs)151. Some known
circulating inhibitors of calcification include fetuin-A, matrix-GLA-protein (MGP)
and osteoprotegrin/rank ligand (OPG/RANKL). Vascular calcification is
regulated by osteogenic agents (fetuin-A, MGP, OPG and RANKL) released by
microparticles in response to mechanical and cytokine-induced stress29.
69
Fetuin-A promotes anti-apoptic activity in VSMCs152. Inhibitors, including OPG,
are present in VSMC derived micro-particles (VSMMPs) and are thought to play
a key role in matrix degradation and calcification. Increased circulating OPG
has been weakly associated with aneurysm progression in man32. In this study
we chose to measure fetuin-A as a biomarker of calcification. This protein
(originally known as alpha2-Heremans Schmid glycoprotein; AHSG) is a liver-
derived partially phosphorylated glycoprotein that circulates at about 700mg/l
and acts as a mineral chaperone that mediates mineral transport from the
extracellular space and general circulation153. A fraction of the total circulating
fetuin-A interacts with insoluble mineral nuclei to form soluble colloidal
calciprotein particles (CPP), preventing calcium crystal deposition154. It has
been shown that low serum levels of fetuin-A have associations with raised C-
reactive protein (CRP)155 and cardiovascular death156. Fetuin-A inhibits
calcification of blood vessels30,157 and has anti-inflammatory properties158.
Increased aortic valvular calcification is associated with low fetuin-A levels in
serum159.
Vascular calcification is a regulated process that involves deposition of
osteoblast-like cells in vessel wall and loss of inhibitors of mineralization or
biomarkers of calcification like OPG, MGP and Fetuin-A28. Although the
relationship between circulating biomarkers of calcification and aortic
calcification on imaging has yet to be defined, there is evidence to show that
low circulating fetuin-A levels are associated with greater aortic calcification160.
70
Aims
(i) To quantify and compare aortic calcification in AAA and control groups using
non-contrast CT.
(ii) To study the relationship between aortic calcification and stiffness (PWV).
(ii) To investigate the relationship between aortic calcification (CT) and
circulating fetuin-A levels
4.2 Methods
4.2.1 Calcification measurement using non-contrast CT
Non-contrast enhanced images were obtained on a 16-slice helical scanner with
a 3mm slice thickness (Brilliance CT, Philips, Eindhoven, Netherlands). Osirix
imaging software version 3.9 was used to measure the calcification (Electron
beam, voxel threshold 130HU; Fig 4-1). The volume method of calcium
scoring161 included in system software was used to measure calcium burden in
the aorta. The volume of the wall occupied by calcium deposits (CVS) is a
validated, accurate technique162 and has excellent repeatability148,161. Areas of
>1mm2 calcium in one or more slices of aorta (voxel volume X number of voxels
in region of interest) were summated. The calcium was measured from the
aortic valve to just above aortic bifurcation in every slice. Anatomical landmarks
were used on CT and CMR to divide aorta into three segments (arch, thoracic
descending aorta and abdominal aorta) as for PWV-CMR. The slice thickness
71
was increased to 5mm for quantification of calcium.
Fig 4-1: Transverse section of abdomen illustrating calcium in the aortic wall (A). Calcium
coloured red after use of calcium scoring tool (B)
AAA diameter measurement:
AAA was measured on CT scan (anterior to posterior) for all subjects who had
segmental PWV analysis. However, if a patient was lost to follow up prior to CT
scan then ultrasound diameter measurement was used (regression models).
Biomarker analysis
Citrated blood samples were collected at recruitment (n=118) and plasma
obtained following centrifuging (1500g) for 15min within 1hr of collection. The
plasma was aliquoted and stored at -20oC. FetuinA analysis (free and VSMPP-
associated) was carried out by one of our collaborators (Prof Catherine
Shanahan, KCL). This method is currently undergoing a patent application.
Aortic calcification was quantified on non-contrast CT (n=77).
4.2.2 Statistics
Data were analysed using SPSS version 19.0 statistical software (SPSS Inc.,
Chicago, IL, USA). Wilcoxon signed-rank, Mann-Whitney U or Kruskal-Wallis
tests were used for non-parametric data. A P-value < 0.05 (two sided) was
72
considered significant. Pearson’s correlation was used for parametric data and
Spearman’s for non-parametric data.
4.3 Results
4.3.1 Aortic calcification in AAA and control subjects
Aorta in AAA subjects was heavily calcified in comparison to control subjects
(Fig 4-2; P 0.0001). Calcification data was skewed, and therefore reported as
median (interquartile range-IQR). Calcification of the entire aorta in AAA (n=39,
710.86 years) and control subjects (n=38, 660.17 years) from aortic valve to
just above bifurcation was 6.690.88 (range 0.01-26.25) and 1.3810.31cm3
(range 0-8.96cm3) respectively. Comparison of segmental aortic calcification
between both groups is shown in Table 4-1.
Fig 4-2: Aortic calcification in AAA (6.6890.88) and control subjects (1.381 0.31)
73
Table 4-1: Comparison of segmental and total aortic calcification in AAA and
control aortas (median and IQR; Mann-Whitney U test and Kruskal-Wallis test).
4.3.2 Aortic calcification along the aortic segments
There was a significantly greater amount of calcium present in the segments of
aortae of patients with AAA than in the corresponding segment in the control
aortae, although the pattern of calcium deposition along the aorta was similar in
both groups (Table 4-1). The thoracic descending aorta was least calcified
whereas abdominal aorta was most calcified, with arch calcification 2-fold
higher than in the thoracic descending aorta, but ~6-fold less than in the
abdominal aorta (Fig 4-3; Fig 4-4).
Aortic calcification (cm3)
Aortic segment AAA
(n=39) Control (n=38)
P
Entire aorta (median/IQR) 6.28 (2.05-10.15) 0.675 (0.228-1.3) 0.001
Arch 0.736 (0.322-1.687) 0.078 (0.001-0.271) 0.001
Thoracic descending aorta 0.318 (0.009-1.036) 0.000 (0-0.045) 0.001
Abdominal aorta 4.233 (1.459-6.987) 0.494 (0.157-0.987) 0.001
Thoracic (arch+ thoracic descending aorta)
1.051 (0.411-2.875) 0.105 (0.001-0.29) 0.001
74
Fig 4-3: Distribution of calcium along the aorta in AAA subjects (n=39).
Abdominal aorta has significantly higher calcium content than the total thoracic aorta
(P0.0001).
75
Fig 4-4: Distribution of calcium along the aorta in control subjects (n=38). The
abdominal aorta has significantly higher calcium content than total thoracic aorta
(P=0.0001).
4.3.3 Relationship between calcification and aortic stiffness (PWV)
77 subjects completed both non-contrast CT and CMR. In AAA patients
(710.86 years), calcium deposition correlated with PWV in the entire aorta
(R=0.45, P<0.0001; Fig 4-5) and the thoracic segment (R=0.45, P=0.004), but
not in the abdominal segment (Table 4-2). Correlation of segmental PWV with
calcium score was also done separately for both groups but no correlation was
found except in thoracic aorta of AAA subjects (Table 4-2).
76
Fig 4-5: Correlation between aortic PWV and calcium (normal log transformation)
in whole aorta of both AAA and control subjects (n=77; Spearman’s r 0.451; P
0.0001)
77
Table 4-2: Correlation between PWV and calcium score (n=77) [AAA (n=39;
710.86 years) and control subjects (n=38; 660.17 years)]. Significant correlation is
marked with *.
Control,
n=38
Spearman’s r P value
Arch 0.167 0.317
Thoracic descending aorta 0.193 0.245
Thoracic aorta (arch + thoracic descending aorta) 0.159 0.340
Abdominal aorta 0.067 0.690
AAA,
n=39
Arch 0.303 0.061
Thoracic descending aorta 0.341 0.033*
Thoracic aorta (arch + thoracic descending aorta) 0.453 0.004*
Abdominal aorta 0.065 0.696
Entire aorta 0.262 0.107
AAA + Controls
n=77
Thoracic aorta 0.422 0.001*
Abdominal aorta 0.169 0.143
Entire Aorta 0.451 0.001*
Aorta 0.194 0.243
78
4.3.4 Fetuin-A analysis
Fetuin-A concentration in vesicle and plasma was measured in 35 subjects who
had a matching calcium score and from which there was sufficient sample
volume for analysis. Nine subjects were excluded because of inadequate
sample volume (n=7) and no CT available for calcium quantification (n=2). The
mean (SD) vesicular fetuin-A concentration and levels in plasma were 3.8
3.0mg/l and 1304570mg/l respectively. We found weak inverse correlations
between both vesicle (Spearman’s r = -0.459; P= 0.018) and plasma fetuin-A
(Spearman’s r = -0.562; P= 0.003) concentration and CT calcium score (Fig 4-6;
Fig 4-7). No correlation was observed between aortic stiffness (cfPWV) and
fetuin-A concentration in vesicle (r=143; P=0.486) or plasma (r=0.035; P=
0.864). There was no difference in fetuin-A concentration in AAA and control
subjects (Table-4-3).
Fig 4-6: Correlation between CT calcium score and micro-vesicle fetuin-A
concentration
79
Fig 4-7: Correlation between CT calcium score and plasma fetuin-A
concentration
Table 4-3: Vesicle and plasma fetuin-A concentration in AAA and controls.
The levels of both free plasma and bound (vesicle) forms of fetuin-A were similar in
both groups (P>0.10, unpaired t test)
Fetuin-A concentration
(mg/l, mean SD)
Fetuin-A source AAA (n=17) Control (n=9) P value
Plasma 1218 623 1465. 437 0.303
Vesicle 3.71 3.46 3.91 1.99 0.876
80
4.4 DISCUSSION
Our objective was to explore the relationship between segmental aortic stiffness
and regional aortic calcification. We used the same segmental division for CT
analysis as used for MRI scanning to measure PWV (Chapter-3). Calcification
was significantly greater in the entire aortae of AAA patients than those of
control subjects, with the abdominal segment most calcified in both groups.
There was a significant correlation between aortic stiffness and aortic
calcification (as measured by CMR-PWV and CT-scan respectively) when the
entire aorta in both AAA and control cohorts was analysed. This correlation
was, however, lost in the abdominal segment and was found only in the thoracic
segment when the AAA and control subjects were analysed independently.
Segmental correlations have been observed in healthy subjects (average age
65years) in previous studies163, but the strength of the relationship was poor in
the ascending aorta and described as only ‘fair’ in both the descending thoracic
and abdominal aorta.
CT analysis of calcification in patients with chronic kidney disease shows a
relationship between abdominal aortic calcification and aortic stiffness164-165.
Comparison of our results with other studies, such as these, is however, difficult
as pathology may vary, the methods chosen in some studies are subjective,
and the segmental division of aorta may also be different. There is no standard
classification scheme for scoring aortic calcification,166 with a variety of ways
being described142,148. Our data shows that most calcium is deposited in the
abdominal aorta in both healthy and aneurysmal aortas. We have also shown
that the abdominal aorta is stiffer in healthy controls than thoracic aorta
(Chapter-3). We did not, however, find any segmental correlation between
81
calcification and stiffness in abdominal aorta (Table-4-2), which suggests that
although calcification may contribute to aortic stiffness, it may not be the only
factor responsible for stiffness22.
The mean plasma fetuin-A concentration is in the subjects assessed for calcium
score in this study were almost 2-fold higher than those previously reported in
the normal population (0.7g/l)153. One reason for this could be that our group
includes men above the age of 65. The fetuin-A level were the same in both
AAA and control groups. I can find no evidence from the literature to show how
serum fetuin-A levels fluctuates with age.
There was a significant inverse relationship between plasma fetuin-A levels and
aortic calcification measured on CT from aortic root to bifurcation in our study.
This was not replicated when assessing the correlation between vesicular
fetuin-A. A similar inverse relationship between plasma fetuin-A and aortic
calcification has been reported, but the calcified area of abdominal aorta was
measured only at the level of L3 and L4 from CT160. Similarly in another study
non-vesicular fetuin-A negatively correlated with coronary artery calcification
scores (CACS), however there was an absence of correlation between serum
fetuin-A and CACS167. Experiments in animal models have shown that fetuin-A
deficient mice develop micro-calcifications168.
We did not find a correlation between aortic stiffness (cfPWV) and fetuin-A
levels. Our study was, however, limited by small number of subjects who had
fetuin-A analysis performed. In a larger study (n=185) increased phosphorylated
fetuin-A CCP levels were independently associated with increased aortic
PWV169.
82
CHAPTER 5
AAA imaging using FDG-PET/CT
5.1 Background
PET offers the possibility of using radiotracers to image functional activity in the
wall of the aorta. Currently one of the tracers with clinical application is the
glucose analogue 18-fluorine (18F) flurodeoxyglucose (FDG). It enables
detection of increased glucose metabolism. Increased uptake is characteristic of
many cancers and other non-malignant processes such as infection and
inflammation68. It could therefore potentially assess the metabolic activity in the
aneurysmal wall69. FDG uptake is expressed as standardized uptake values
(SUV), for tissue attenuation. The SUV is calculated either pixel-wise or over a
region of interest for each image.
Atherosclerosis is more prevalent in abdominal aorta and in combination with
PET/CT, segmental measures of PWV will distinguish between stiffening
associated with different patterns of calcification in the thoracic and abdominal
aorta. The pattern of calcification and co-localisation with metabolically active
plaque will determine whether calcification is likely to be primary or secondary
to atherosclerosis.
5.2 Methods:
The scan consisted of scout CT, attenuation correction CT scan, non-contrast
enhanced CT and 3 dimensional FDG scan. PET/CT fusion images (Fig 5-1)
were used to co-register calcification with aortic wall metabolic activity. These
83
PET-CT fusion images68 were scored for tracer uptake and enhancement. FDG
uptake on PET/CT images was co-related with measures of plaque burden
quantified on CMR. (REC reference: 10/H0802/89)
5.2.1 FDG-PET/ CT scan
The scans were acquired using a Discovery volume PET/CT scanner (VCT ;
General Electronics Health Care). As with PET scans performed for
investigating oncological, inflammatory and infectious processes, the patients
were starved for at least 6 hours prior to the scan. This is to minimise
background uptake from the gastrointestinal track that would obscure the
abdominal aortic 18F-FDG uptake. An intravenous injection of 300 to 400 MBq
of 18F-FDG is administered through an indwelling catheter. The patient is sat in
a quiet injection room during the subsequent 60-min of 18F-FDG uptake phase,
before the acquisition of the scan. This is normally 90 minutes for oncological
studies but was reduced for vascular imaging as optimal signal to background
uptake. Images are then acquired with an emission scan photons collected from
the patient) and transmission scan (using an external gamma source). We
performed a classical acquisition from the aortic arch to the femoral heads over
30minutes. The effective radiation dose being 0.025mSev/MBq. The sequences
were recorded at each couch position for 3 minutes. Coronal, sagittal and
transaxial images were based on ordered subset expectation maximisation
iterative reconstruction algorithm with post-injection segmented attenuation.
The CT scan was carried out of the arch, thoracic and abdominal aorta (CT-
parameters used are outlined in Appendix 8.1). A scout CT scan is performed
with helical CT from the region of interest namely arch to femoral heads.
Following the PET scan a non-contrast CT is performed. The patient setup was
head first, supine, wedge under the knees and arms were placed above the
84
patient’s head for CT acquisitions (arms down if patient was unable to raise).
5.2.2 FDG-PET/CT Image Processing
Co-registration was carried out at a Hermes workstation (HERMES Medical
Solutions Inc. Canada).
5.2.3 FDG-PET/CT Image analysis
A sub set of patients (n=10) had 18-F FDG-PET/CT scan. PET/CT was used to
identify calcification and metabolically active atheromatous plaque using FDG
as a marker170. Images were analysed using Osirix imaging software version
3.9. The areas of most intense aortic wall 18F-FDG uptake were identified on
each slice during a slice-by-slice analysis and regions of interests were drawn
(ROIs) over the aortic wall and lumen. The maximum activity concentration for
each region was recorded. For each period of dynamic PET imaging, the mean
maximum activity concentration (that is corrected for decay) was recorded and
used to determine maximum standardised uptake value (SUVmax) that was in
turn normalised to body weight of the patient. This is calculated using the
formula below:
ROI decay – corrected activity (kBq/ tissue (mL)
SUV = _______________________________________
Injected 18F-FDG dose (kBq/body weight (g)
The associated standard error (SE) (SD/√4) was calculated. This calculation is
repeated in the patients to ensure that the mean SUVmax for the aortic wall and
the lumen and the target-to-background (TBR) ratio is calculated. Standardised
uptake values (SUVs) > 2 for all regions of FDG uptake within aortic lumen and
85
wall was considered significant (Fig 5-2). SUVmax of liver and blood was
measured.
5.3 Results
Eleven subjects had 18-F FDG-PET/CT scan. One subject was excluded from
analysis because he failed to have a CMR (excluded due to suspicion of metal
inside body) for plaque imaging. The mean AAA diameter was 4.24 cm (3-5.6).
The SUVmax was 2.617 and 1.865 in liver and blood respectively. Abdominal
aorta was divided into two segments aneurysmal and normal aorta. FDG-PET
uptake (> 2 SUVmax) was seen in the aneurysmal lumen and normal aortic
segment of 50% (n=5) patients. Only two subjects (20%) showed an uptake of
FDG (> 2 SUVmax) within the aneurysm wall (Fig 5.1 and 5.2). The wall of
normal aorta did not show an uptake > 2 SUVmax. There was no correlation
between plaque burden, aortic calcification and FDG uptake.
Fig 5-1: Fused PET/CT
86
Fig 5-2: Image showing a FDG uptake of > 2SUVmax
5.4 Discussion
The mean AAA diameter of this small sub-group (n=10) that underwent PET/CT
was 4.24 cm (3-5.6). In one study the mean SUVmax of patent vessel has been
reported 1.9 171. In our group of subjects the mean SUVmax of blood within
aorta was 1.86. Only five subjects (50%) had FDG uptake >2 SUVmax in
aneurysm lumen and similarly four had uptake in the lumen of non-aneurysmal
aorta. FDG-uptake of >2 SUVmax was observed in the aneurysm wall of only
two subjects. Our results are in agreement with previous studies done to show
FDG uptake in small AAA subjects66,171-173.
It has been suggested that a finding of increased FDG uptake is suggestive of
plaque destabilization and creates concern about adverse events174-175.
However, I did not have sufficient data to observe any correlation between
metabolic activity shown by FDG uptake and calcification or aortic stiffness.
87
CHAPTER 6
A novel ultrasound scanning device (AortaScan)176 for
screening of AAA in the community
6.1 Background
Screening men for abdominal aortic aneurysm (AAA) with a single ultrasound
scan prevents deaths from rupture177-179. A National Screening Programme of
men aged 65years been introduced across England and aims to have full
coverage by 2013180. Men are invited to visit a screening location, usually in the
community, for an abdominal ultrasound scan, carried out by a trained
screening technician using a portable scanner.
The NAAASP (NHS Abdominal Aortic Aneurysm Screening Programme) will
require nearly 500 whole-time equivalent screening technicians to undertake all
the scanning in England181. These technicians are not expected to have prior
understanding of aortic anatomy or experience of ultrasound scanning. A
training protocol has been established for these technicians consisting of formal
didactic training, a minimum number of supervised scans and a competency
assessment182. It may take up to 6months training before each technician is
qualified to scan independently within the programme181.
The success of the screening programme will critically depend on not only
training the technicians to accurately measure aortic size but also retaining staff
once trained. Early experience with this model of screening suggests that staff
training and retention may be the greatest challenge faced by the programme.
88
The use of a portable scanning device that automatically detects AAA with a
high degree of accuracy would eliminate these training problems. The
AortaScan BVI 9700 (Verathon Medical UK Ltd) is a portable 3D ultrasound
instrument (Fig-8) that may have this ability.
6.2 Aims
To evaluate the accuracy of a novel portable ultrasound scanning device, the
AortaScan BVI 9700, in the detection of AAA.
6.3 Methods
Subjects from our local AAA screening and surveillance programs were
recruited from January 2010 to April 2011. These subjects were either known to
have AAA (based on previous ultrasound, CT or MR imaging) or a normal aorta
(based on screening using conventional ultrasound). AAA was defined as
diameter >3cm. Subjects with body mass index (BMI) >35 kg/m2 were excluded
from the study to limit CT radiation dose. This study is a part of National
Institute for Health Research (NIHR) programme into aortic disease approved
by the local research ethics committee (07/Q0702/62). A short medical history
was taken and BMI was measured on recruitment.
6.3.1 AortaScan
All subjects underwent assessment with the AortaScan AMI 9700 (Verathon
Medical UK, Fig 6-1).
89
Fig 6-1: AortaScan AMI 9700183
This device automatically assesses the aortic diameter with minimal user skills.
It uses a 13mm diameter 3.0 MHz transducer with a penetration depth of 18cm.
The aorta is detected using differences in echo-density between the vessel and
surrounding tissues, and the device derives the diameter from the measured
sac volume. All scans were performed by the same operator (MC) blinded to the
presence of AAA. Prior to the study the operator was trained to use the device
according to the manufacturer advice. This training consisted of reading a four-
page instruction booklet, watching the onboard video tutorial (4mins) and a
10min familiarisation session with a representative from the device
manufacturer.
Subjects were scanned supine in a temperature-controlled relaxed environment.
The abdomen was exposed and the scan performed according to the
90
Instructions For Use183. The probe is sequentially placed at four midline
positions from the xiphisternum to the umbilicus. Scanning is activated by
pressing a button on the probe and takes less than 3secs at each position. The
aortic maximum diameter is calculated and a colour LCD screen display
indicates either a normal aorta or the aortic diameter if greater than 3cm (Fig 6-
2). The greatest diameter, measured at each of the 4 positions, was taken as
the value to compare to the true maximum diameter (determined by CT). The
data were uploaded to a dedicated image management software package
(ScanPoint® 3.00; Fig 6.3) and database183.
Fig 6-2: AortaScan screen display once aneurysm is detected183
91
Fig 6-3: B-mode image on ScanPoint: aneurysm measuring 4.5cm(v mode) and
4.2cm(manual)
6.3.2 CT-Scan
All patients underwent aortic CT within 8wks of the AortaScan. Non-contrast
enhanced images were obtained on a 16-slice helical scanner with a 3mm slice
thickness (Brilliance CT, Philips, Eindhoven, Netherlands). Osirix (v3.9) imaging
software was used to measure the aorta. AAA was defined as an aorta with an
anterior-posterior (inner to inner) diameter of ≥3cm.
6.3.3 Statistics
Differences between the control and AAA groups were analysed using unpaired
t-test for continuous data. A P-value less than 0.05 (two sided) was considered
significant. Sensitivity and specificity was calculated using contingency table.
The data was analysed using SPSS version 19.0 statistical software (SPSS
Inc., Chicago, IL, USA).
92
6.4 Results
A total of 91 subjects underwent imaging with AortaScan and CT (mean age
68.5 years). 43 subjects had AAA confirmed by CT and 48 subjects had a
normal sized aorta (<3 cm diameter). The mean CT-aortic diameter was 3.7 cm
(range 3-5.5 cm) in AAA subjects and 2 cm (1.5-2.9 cm) in control subjects.
Subject demographics are shown in (Table 6-1).
Table 6-1: Subject demographics
The AortaScan correctly detected AAA in 35/43 subjects (sensitivity 81%) with
false positives in 13 subjects who did not have AAA (specificity 72%). The
positive and negative predictive values were 72% and 81% respectively (Table
6-2).
AAA (n=43) Control (n=48) P-value
Age (years) 71 (64-81) 66 (65-68) 0.0001
CT-Aortic diameter (cm) 3.7 (3-5.5 cm) 2 (1.5-2.9 cm)
Body mass index (kg/m2) 26.43 (16-35) 26.10 (19-33) 0.647
93
Table 6-2: Contingency table showing accuracy of AortaScan vs CT-scan
The aneurysms missed by the AortaScan ranged from to 3.6 cm to 4.4cm in
size (mean 3.85 cm). There was no difference in mean BMI between the two
groups. Comparisons of AortaScan results from patients with normal BMI (18.5-
25 kg/m2) and abnormal BMI (<18.5 or >25 kg/m2) showed no effect of BMI on
AortaScan accuracy (Fig 6-4, P= 0.157, Chi2 analysis).
AortaScan
BVI 9700
CT Scan
AAA (yes) AAA (no)
AAA (yes) 35 13 Positive predictive value
72%
AAA (no) 8 35 Negative predictive
value 81%
Sensitivity 81% Specificity 72%
94
Fig 6-4: BMI had no significant effect on AortaScan results (P=0.157 Chi2analysis)
6.5 Discussion
Consensus exists across guidelines on one-time screening of elderly men to
detect and treat AAAs 5.5cm184. Screening for any disease requires a test that
is accurate, easily available and cost-effective (i.e. the use of relative
inexperienced scanning technicians rather than trained sonographers). The
AortaScan AMI 9700 is a novel automated ultrasound device designed to detect
the presence of an AAA, with minimal training of the operator185. In this study
we have compared the result of assessment of aortic size by AortaScan with
that of the ‘gold’ standard measure of anatomical size, CT, in order to examine
the accuracy of this device and to determine whether the AortaScan could have
a role within an AAA screening programme.
95
The manufacturers of the device do not claim that the AortaScan will provide an
accurate size of the aorta, only that the device indicates that the aorta is either
normal (i.e. <3cm diameter) or >3cm diameter (with an estimate of size if the
latter). The AortaScan should therefore only be considered for screening rather
than surveillance. Patients with detected small aneurysms would need to enter
a surveillance programme that employs conventional ultrasound
measurements. Future developments of the device may overcome this
limitation.
The results of our study show the sensitivity of the AortaScan is 81% when
compared with CT. The sensitivity of the device in its current form is therefore
not sufficient for its introduction into an AAA screening programme. Factors that
affect accuracy may include patient morphology, vessel tortuosity and the
presence of thrombus within the sac. Although we found no significant
relationship between incorrect results and BMI there was a trend towards a
greater BMI in those subjects with false negative results. No patients in this
study had severe tortuosity, and as the CT scans were performed without
contrast, we were unable to analyse whether thrombus had any effect on
accuracy. We understand the manufacturers are currently working to increase
the accuracy for the next generation of the device.
In this study we have confined testing to patients with small (i.e. <5.5 cm
diameter) AAA. These would make up the majority of AAA detected in the
screening programme37. It could be anticipated that for larger AAA the device
would be more sensitive.
96
Assessment of the accuracy of the previous AortaScan model against
conventional ultrasound rather than CT showed a 90% sensitivity and 94%
specificity to detect AAA45. The conventional ultrasound also suffered from a
number of false positives and false negatives, rendering the results difficult to
interpret. Little information was reported regarding factors that affected false
results. One possible explanation for a greater sensitivity in that study is that the
mean aortic diameter was larger than in ours (3.9 cm vs 2.8 cm).
Evidence is emerging that patients with dilated aortas that do not currently meet
the definition of AAA (i.e. measuring 2-3 cm in diameter) may also dilate over
time and eventually be at risk of rupture186. These patients may in future be
included on surveillance programmes and so devices such as AortaScan will
also need to be able to accurately detect these small diameter aortas.
Trials demonstrating the efficacy of AAA screening have used trained
sonographers to screen a limited population13 37. The number of scans,
however, required in a national programme far exceed the capacity available for
sonographers to perform, and so the model adopted by the NAAASP relies on
training personnel previously unskilled in ultrasound to perform the screening
scans. Although conventional ultrasound can achieve a very high sensitivity and
specificity this requires a prolonged training programme to achieve a minimum
level of operator ability. There are concerns that in practice it may be difficult to
achieve a consistent level of quality from inexperienced scanning personnel. An
additional challenge is to maintain staff retention after training. An automated
ultrasound scanner to detect AAA would eliminate these problems.
97
The availability of an automatic device to accurately detect AAA with minimum
operator training would greatly increase the chance of delivering a
comprehensive and effective screening programme.
(This chapter is from my published work176).
98
CHAPTER 7
General discussion and future studies
Abdominal aortic aneurysmal disease (AAA) is a significant contributor to
morbidity and mortality, in particular in men over the age of 65 years.
Understanding the processes that lead or contribute to aortic dilatation, in
conjunction with effective screening for this condition, should significantly
improve our management of this disease. The aims of my thesis were therefore
to investigate the relationship between aortic stiffness, calcification and AAA,
and to assess a novel ultrasound-based device for use in AAA screening
programmes.
7.1 Evaluation of tonometry and CMR to measure aortic
stiffness
cfPWV is gold standard way to measure aortic stiffness121-123,187 but it does not
give any information on regional aortic stiffness. The concept of regional aortic
stiffness became popular because these changes in aortic stiffness may
precede aortic remodeling or aneurysm formation. In the present study,
tonometry and CMR had a modest agreement in the measurement of aortic
PWV. PWV-CMR produced PWVs that were lower than those measured by
cfPWV, which is in keeping with the results of others130. This is most likely as a
result of the different path measured by each method. cfPWV includes carotid
and iliac arterial beds, whereas CMR only measures PWV from aortic root to
bifurcation of aorta.
99
MRI has been used to measure PWV along the aortic segments to show
changes in regional aortic stiffness83. PWV tends to increase in the distal
segments of aorta130 suggesting the distal aorta is stiffer, data that has been
confirmed by invasive catheter measurements131. Post-operative measurement
of PWV by Doppler echocardiography of the descending thoracic aorta of
patients with infra-renal AAA, has suggested that a low PWV or high
compliance in the thoracic aorta leads to AAA rupture86 .They also suggest that
a PWV > 15m/sec is associated with a very rigid aorta, therefore in such
subjects AAA may rupture due to increased wall stress, this observation is
based on data from two subjects. Finally, they concluded that a higher thoracic
compliance might be related to faster growth of the AAA and earlier rupture.
However, the main limitation of their study is that PWV is measured in thoracic
aorta following AAA repair and sample size is small.
7.2 Aortic stiffness and AAA
Aortic stiffness or pulse wave velocity is an independent predictor of
cardiovascular events and is known to increase with age. The aorta is subject to
repetitive cyclic stress, which may lead to fragmentation and breakdown of the
elastic components of the aortic media, particularly elastin and collagen. One
hypothesis is that these histological changes lead to stiffening and dilation of
the aortic wall21. Indeed, there is a well-established relationship between age-
related increase in aortic stiffness and diameter130. Preliminary observations
also suggest an association between aortic stiffness and aortic diameter dilation
in patients with abdominal aortic aneurysm80.
100
We used two methods to measure aortic stiffness - applanation tonometry121
and MRI129. Tonometry measures stiffness in the entire aorta, but MRI can also
measure segmental aortic stiffness. The results from both techniques revealed
that subjects with AAA have stiffer aorta than normal men of a similar age;
which was not unexpected, as our AAA patients are known to be vasculopaths,
a finding that is supported by the significantly greater CIMT in these subjects in
this study.
Carotid-femoral PWV analysis showed a generally stiffer whole aorta in patients
with AAA. Measurement of aortic PWV by CMR revealed that stiffness does not
vary along its length in patients with AAA, while in control subjects; the
abdominal segment was stiffer than that of the thoracic segments. Thoracic
segment was significantly stiffer in AAA patients than in control subjects,
although the stiffness of the abdominal segment was similar in both groups.
Aging is associated with increased stiffness in both the thoracic and abdominal
aorta in the general population83,130. The finding of similar abdominal PWV in
patients with AAA and controls is, at first sight, surprising since aneurysmal
disease is characterised by medial degeneration expected to increase intrinsic
stiffeness of the wall20. One explanation is that an increase in intrinsic stiffening
of the wall of the abdominal aorta may be offset by the inverse relation of PWV
to aortic diameter. According to the Moens-Korteweg equation, PWV = √Eh/D
where E = elastic modulus, a measure of intrinsic stiffness, h = wall thickness
and D = diameter. Thus, for a given elastic modulus and wall thickness, a larger
aortic diameter would result in a lower PWV. Differences in abdominal aortic
diameter between AAA patients and control subjects may thus mask differences
in intrinsic aortic elasticity. A recent study has also shown an association
101
between arterial stiffness measured by tonometry and ascending aortic
dilatation188.
The studies reporting segmental aortic stiffness in literature are done on
subjects with normal aortic diameter and our control group data is in keeping
with these studies. The studies, which report relationship of aortic stiffness or
PWV with AAA, have not either measured segmental aortic stiffness or stiffness
is measured after the AAA has been repaired86,133,189.
Stiffness increases with age and our AAA group was older than control group.
Similarly AAA subjects were vasculopaths, with the CIMT significantly thicker in
AAA subjects and all known risk factors for cardiovascular disease were
common in this group. Our demographic data showed that systolic hypertension
is more prevalent in the AAA group. We therefore used multivariate analysis to
adjust for these factors and found that cfPWV was independently, positively
correlated with AAA disease. Logistic regression showed that the risk of
developing AAA increases over 3.1-fold for every 1m/s increase in cfPWV. MAP
showed a significant association with AAA.
Studies have shown that hypertension is prevalent in AAA subjects. Pressure-
strain elastic modulus (Ep) as described earlier (Chapter-1) is also a measure of
arterial stiffness like PWV. In one study Ep was measured in AAA subjects and
age matched controls using ultrasound. They found AAA subjects had stiffer or
less elastic aortas than controls and there was no relationship between aortic
diameter and aortic stiffness20. It is difficult, however, to measure Ep in the AAA
wall as there is often thrombus present and imaging cannot distinguish between
this and the true wall. The presence of thrombus may therefore give rise to
102
erroneous Ep readings. Histological specimens have confirmed that aneurysmal
aortas are less elastic than age matched aortas190.
Assessment of a relationship between aortic stiffness and aneurysm growth rate
was analysed with the aid of a linear model as we had a linear trend in growth
of AAA in our cohort of patients. Our data suggests that the bigger an aneurysm
is at presentation the faster it grows, which is in keeping with a recently
published meta-analysis191. We also observed a trend that an increase in
cfPWV was associated with a decrease in AAA growth rate, but it was not
statistically significant. There was no association between segmental PWV and
AAA growth. Our analysis of the relationship between AAA growth and wall
stiffness was a retrospective one in which cfPWV was only measured at only 1
time point. A more precise analysis of the relationship between stiffness and
growth rate could only be done by measuring both parameters simultaneously.
We did not find any association between aortic stiffness and CIMT. The mean
CIMT in our AAA patients and control group is in keeping with other studies in
AAA subjects111 and healthy population over 60 years old112-113. A higher CIMT
quartile (Q4) is associated with increased thoracic aortic calcification115-116,118.
Division of our data set into quartiles, as occurs in most studies112,115 did not
show any relationship between stiffness (cfPWV) or aortic diameter with
increasing CIMT quartiles. Our study is however limited by the small number of
subjects, while most CIMT studies report on data obtained thousands of
patients119.
7.3 Aortic calcification
Calcification of the aorta, like aneurysm formation, is associated with ageing.
There is evidence that a calcified aneurysm has got a 3-fold increased risk of
103
rupture than a non-calcified aneurysm145. We found that calcification in the
entire aorta was significantly higher in AAA patients than control subjects. The
abdominal aorta was more calcified than the thoracic aorta in both groups. This
is in keeping with a large study measuring calcification in subjects >60 years in
multiple vascular beds192.
Our results showed a fair correlation between aortic stiffness and aortic
calcification in the entire aorta, as measured by CMR-PWV and CT-scan
respectively. Surprisingly this correlation disappeared in the abdominal aorta,
but remained in thoracic aorta on segmental comparison of PWV and calcium
score. A significant positive association between aortic stiffness and aortic wall
calcification in healthy subjects of a similar age to those in this study (average
age 65years) has previously been reported163. In that study the strength of this
relationship was poor in ascending aorta and moderate in both the descending
thoracic and abdominal aorta. Two other groups have found that abdominal
aortic calcification and aortic stiffness are positively correlated in patients with
chronic kidney disease164-165. There was no evidence of kidney dysfunction in
our aneurysm cohort (eGFR). Comparison of our results with other studies is
difficult as the methods chosen in some studies are subjective and segmental
division of the aorta also varies.
The abdominal aorta was the most calcified part of the aorta in both AAA and
healthy controls. We did not find any correlation between calcification and
stiffness in the abdominal segment. Our linear regression analysis did, however,
show that cfPWV and total aortic PWV (PWV-CMR) was significantly,
independently, positively associated with calcification in abdominal aorta, which
is in agreement with the work of others163. This observation supports the notion
104
that aortic calcification may contribute to aortic stiffness, but is not the only
factor causing stiffness21.
We found a significant inverse relationship between fetuin-A level and aortic
calcification measured on CT from the aortic root to the bifurcation. A similar
inverse relationship between plasma fetuin-A and aortic calcification has been
reported, but the calcified area of the abdominal aorta was measured only at the
level of L3 and L4 from x-ray computed tomography160. We did not find a
correlation between cfPWV and fetuin-A levels. These results should be
interpreted with caution, as the size of population studied was small (n=26). A
larger study (n=185) reported that increased levels of phosphorylated fetuin-A
containing calciprotein particles (CPP) were independently associated with
increased aortic PWV169. Our results also suggest that stiffening of abdominal
aorta is multi-factorial process that involves atherosclerosis, calcification, elastin
breakdown, medial thinning and accumulation of glycoproteins20.
7.4 AortaScan
This device had a potential to replace current AAA screening equipment and
make screening available to patients on a routine visit to their general
practitioner. We found that AortaScan is a portable and easy to use ultrasound
device requiring minimal training, but in its current form does not have adequate
sensitivity in assessing aneurysms. Further technical development is needed in
order for this device to be considered for use as a screening device for AAA in
place of conventional ultrasound.
105
7.5 Study limitations
Our study has a cross-sectional design and includes only subjects with small
aneurysms with a limited sample size, further longitudinal studies are required
to see how aortic stiffness changes with age and with increase in diameter of
aneurysm. The cross-sectional nature of our study precludes conclusions
regarding causality. Some MRI scans from PWV analysis were excluded as we
were getting bizarre flow graphs especially in AAA subjects. There is minimal
data available how pulse wave progresses in presence of aneurysm.
Stiffness increases with age and our AAA group was older than control group.
Similarly AAA subjects were vasculopaths and all known risk factors for
cardiovascular disease were common in this group, we cannot exclude the
possibility that they may have influenced the findings. Though, we used
multivariate analysis to adjust for these factors.
7.6 Further studies
A cross-sectional case-control study should be carried out to examine the utility
of CT and PWV (tonometry) in assessing progression of calcification/
stiffness/aortic dilation in relation to systolic hypertension and aneurysm
formation in a cohort of patients with AAA. This study would show whether there
is a relationship between the progression of aortic stiffness/calcification and
aneurysmal disease. The study will allow characterisation of disease patterns
and refinement of the imaging techniques, which could then be used in more
extensive investigations that include patients with small AAA detected by the
National Screening Program. These techniques will lead to a better
understanding of aneurysm pathophysiology and greater power to predict
aneurysm expansion and rupture.
106
This study includes only subjects with small AAA; it will be useful to know the
aortic stiffness in subjects with large AAA. Aortic stiffness was measured only
once during this study and it will be useful to see variation in PWV in a
longitudinal study.
The current guidelines from European Society of Vascular Surgery193 advise
prescription of antiplatelet agent and statins to all subjects with small AAA
because they are generally vasculopaths. It has been shown in some studies
that ‘statins’ reduce aortic stiffness by reduction in plaque volume194-195 and
similar research in AAA subjects may be useful. There is, however, conflicting
data on the effects of statin pharmacotherapy on AAA expansion196-197.
7.7 Summary
This is the first study to measure segmental biomechanical properties of the
aorta in patients with small AAA. We found that patients with AAA had
increased overall pulse wave velocity, measured by cfPWV and CMR. AAA
subjects had a stiffer thoracic aorta than control subjects. In subjects with a
normal sized aorta the abdominal segment was considerably stiffer than their
thoracic aortic segment. We speculate that the tendency for aneurysm
formation in the abdominal aorta may be a physiological response to the
increased stiffness that occurs in the thoracic aorta. However, it is equally
possible that degenerative dilatation may be an independent parallel process to
stiffness as a consequence of mechanical changes in aging aorta. A
longitudinal study is required to clarify this cause and effect relationship.
The techniques outlined in this thesis allow not only delineation of aneurysm
morphology but also assessment of physiological and metabolic activity. These
imaging modalities offer the possibility of more accurate risk assessment than
107
simply diagnosis and periodic size measurement, and their use could be
included, along with measurement of aneurysm size, during surveillance. More
evidence is, however, needed to know whether this will offer any survival benefit
and whether they would be cost-effective.
Further work is required to understand the multiple factors associated with
aneurysm formation, rupture and growth because some patients with small
aneurysms in screening programmes still face the threat of aneurysm rupture
and mortality.
108
CHAPTER 8
Appendix
8.1 Non-contrast CT protocol
Patient Radiation Dose Assessment
Aortic CT study – carotid bifurcation to femoral artery
Introduction
The study involves one CT of neck, chest, abdomen and pelvis without contrast
to look at the aorta. The scan extends from the carotid bifurcation to the femoral
artery. In this submission allowance for additional dose to the patient has been
made for patients with a Body Mass Index (BMI) of up to 35.
Data
CT scan parameters have been estimated as follows: 120kVp, 300mA, 0.75
second rotation, 225mAs per rotation on 40 x 0.625mm slice acquisition over
approximately 80cm with an equivalent pitch of 1.2. Estimated Dose Length
Product (DLP) per scan = 2094mGy.cm. The estimated approximate effective
dose will be 18mSv per scan under these factors.
109
Estimated Effective Dose
Patients will undergo one CT examination as part of the study. Doses are
shown in the table below. The stated doses are for a patient with BMI of 35. It is
expected that for patients with a BMI less than 35 the dose may be reduced.
Examination DLP per
Examination
(mGy.cm)
Dose per
Examination
(mSv)*
Number of
examinations
Total
Dose
(mSv)
Total DLP
(mGy.cm)
CT neck,
chest,
abdomen
and pelvis
2094 18 1 18 2094
The European reference levels for diagnostic CT in terms of DLP are as follows;
CT chest 650 mGycm, CT abdomen 780 mGycm, CT pelvis 570 mGy.
Conclusions
The Dose Length Product (DLP) dose constraint set for each CT examination at
2094mGy.cm is comparable to the sum of the European reference dose levels
for chest, abdomen and pelvis examinations. This dose constraint applies to
patients of BMI up to 35.
The total effective dose received by the study subjects of 18 mSv corresponds
to a total detriment (cancer incidence weighted for lethality and life impairment
and the probability over two succeeding generations of severe hereditary
disease) of 1.0 x 10-3 or approximately 1 in 1,000 (ICRP103). The risk factor
used here relates to the detriment for the whole population that contains a
range of ages.
110
The level of radiation dose given in this study (18mSv) corresponds to
approximately 8 years equivalent natural background radiation, where
background radiation is approximately 2.2mSv per year in the South East of the
UK.
David Gallacher
Consultant Physicist
Deputy Radiation Protection Adviser to Guy’s and St. Thomas’ NHS Foundation
Trust Medical Physics Department
26th January 2010
111
8.2 PET protocol
112
113
114
115
8.3 Biomarkers
Instructions to prepare platelet poor plasma collection prior to fetuin-A
measurements:
1.Collect blood in citrate tubes.
2.Spin on same day.
3.Store on ice until spun. (g force: 1500 ; Temp: 4oC; Time: 15 minutes
3,200 rpm)
4.Store at -80oC.
5.Transfer on ice.
Ultra-centrifuged for 40 mins at 100000g.
Ca measurements and fetuin sedimentation which might be indicative of mineral
complexes.
The best way is to make platelet poor plasma – so the blood samples need to
be taken with citrate. The plasma is then centrifuged once at about 2500 rpm
and then must be ultracentrifuged for 40 mins at 100000g. If this is not possible
then the samples will be contaminated with platelets. This may not be too much
of a problem as the microparticles can still be isolated by the centrifugations at
above but it may be harder to make measurements for factors that are not
related to platelets. We could however do Ca measurments and fetuin
sedimentation which might be indicative of mineral complexes.
116
8.4 Data
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
8.5 AortaScan images
Figure 8-1: Technology: V-mode measures ultrasonic reflections in multiple
planes to produce a 3D image183
147
8.6 Ethics Approval Document
148
149
150
8.7 Publications, presentations and published abstracts
Publications and presentations relate to this work include:
8.7.1 Publications
1. Abbas A, Smith A, Cecelja M, Waltham M. Assessment of the accuracy of
AortaScan for detection of abdominal aortic aneurysm (AAA).
European Journal of Vascular and Endovascular Surgery 2011
2. Abbas A, Attia R, Smith A, Waltham M. Can we predict abdominal aortic
aneurysm (AAA) progression and rupture by non-invasive imaging? -A
systematic review.
International Journal of Clinical Medicine, 2011; 2:484-499. 1
3. Peel S A, Hussain T, Cecelja M, Abbas A, Greil G F, Chowienczyk P,
Spector T, Smith A, Waltham M, Botnar R. Accelerated aortic imaging using
small field of view imaging and electrocardiogram-triggered quadruple
inversion recovery magnetization preparation.
Journal of Magnetic Resonance Imaging, 2011; 34(5): 1176–1183.
8.7.2 Submitted for publication
Abbas A, Cecelja M, Hussain T, Modarai B, Smith A, Chowienczyk P, Waltham
M. Regional changes in aortic stiffness measured by phase contrast magnetic
resonance imaging in the presence of small abdominal aortic aneurysm.
Hypertension
151
8.7.3 Presentations
INTERNATIONAL
1. Abdominal aortic aneurysm is associated with increased aortic stiffness
Abbas A, Cecelja M, Hussain MT, Greil G, Smith A, Chowienczyk P,
Waltham M. Presentation at the American Association for Thoracic
Surgery-Aortic Symposium 2012, Sheraton New York Hotel and Towers,
USA, 26-27 April, 2012
2. Assessment of the accuracy of AortaScan for detection of abdominal aortic
aneurysm (AAA) in a screening programmeAbbas A, Smith A, Cecelja M,
Waltham M. Oral presentation at the European Society of Cardiovascular
Surgery (ESCVS), 60th International Congress, Moscow, Russia. 20-22
May 2011
3. Zoom accelerated Quadruple Inversion Recovery imaging for fibrous cap
visualization in the abdominal aortic aneurysmHussain MT, Abbas A,
Waltham M, Greil G, Botnar R . Poster presentation at the International
Society for Magnetic Resonance in Medicine Annual Meeting (ISMRM),
Montréal, Québec, Canada. 7-13 May 2011
4. Segmental aortic stiffness measured by MRI and its effect on
cardiovascular risk assessment in patients with abdominal aortic aneurysm
(AAA). Abbas A, Cecelja M, Hussain MT, Greil G, Smith A, Chowienczyk
P, Waltham M. Oral presentation at the International Conference on
Prehypertension and Cardio Metabolic Syndrome (PreHT), Vienna, Austria.
24-27 February 2011.
5. Accelerated aortic plaque imaging using small field of view imaging and
quadruple inversion recovery magnetization preparation. Peel S, Hussain
MT, Cecelja M, Abbas A, Greil G, Botnar R. Poster presentation at the
Society of Cardiac Magnetic Resonance Annual Meeting, Nice, France. 3-
6 February 2011.
152
NATIONAL
1. Regional changes in aortic stiffness measured by phase contrast magnetic
resonance imaging in the presence of small abdominal aortic aneurysm.
Abbas A, Cecelja M, Hussain MT, Greil G, Smith A, Chowienczyk P,
Waltham M. Oral presentation at the Vascular Society of Great Britain and
Ireland. Manchester, 28-30 November 2012
2. Aortic stiffness as a predictor of abdominal aortic aneurysm (AAA)
formation. Abbas A, Cecelja M, Hussain MT, Greil G, Smith A,
Chowienczyk P, Waltham M. Oral presentation at the Society of Academic
and Research Surgery (SARS), Nottingham, 4-5 January 2012
3. Segmental aortic stiffness measured by MRI in patients with abdominal
aortic aneurysm (AAA). Abbas A, Cecelja M, Hussain MT, Greil G, Smith
A, Chowienczyk P, Waltham M. Oral presentation session at the
Association of Surgeons in Training (ASiT-medal session), Sheffield, 15-17
April 2011.
4. Assessment of the accuracy of AortaScan for detection of abdominal aortic
aneurysm (AAA) in a screening programme. Abbas A, Smith A, Cecelja M,
Waltham M. Poster presentation at the Association of Surgeons in Training
(ASiT), Sheffield, 15-17 April 2011
5. Segmental aortic stiffness measured by MRI in patients with abdominal
aortic aneurysm (AAA). Abbas A, Cecelja M, Hussain MT, Greil G, Smith
A, Chowienczyk P, Waltham M. Oral presentation at the Abdominal Aortic
Aneurysm Research Meeting (NHS Abdominal Aortic Aneurysm Screening
Programme) Gloucester. 16 February 2011
6. Assessment of the accuracy of AortaScan for detection of abdominal aortic
aneurysm (AAA) in a screening programme. Abbas A, Smith A, Cecelja M,
Waltham M. Oral presentation at the Abdominal Aortic Aneurysm Research
Meeting (NHS Abdominal Aortic Aneurysm Screening Programme)
Gloucester. 16 February 2011
153
8.7.4 Published abstracts
1. Abbas A, Cecelja M, Hussain MT, Greil G, Smith A, Chowienczyk P,
Waltham M. Aortic stiffness as a predictor of abdominal aortic aneurysm
(AAA) formation. British Journal of Surgery, 2012. 99 (S4), 17.
2. Premaratne S, Abbas A, Ahmad A, Saha P, Patel A, Modarai, Smith A,
Waltham M. A role for statins in promoting venous thrombus resolution.
British Journal of Surgery, 2012. 99 (S4), 15.
3. Abbas A, Smith A, Cecelja M, Waltham M. Assessment of the accuracy of
AortaScan for detection of abdominal aortic aneurysm (AAA) in a screening
programme.
Interact Cardio Vasc Thorac Surg 2011; 12: S5.
4. Abbas A, Cecelja M, Hussain MT, Greil G, Smith A, Chowienczyk P,
Waltham M. Segmental aortic stiffness measured by MRI in patients with
abdominal aortic aneurysm (AAA). (ASiT-medal session), International
Journal of Surgery, 2011. 9 (7), 496. doi:10.1016/j.ijsu.2011.07.008
5. Abbas A, Smith A, Cecelja M, Waltham M. Assessment of the accuracy of
AortaScan for detection of abdominal aortic aneurysm (AAA). International
Journal of Surgery 2011. 9 (7), 541. doi:10.1016/j.ijsu.2011.07.228
6. Peel S A, Hussain MT, Cecelja M, Abbas A, Greil G F, Botnar R.
Accelerated aortic plaque imaging using small field of view imaging and
quadruple inversion recovery magnetization preparation. Journal of
Cardiovascular Magnetic Resonance 2011, 13(Suppl 1): P368. doi:
10.1186/1532-429X-13-S1-P368
7. Abbas A, Ehsan , Jameel , daSilva A. Long term Outcome of Patients
Presenting to a Claudication Clinic with Leg Pain. Abst Interactive Cardio
Vascular and Thoracic Surgery April 15, 2009, 8 (Suppl.1) S112-S113
154
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