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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

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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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