Combining Multiple Complex Modelling Components to Create a Decision Support System for Heart Valve Disease
Conditions: Heart Valve Diseases
Sponsor: German Heart Institute

 
 
 
 
 
 
European Commission 
Directorate General for Communications Networks, Content and Technology 
Sustainable and Secure Society - Health and Well-being 
 
H2020 PHC-30-2015 689617 
Research and Innovation Action 
 
 
 
 
 
 
 
Work Package: WP4  
Digital Patient 
Deliverable: D4.7 
Comprehensive Data Summary and Review 
Version: 1v6 
Date: 28-Feb-19 
 
 
 

 
H2020 PHC-30-2015 689617: EurValve 
WP4 : Digital Patient 
D4.7: Comprehensive Data Summary and Review 
Version: 1v6, Date: 28-Feb-19 
 
 
 
 
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DOCUMENT INFORMATION 
IST Project Num 
H2020 PHC-30-2015 689617 
Acronym 
EurValve 
Full title 
Personalised Decision Support for Heart Valve Disease 
Project URL 
http://www.eurvalve.eu 
EU Project officer 
Milan Popovic (Milan.Popovic@ec.europa.eu) 
 
Work package 
Number 4 4 
Title 
Digital Patient 
Deliverable 
Number 4.7 
Title 
Comprehensive Data Summary and Review 
 
Date of delivery 
Contractual 
28 Feb-19 
Actual 
28-Feb-19 
Status 
Version 1v6 
Final  
Nature 
Prototype      Report      Dissemination      Other  
Dissemination Level  Public (PU)  
Restricted to other Programme Participants (PP)  
Consortium (CO)  
Restricted to specified group (RE)  
 
Authors (Partner) 
M Kelm, T Khune 
Responsible Author  M Kelm 
Email 
mkelm@dhzb.de 
Partner 
DHZB 
Phone 
00 49 30 45 93 20 78 
 
Abstract (for 
dissemination) 
The development and testing of the EurValve Decision Support System included its 
application to patient data collected within a prospective clinical study. The data includes 
clinical information from each patient, and population and epidemiological data that are 
used both for data inference and for data and model interpretation. All data is appropriately 
annotated, organised and represented using the infrastructure developed in WP2. This 
document summarises the data available at PM30. 
Keywords 
Decision Support, Clinical Trial, Aortic, Mitral, Valvular 
The information in this document is provided as is and no guarantee or warranty is given that the information is fit for any 
particular purpose. The user thereof uses the information at its sole risk and liability. Its owner is not liable for damages 
resulting from the use of erroneous or incomplete confidential information. 
Version Log 
Issue Date 
Version 
Author 
Change 
19-Jul-18 
1v1 
M Kelm 
Initial Draft 
24-Jul-18 
1v2 
K El-Arifi 
Minor changes, formatting 
30-Jul-18 
1v3 
PMO 
Release version 
17-Jan 19 
1v4 
M Kelm 
Draft for Resubmission with Updates 
25 Feb 19 
1v5 
K El-Arifi 
Updated 
28 Feb 19 
1v6 
K El-Arifi 
Additions 
 
 

 
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CONTENTS 
Executive Summary............................................................................................ 4 
1 
Introduction ................................................................................................ 5 
2 
Delivered Prospective Clinical Recruitment ................................................. 7 
3 
Baseline Characteristics ............................................................................... 9 
4 
Clinical Outcome ....................................................................................... 13 
5 
Quality of Life ............................................................................................ 14 
6 
6 Minute walk distance ............................................................................. 18 
7 
Augmented Data ....................................................................................... 19 
8 
Data Correlations ...................................................................................... 22 
9 
Biopsies ..................................................................................................... 30 
10 Mobile Health Trackers ............................................................................. 31 
11 Gender Aspects of Cardiovascular Disease ................................................ 32 
Definitions ....................................................................................................... 33 
Appendix A: EurValve Data Summary .............................................................. 34 
 
LIST OF FIGURES 
Figure 1: Prospective Clinical Study Design ............................................................................................ 5 
Figure 2: Study Diagram .......................................................................................................................... 6 
Figure 3 Recruitment at all 3 clinical sites .............................................................................................. 7 
Figure 4 Treatment undertaken .............................................................................................................. 8 
Figure 5 Geometric and Functional Myocardial Parameters of the Study Cohort ............................... 12 
Figure 6 Kaplan-Meier survival curve ................................................................................................... 13 
Figure 7: WHOQOL-BREF Quality of Life Domains in EurValve patients. .............................................. 15 
Figure 8: Quality of life changes after treatment. ................................................................................ 15 
Figure 9: Guideline-based Standard Imaging Parameters and QoL Scores .......................................... 16 
Figure 10: Patient Subjective/Objective Data Correlation .................................................................... 17 
Figure 11 6 Minute walk test before and after treatment [distance in m] .......................................... 18 
Figure 12: Image-based analysis results ............................................................................................... 25 
Figure 13: End diastolic volumes and end systolic volumes ................................................................. 25 
Figure 14: Correlation between measures of disease and exercise capacity ....................................... 28 
Figure 15: Correlation between computed change in cardiac work .................................................... 29 
 
LIST OF TABLES 
Table 1: Baseline Characteristics of the Study Cohort .......................................................................... 11 
Table 2 List of model inputs and outputs ............................................................................................. 20 
Table 3 Case processing statistics ......................................................................................................... 21 
 
 
 

 
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EXECUTIVE SUMMARY 
The EurValve project’s aim is to implement and test, in a relevant clinical target cohort, a 
Decision Support System (DSS) for aortic and mitral valve replacement and repair. The testing 
process will include the application of the DSS to patient data collected within a prospective 
clinical study, and WP4 is responsible for the collection of the data on which this element of 
the testing depends.  
The data include clinical information from each patient, and population and epidemiological 
information that is used both for data inference and for data and model interpretation. All 
data is appropriately annotated, organised and represented using the infrastructure 
developed in WP2. This document gives a comprehensive summary of the data status at 
project end. 
This version of the Deliverable has been significantly expanded, to include a description and 
discussion of the project’s Augmented Data - that which is generated by the EurValve 
processes of simulation. In an extension of this, there is a major section addressing the issues 
of validation, a topic raised by many of the clinicians involved in the randomised controlled 
experiment. The thought processes that have driven the EurValve approach to validation of 
the computational measures are laid out, and the complex issues of association between 
measures of severity of disease and the acute and long-term effects of intervention are 
explored. 
 
 

 
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1 INTRODUCTION 
In the EurValve project, we aim to implement and test a DSS for aortic and mitral valve 
replacement and repair in a relevant clinical target cohort. For this clinical component, testing 
is being conducted within a prospective clinical study, the data for which is discussed in this 
document, and the design of which is shown in figure 1. For completeness, a summary of all 
EurValve data is given in Appendix A. 
WP4 collects the data on which this aspect of the project depends. These data include clinical 
information from each patient, and population and epidemiological data that are used both 
for data inference and for data and model interpretation. All data will be appropriately 
annotated, organised and represented using the infrastructure developed in WP2. 
Not all of the assessments listed can be performed on every patient, but sufficient data 
available needs to be ensured to support the analysis processes. In brief: A total of 120 
patients were intended to be enrolled, in two subgroups:  
• Group 1: patients with aortic valve disease (N=60; per clinical centre N=20) 
• Group 2: patients with mitral valve disease (N=60; per clinical centre N=20). 
According to current hospital control data, we anticipated that approximately 30% of the 
patient to have combined aortic-mitral valve disease. 
 
Figure 1: Prospective Clinical Study Design 
Aortic valve disease
Aortic valve stenosis /  insufficiency
N= 60
Mitral valve insufficiency
N= 60
Before treatment
Follow-up
Enrollment
Treatment
Valve replacement/ 
repair according to
current clinical
guidelines
Step 1 – Validation
Comparison of modelled (predicted) vs measured 
outcomes
Step 2 – DSS integration
Comparison of virtual decision making vs. current 
clinical decision making
Clinical routine data: 
ECG, Lab, Anthropometrics, 
Medication
Functional cardiac assessment: 
Echocardiography
MRI
Functional vascular profile:
MRI
Valve segmentation: 
Echocardiography / CT
Clinical routine data: 
ECG, Lab, Anthropometrics, 
Medical history, Medication
Functional cardiac assessment: 
Echocardiography
MRI
Functional vascular profile: 
MRI
Valve segmentation: 
Echocardiography / CT
Proteomic
information:
Biopsy taken
during procedure

 
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At each of the three clinical sites (STHFT, CATH, DHZB) the following visits were planned: 
 Visit 1: All patients will be investigated before valve intervention by imaging, ECG, 
laboratory tests, anthropometrics (blood pressure, body weight, clinical status etc.). 
These data will be used for modelling. 
 Operation (valve replacement/repair). In EurValve all patients were treated according to 
current clinical guidelines. In the Berlin centre an additional myocardial biopsy was 
planned in patients undergoing surgery, with the biomaterial being used for proteomic 
analysis. 
 Visit 2: After treatment patients were followed-up undergoing the study protocol again. 
This allows comparing the modelled (predicted) against measured outcome data needed 
for the validation of the model and the DSS. In this second step a comparison between 
virtual decision making using a DSS and current clinical decision making will be carried out. 
Within EurValve two main model-based outcomes were validated: (I) model-based output 
before an intervention is performed and (II) model-based output after a virtual intervention 
is performed. Wherever available the diseased valve state was tuned according to the patient-
specific 3D geometry (from CT or TEE) of the valve. In all remaining cases the 0D model was 
tuned using clinical imaging information obtained from routine diagnostics. We have sketched 
this out in the study diagram below. 
 
 
Figure 2: Study Diagram 
This deliverable describes the patient data pool that was used as a reference for the model 
tuning and all subsequent clinical validation steps in the 0D model. 

 
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2 DELIVERED PROSPECTIVE CLINICAL RECRUITMENT 
From an original target of 120 patients, a total of N=169 patients have been enrolled across 
all three clinical sites where follow-ups have been performed in N=164 patients, resulting in 
a total of N=333 individual disease states, and thus a slight over-performance in recruitment 
at all three clinical sites. This is a natural consequence of the slight over-recruitment 
employed to ensure adequate numbers in the face of probable later-stage dropouts.  
Recruitment at all 3 clinical centres is shown in the figure below. 
 
Figure 3 Recruitment at all 3 clinical sites 
 
Completed  preoperative  and postoperative datasets are to date available in n=164 cases. Of 
those, n=98 had primary treatment indication for aortic valve disease cases and n=66 patients 
had primary treatment indication for mitral valve disease. All patients with treatment 
indication received surgical or interventional (TAVI) treatment procedures accordingly. 
 
 

 
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Figure 4 Treatment undertaken 
Milder forms of valve disease and resulting overlap between both disease was found in N=24 
patients (Table 1). In n=17 AVD patients there was also relevant mitral valve insufficiency, a 
common clinical combination. 
 
 
MVRepair 
MVReplace 
TAVR 
SAVR 
Frequency 

 
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3 BASELINE CHARACTERISTICS 
The baseline characteristics are illustrated in Table 1: 
Parameter 
N  
Value 
Male Gender 
98/169 
57 % 
Age 
169 
70.6±12.6 years 
Body weight 
169 
78.3±15.5 kg 
Body Mass Index (BMI) 
169 
27.1±4.4 kg/m2 
 
 
 
 
MITRAL VALVE DISEASE 
91 
54% 
MI 
89 
98% 
Mixed 
1 
2% 
 
 
 
Aetiology 
91/169 
 
degenerative/calcified 
 
10% 
Functional 
 
10% 
Infective 
 
1% 
Ischemic 
 
3% 
Prolapse 
 
75% 
Rheumatic 
 
1% 
 
 
 
 
AORTIC VALVE DISEASE 
102/169 
60% 
AI 
4/102 
4% 
AS 
84/102 
82% 
Mixed 
14/102 
14% 
Max pressure drop 
 
68.1 ±30.46 mmHg 
Mean pressure drop 
 
42.7 ±19.88 mmHg 
 
 
 
Aetiology 
82 
 
Calcifications 
 
94% 
Bicuspid valve 
 
3% 
Other 
 
3% 
 
 
 
Thoracic aortic aneurysm 
5 
5% 
Abdominal aortic aneurysm 
2 
2% 
 
 
 
Symptoms 
 
 
Palpitations 
46/167 
28% 
Edema 
41/167 
25% 
Syncope 
11/163 
7% 

 
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Parameter 
N  
Value 
Nycturia 
37/163 
23% 
NYHA I 
28/163 
17% 
NYHA II 
69/163 
42% 
NYHA III 
56/163 
34% 
NYHA IV 
10/163 
6% 
CCS 
 
 
No CCS symptoms 
106/168 
63% 
Class 1 
40/168 
24% 
Class 2 
14/168 
8% 
Class 3 
5/168 
3% 
Class 4 
4/168 
2% 
 
 
 
PHYSICAL ACTIVITY 
 
 
None 
37/167 
22% 
Sporadically 
48/167 
29% 
Regular 
68/167 
41% 
Frequently 
14/167 
8% 
 
 
 
OVERALL PHYSICAL WELLBEING 
 
 
very good 
10/155 
6% 
Good 
37/155 
24% 
Fair 
56/155 
36% 
Poor 
40/155 
26% 
very poor 
12/155 
8% 
 
 
 
OPERATIVE RISK 
 
 
Arterial hypertension 
118/168 
72% 
Previous pulmonary artery hypertension 
8/163 
5% 
Frailty 
4/168 
2% 
Urgency 
13/168 
8% 
 
 
 
Smoking 
 
 
Smokers 
18/167 
11% 
Previous smokers 
80/167 
48% 
Non-Smokers 
69/167 
41% 
 
 
 
Alcohol consumption 
 
 
History of alcohol abuse 
3/164 
2% 
Regular 
30/164 
18% 
Mild 
40/164 
24% 
Rare 
35/164 
21% 
None 
53/164 
32% 

 
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Parameter 
N  
Value 
 
 
 
Previous coronary artery bypass surgery 
10/168 
6% 
Previous aortic valve surgery 
2/168 
1% 
Previous Stroke 
17/166 
10% 
Previous episode of heart failure 
21/159 
13% 
Existing permanent atrial fibrillation 
33/168 
20% 
Existing intermittent atrial fibrillation 
19/166 
11% 
Chronic rheumatic disease 
12/162 
7% 
Chronic renal impairment 
35/167 
21% 
Existing chronic liver disease 
6/160 
4% 
Existing malignant disease 
20/166 
12% 
Existing dementia 
0/164 
0% 
 
 
 
Physiological parameters 
 
 
Heart rate 
 
70.23±11.4 /min 
Systolic blood pressure 
 
138±21.6 mmHg 
Diastolic blood pressure 
 
74±12.1 mmHg 
Mean blood pressure 
 
95±13.4 mmHg 
Table 1: Baseline Characteristics of the Study Cohort 
 
 

 
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Geometric and resulting parameters within the cohort are shown in Figure 5. 
 
 
Figure 5 Geometric and Functional Myocardial Parameters of the Study Cohort 
 
 

 
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4 CLINICAL OUTCOME 
The following figure shows a Kaplan-Meier survival curve of study participants during the 
course of the clinical study. N=7 of 164 died during the course of the study, accounting for an 
overall mortality of 4%. Considering included treatment groups with elderly patients with 
existing co-morbidities and TAVI patients the mortality was rather low. While the follow-up 
assessment was performed at 3-8 months after treatment, 2 out of 7 events were reported  
between 8 and 20 months. As patients were not included at the same time, the time frame 
between 8 and 20 months was not fully assessed within the entire study cohort. The mortality 
upon standardized follow between 3-8 months up was 5 out of 164 (3%), which is in line with 
existing literature date.  
 
Figure 6 Kaplan-Meier survival curve 
7 out of 164 (4%) died during the course of the study. Upon standardised follow up between 
3-8 months was 5 out of 164 (3%) 
 
The overall low mortality illustrates the already low rate of lethal complications during 
cardiothoracic surgery. Nevertheless, individual mortality varies and will depend on distinct 
risk factors that can be assessed using standardized scoring system (EUROSCORE I, 
EUROSCORE II, Society of Thoracic Surgery - STS). 
 
 

 
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5 QUALITY OF LIFE 
Additionally to standardised cardiologic clinical data, quality of life has been assessed using 
the World Health Organisation (WHO) WHOQOL-BREF questionnaire. Responses can be 
grouped into four major life quality domains: 
 
 
 
 
All four health domains are illustrated in Figure 7a and 7b for patients before and after 
surgery. No significant QoL differences were found before and after patients undergoing 
surgical treatment, which presents an opportunity for optimising treatment and post-
treatment programs in the future. 
 
 
Physical Health 
o Energy and fatigue 
o Pain and discomfort 
o Sleep and rest 
Psychological 
o Bodily image and appearance 
o Negative feelings 
o Positive feelings 
o Self-esteem 
o Thinking, learning, memory and concentration 
Social Relations 
o Personal relationships 
o Social support 
o Sexual activity 
Environment 
o Financial resources 
o Freedom, physical safety and security 
o Health and social care: accessibility and quality 
o Home environment 
o Opportunities for acquiring new information and skills 
o Participation in and opportunities for recreation/leisure 
o Physical environment (pollution/noise/traffic/climate) 
o Transport 

 
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a) Pre-operative 
b) Postoperative 
Figure 7: WHOQOL-BREF Quality of Life Domains in EurValve patients.  
The scale ranges from 0 (lowest possible quality within the domain) to 100 (best possible quality within the domain) 
 
The changes in quality of life scores are illustrated in the following figure. Despite slight, yet 
statistically insignificant, changes within the physical quality of life domain, overall scores 
remained almost unchanged within the entire cohort.  
 
 
Figure 8: Quality of life changes after treatment. 
 
 

 
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Unchanged quality of life cores are based on the entire study cohort cannot exclude 
improvement or worsening for individual patients. 
While QoL domains are correlated to each other, no direct association between guideline 
based standard imaging parameters (left ventricular ejection fraction, mean aortic valve 
pressure gradient, mitral valve regurgitation) were found, as illustrated in Figure 9. 
 
 
Figure 9: Guideline-based Standard Imaging Parameters and QoL Scores 
 
 

 
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The physical and environmental quality of life domain was associated to the absolute distance 
and patient-specific 6-Minute walk test – the longer the 6 Minute Walk test difference, the 
higher they rated environmental quality of life (Figure 10). 
 
 
Figure 10: Patient Subjective/Objective Data Correlation 
Correlation between environmental quality of life and 6-Minute Walk test distance in meters 
 
 

 
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6 6 MINUTE WALK DISTANCE 
The results of the 6 minute walk distance test are shown in the figure below: 
 
Figure 11 6 Minute walk test before and after treatment [distance in m] 
 
 

 
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7 AUGMENTED DATA 
One of the primary aims of EurValve was to augment the measured clinical data with further 
parameters extracted from the operation of a computational model of the systems 
physiology. There are several aspects of these data that could provide valuable diagnostic or 
prognostic insight. 
 Assuming that the model effectively represents the physiological processes, it embeds 
knowledge. For example, a variable elastance model has been shown to produce a 
simple but effective representation of ventricular contraction, and the concepts of 
maximal and minimal elastance are directly interpretable. EurValve has developed a 
process for personalisation of some of the model parameters, including elastance 
parameters, based on measured clinical data. These personalised parameters are 
returned as augmented data in the pseudonymised patient record. 
 Subject to the underpinning assumptions, the models are able to compute 
parameters, such as valve pressure-flow characteristics from the image data or left 
ventricular peak power or energy expenditure in a specific exercise state, that are not 
easily measured in the clinic. These parameters contribute to the augmented data. 
 Prospective interventions can be carried out in the model to predict the likely 
physiological benefit. For example the expected changes in the PV loop, or the 
expected reduction in left ventricular work associated with a valve replacement, can 
be computed. These also constitute augmented data. 
Table 2 lists the augmented data associated with the system model, separated into two 
columns. The first column is the set of model input parameters, which includes several 
parameters that are personalised based on measured clinical data as well as the valve 
characterisation from the image data, and the second column is the set of output parameters. 
 
Model inputs 
Model outputs 
Heart Rate [bpm] 
Cardiac Output (lpm) 
Elvmin [mmHg/ml] 
LV End Diastolic Volume [ml] 
Elvmax [mmHg/ml] 
LV End Systolic Volume [ml] 
Systemic Arterial Resistance [mmHg.s/ml] 
Stroke Volume [ml] 
Systemic Arterial Capacitance [ml/mmHg] 
Ejection Fraction [-] 
Mean circulatory filling pressure [mmHg] 
Maximum LV Pressure [mmHg] 
Start of left ventricular contraction [-] 
Minimum LV Pressure [mmHg] 
Peak of left ventricular contraction [-] 
LV End Diastolic Pressure [mmHg] 
End of left ventricular contraction [-] 
Peak LVdp/dt 
Volume offset in LV pressure equation [ml] 
LV Stroke Work per Beat [mmHg.ml] 
Elamin [mmHg/ml] 
LV Stroke Power Expenditure [W] 
Elamax[mmHg/ml] 
LV Peak Power [W] 
Start of left atrial contraction [-] 
Whalley Wasted Power [W] 

 
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Model inputs 
Model outputs 
Peak of left atrial contraction [-] 
Systolic Blood Pressure [mmHg] 
End of left atrial contraction [-] 
Diastolic Blood Pressure [mmHg] 
Volume offset in LA pressure equation [ml] 
Mean Arterial Pressure (True) [mmHg] 
Aortic Valve quadratic coefficient 
[mmHg.s^2/ml^2] 
Mean Arterial Pressure (nominal) [mmHg] 
Aortic Valve linear coefficient [mmHg.s/ml] 
LV End Diastolic Pressure [mmHg] 
Aortic Valve regurgitant quadratic coefficient 
[mmHg.s^2/ml^2] 
Forward Flow through Aortic Valve [ml/beat] 
Aortic Valve regurgitant linear coefficient 
[mmHg.s/ml] 
Forward Flow through Mitral Valve [ml/beat] 
Aortic Valve smoothing threshold [mmHg] 
Maximum Pressure Drop over Aortic Valve 
[mmHg] 
Aortic Valve smoothing polynomial order 
Mean Pressure Drop over Aortic Valve 
[mmHg] 
Mitral Valve quadratic coefficient 
[mmHg.s^2/ml^2] 
Stroke Power Lost to Aortic valve resistance 
Mitral Valve linear coefficient [mmHg.s/ml] 
Ratio of Stroke Power Lost to AV resistance: 
Stroke Power 
Mitral Valve regurgitant quadratic coefficient 
[mmHg.s^2/ml^2] 
Mitral Valve Regurgitation [ml/beat] 
Mitral Valve regurgitant linear coefficient 
[mmHg.s/ml] 
Mitral Valve Regurgitant Fraction [-] 
Mitral Valve smoothing threshold [mmHg] 
Stroke Power Lost to Regurgitant MV 
Mitral Valve smoothing polynomial order 
Ratio of Stroke Power Lost to Regurgitant MV: 
Stroke Power 
Venous/Pulmonary Capacitance [ml/mmHg] 
Aortic Valve Regurgitation [ml/beat] 
Systemic Arterial Capacitance Unstressed 
Volume  [ml] 
Aortic Valve Regurgitant Fraction [-] 
Venous/Pulmonary Capacitance Unstressed 
Volume  [ml] 
Stroke Power Lost to Regurgitant AV 
Rest Heart Rate from Philips Watch Data [bpm] 
Ratio of Stroke Power Lost to Regurgitant AV: 
Stroke Power 
Exercise Heart Rate from Philips Watch Data 
[bpm] 
  
Table 2 List of model inputs and outputs 
WP3 provided tools, operable through the MEE developed by WP2, to segment the valve 
images, to compute the pressure-flow characteristic of the valves, to personalise a 0D model 
of the systemic circulation and to quantify the uncertainty of the generated results. Further 
information on the model assumptions and operational details are provided in D3.4, and a 
summary of the final computational analysis protocol in D1.3. In summary, the Computational 
Fluid Dynamics model, or the Reduced Order Model, returns valve pressure-flow 
characteristics. The final systems model protocol is a seven stage analysis. Firstly model 
parameters are personalised using clinical observations at the time of examination, and then 

 
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the model extrapolates back to the rest state and forward to an exercise state. These three 
stages provide the augmented data to characterise the pre-intervention state, followed by 
four stages that predict the post-intervention data under a candidate intervention. 
Two primary variations of the workflow were developed. The first uses the clinical image data 
to characterise the pressure/flow relationship of the valve. It was not possible to collect image 
data of adequate quality to perform accurate valve segmentation for approximately one half 
of the EurValve clinical cohort, and so in the second variation the measured pressure gradient 
(aortic valve) or measured regurgitant fraction (mitral valve) was used for diseased valve 
characterisation. For this second approach this means that these measures cannot be used 
for model validation, but the physiology characterisation and the prediction of the changes 
under intervention can still be performed. In fact the addition of this second workflow also 
increases the potential for post-project application to a wider spectrum of clinical cases.  
The workflow, in one or both variations, was operated by WP6 on all cases.  
Centre 
Number of  
Unique cases 
Cases processed with 
image-based workflow 
Cases processed with 
measurement-based 
workflow 
 
Mitral 
Aortic 
Mitral 
Aortic 
Mitral 
Aortic 
Berlin 
46 
36 
23 
4 
46 (3) 
36 
Eindhoven 
0 
39 
0 
0 
0 
39 
Sheffield 
20 
22 
19 (4) 
20 (1) 
17 (1) 
22 (1) 
Total 
66 
97 
42 (4) 
24 (1) 
63 (4) 
97 (1) 
Table 3 Case processing statistics 
Figures in red indicate number of cases for which the analysis process failed 
Table 3 shows the statistics of the case processing. The main entries in the table are the 
number of cases that were subjected to processing. The figures in red indicate the number of 
these cases for which the analysis protocol failed to produce the augmented data, usually 
because of a failure of the model personalisation step. Note that the process for the aortic 
valve is generally very robust, with only one case from 97 analysed failed to reach 
convergence. The process for mitral cases is slightly less stable, perhaps reflecting the 
complexity of the disease and/or the uncertainty of clinical measurements, but nevertheless 
only 1 mitral case failed to converge using one or other of the two work flows. Model-
augmented data has been generated for 96 aortic cases and for 65 mitral cases. 
 
 

 
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8 DATA CORRELATIONS 
The primary output of EurValve is a Decision Support System (DSS) that seeks to improve 
diagnosis and interventional planning for heart valve disease. One of the aims is to present to 
the clinician augmented data, derived by the operation of a computational model, that 
provides additional information to aid the process of decision-making.  Deliverable D6.3 
focuses on the clinical evaluation of the usability and potential utility of the DSS. This 
evaluation is positive but, as should be expected, a consistent comment of the clinical review 
cohort was that extensive validation is required before clinical adoption. The purpose of this 
section is to explain the thought processes that have driven the EurValve approach, to 
validate the computational measures, and to explore the complex issues of association 
between measures of severity of disease and the acute and long-term effects of intervention. 
EurValve is predicated on a series of assumptions and hypotheses, the key elements of which 
are summarised below. 
 Important aspects of the systems physiology of an individual can be represented by a 
simple, zero dimensional, model that describes the distribution of pressure and flow, and 
from which key parameters such as left ventricular work and peak power expenditure 
can be determined.  
 A relevant systems physiology model of an individual with valve disease must include a 
characterisation of the pressure-flow relationship of the diseased valve.   
o Hypothesis 1: The pressure-flow relationship for the diseased valve can be estimated 
by computational fluid dynamics (CFD) analysis based on the valve anatomy in the 
appropriate configuration (open for a stenotic aortic valve, closed for a regurgitant 
mitral valve). A sub-hypothesis is that this relationship can be approximated by a 
Reduced-Order Model (ROM) constructed from the operation of many CFD models. 
These are essentially physics/engineering focused issues, but the first validation of the 
primary hypothesis is performed against the clinical measures in the EurValve cohort 
and is presented in this section. The sub-hypothesis is simply a direct comparison of 
analytical approaches, and so this is covered separately in WP3.4. 
 Often clinical decisions are made based on sparse data collected in the course of a series 
of clinical examinations performed whilst the patient is at rest, which might be in no way 
representative of the conditions under which the effects of the disease are most 
manifest. If a model could represent the physiological excursions of an individual as they 
go about their daily lives it might provide important diagnostic information. 
 The model is able to represent a proposed intervention and to compute the estimated 
effects of the intervention on the systems physiology. 
o Hypothesis 2: A primary effect of the valve disease is to increase the work that the 
heart does to generate the flow required to meet the metabolic demand. One of the 
clinical outcomes of valve disease is progression towards heart failure as the 

 
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ventricle works harder, and if this work and associated peak power could be 
quantified reliably this might have immediate diagnostic value. Changes of these 
quantities after intervention might provide direct and quantitative measures of the 
physiological benefit of the intervention and be associated with the capacity for 
positive remodelling of the ventricle and recovery of the patient.    
This hypothesis is explored in the following pages. It is important to recognise that there 
are two phases of response to the intervention. Theoretically the immediate and acute 
response to a valve intervention will (almost) always be positive because the work that 
the heart is wasting, either to drive flow through a diseased and restricted aortic valve 
or to push flow back into the atrium through a leaky mitral valve, will be reduced. 
EurValve recognises the other system characteristics, including the status of the systemic 
circulation and the elastance and contractility of the ventricle, but essentially it would 
be expected that the more severe the disease the greater the benefit of intervention. In 
fact both acute and longer-term benefits are complex. Acutely, intervention on either 
valve tends to increase systemic arterial pressures, and these are immediately regulated 
either by the patient’s own homeostatic mechanisms or by pharmacological 
intervention. This response is recognised in the EurValve analysis protocol (see the WP3 
section in D1.3). The positive acute response is also masked, over a recovery period, by 
the inevitable insult of, or complications associated with, the intervention.  
The longer term benefit has two aspects, the direct benefit because the heart does less 
work to meet any particular metabolic demand even if the cardiac properties are 
unchanged and the potential for positive ventricular remodelling and improvement of 
cardiac performance. The latter is particularly complex. For this it would not be expected 
that there would be a proportional relationship between severity of disease, improved 
valve performance associated with intervention and remodelling outcome. Rather it 
might be expected that the benefit in terms of remodelling will be relatively low if the 
valve disease is a minor contributor to cardiac work or power, but also low if the failing 
heart (perhaps due to the valve disease) has progressed to the point at which it no longer 
has the biological capacity to recover. It might be expected that there is a ‘sweet spot’ in 
which there is significant immediate benefit and also long-term remodelling benefit. 
These issues are expanded below. 
The primary focus of EurValve is the development of the DSS and the exploitation of the 
system model to provide the augmented data and the predictions of physiological outcomes 
of prospective interventions. EurValve has a cohort of 120 patients, 60 with aortic disease and 
60 with mitral disease, on which the process has been operated and on which the hypotheses 
can be tested. 
Hypothesis 1 centres around the representation of the haemodynamics of the valve. The 
computational fluid dynamics is performed using the Fluent software from ANSYS. This is 
arguably the leading CFD package in the world: it has been extensively validated and it is used 
in almost every branch of industrial fluid mechanics. There is no doubt that, if the problem is 
correctly formulated including appropriate definition of the anatomy and appropriate 

 
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boundary conditions, the resulting relationship will be accurate. EurValve assumes that the 
transient pressure/flow relationship can be approximated based on a series of steady-state 
simulations. Simulations performed in the first period of the project, and reported by WP3, 
support this assumption. The most important input to the CFD model is the anatomy, or the 
geometry of the diseased valve. The image segmentation to determine the geometry has 
been performed using state-of-the-art tools developed in WP3.2. We have learned that it is 
very difficult to capture valve images, at the appropriate point in the cardiac cycle, to provide 
images of sufficient quality to support the segmentation of the valve leaflets. This is especially 
true for the case of mitral regurgitation, for which we are seeking to identify a small orifice of 
complex geometry. Nevertheless, more than half of the cohort has been analysed to provide 
the data to test hypothesis 1. The valve pressure-flow relationship has been characterised 
using the CFD analysis protocol and the systems model operated to convergence over the 
cardiac cycle. The model provides the transient distributions of pressure, flow and volume 
that characterise the systems physiology, and from which the left ventricular pressure volume 
loop, amongst other features, can be computed. The most direct clinical measures of valve 
performance are the peak or mean pressure gradient across the diseased aortic valve and the 
regurgitant fraction of flow across the diseased mitral valve. These can be computed from the 
model and compared with the clinical measurements. The results are illustrated in Figure 12. 
These are the results for all cases.  
For the aortic valve there is a clear trend and, in terms of a specific intervention threshold of 
70 mmHg, six cases give an incorrect result (four false negatives and two false positives). At 
the project’s final review some of the outliers will be discussed: there are complications for 
several of these cases. For the mitral cases there is more scatter: this might reflect the 
difficulty of segmentation of the small orifice causing an inaccuracy of the computational 
result or difficulty in accurate clinical estimation of the regurgitant fraction, or both.  Once 
again reasons for specific outliers will be discussed at the review. Although the comparison is 
poorer for the mitral cases, there is perhaps a more urgent need for the model-based support 
because the current physiological parameters used for the decision are more difficult to 
determine and more subject to error. Figure 13 shows the measured versus computed end 
systolic and end diastolic volumes at rest post-intervention. These are regarded as more 
reliable clinical measures than the regurgitant fraction, and the correlation is stronger. 
At a technical level, the ROM-based computation is compared with the CFD-based 
computation in deliverable 3.4. The ROM provides a very effective method of computation of 
valve characteristics for the aortic valve, and operates in near real-time. This is a major 
achievement in terms of potential clinical utility of the image-based DSS for aortic valve. It 
also suggests that there is significant potential for alternative pathways based on simpler 
image-based parameterisation of the aortic valve without necessarily full segmentation of the 
leaflets, which would make the product applicable more widely in routine clinical application. 

 
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a. Peak Pressure Gradient (Aortic) 
b. Regurgitant fraction (Mitral) 
Figure 12: Image-based analysis results 
Patient at rest: a) Computed v Measured Peak Pressure Gradient for aortic cases, b) Computed v Measured 
Regurgitant Fraction for mitral cases 
As summarised in Section 7 Augmented Data, two primary workflows have been operated in 
EurValve. The first is the valve-image based workflow, operated on more than half of the 
cohort, and the second is the measurement-based workflow, operated on the whole cohort. 
Note that hypothesis 1 and Figure 12 are entirely associated with the accuracy of the valve-
image based processing workflow. It is not relevant to the second workflow, which uses the 
measured clinical values of the diagnostic parameters. 
 
Figure 13: End diastolic volumes and end systolic volumes  
Measured by cardiac magnetic resonance imaging plotted against volumes predicted by the model in the post-
operative rest state 

 
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At a technical level, the ROM-based computation is compared with the CFD-based 
computation in deliverable 3.4. The ROM provides a very effective method of computation of 
valve characteristics for the aortic valve, and operates in near real-time. This is a major 
achievement in terms of potential clinical utility of the image-based DSS for aortic valve. It 
also suggests that there is significant potential for alternative pathways based on simpler 
image-based parameterisation of the aortic valve without necessarily full segmentation of the 
leaflets, which would make the product applicable more widely in routine clinical application. 
Hypothesis 2 is much more difficult to validate, and will require an appropriately-powered 
clinical trial with appropriate end-points. There are very many issues to consider. In its clinical 
cohort EurValve is measuring patient activity data pre-intervention and post-intervention. 
This has sparked enormous interest in every clinical audience to whom the study has been 
presented. We believe that this is the first time that such a study has been conducted on a 
valve patient cohort. It provides potentially important quantitative diagnostic information 
pre-intervention, and a quantitative measure of change post-intervention. Interpretation is 
likely, however, to be complex. The spectrum of activity pre-intervention might indeed be a 
measure of the effect of the disease on the life of an individual, and there is no doubt that 
the activity levels measured in the EurValve cohort are low compared with those expected in 
a healthy peer group, but the study numbers are relatively low. Lack of activity might be 
attributable directly to the disease or to a host of other factors, from co-morbidities through 
to psychology and inclination. EurValve has produced a rich and deep set of data on its 
relatively small cohort, and analysis of associations continues and we believe will underpin a 
substantial number of publications, but the results will remain speculative and direction-
pointing until numbers are increased significantly.  
It is very interesting to explore the effect that the valve disease has under different 
physiological conditions. The disease causes high levels of ventricular work during even 
moderate exercise, and this is quantified by the model. This in turn limits the activity that the 
patient can undertake, and this might be measured directly by the six minute walk test, so 
one might expect a correlation with computed cardiac work, or a normalised measure (such 
as to body surface area). These are acute measures of the capacity of the heart do work, or 
at least of the capacity to translate this to external work. However all patients spend most of 
the time in a low activity state, and the propensity to degeneration towards heart failure 
might be more associated with cardiac work in the rest state than under exertion, although 
we do not know the balance between acute high load events and long-term mild overload in 
triggering the progression.  
In the post-intervention state we might expect that ventricular work, and/or peak power, 
reduces for any specific level of external work and that the patient might be able to do more 
post-intervention than they could do pre-intervention. Whether this will be reflected in 
measured activity as they go about their lives is yet to be determined, but EurValve is seeking 
a correlation between computed and measured changes. One of the most important clinical 
questions is whether the patient will experience positive remodelling and improved cardiac 
performance as a result of the reduced work. Again, it is unknown whether the rest or the 
stressed state is most likely to be associated with this phenomenon, but it is not unlikely that 

 
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the rest state might be dominant. It is interesting to examine whether there is an association 
directly between pre-intervention measures and outcome. In the EurValve study the focus is 
on short-term response, measured by increased activity, by increased performance in the six 
minute walk test or by reduction in ventricular mass based on image data over a short period 
of months after the intervention. One of the clinical centres has ethical approval and funding 
to continue the study on its cohort to a twelve month time point, and this will provide 
interesting follow-up data. EurValve does not have the capacity or the numbers to follow the 
patients to the harder clinical end points of morbidity and mortality, but this will be an 
important part of a clinical trial of the DSS.  
A major drive over coming months is to publish the associations that EurValve has been able 
to determine. This work is ongoing, but some positive associations that have already been 
identified are outlined below. An obvious complication in the seeking of associations is that 
relationships are not necessarily linear, but the data is sparse to support the establishment of 
nonlinear associations. As an example, it is not unlikely that if the valve disease does not raise 
the work or power requirement too much, and there has not been major ventricular 
remodelling, there might be little immediate benefit from an intervention. If the valve disease 
does cause significant change then it would be predicted that there would be strong benefit 
and potential to remodel, but there will come a time when the ventricle is so damaged that, 
although there might be immediate and acute benefits in relief of symptoms, it is no longer 
able to see the benefit of a longer-term remodelling process. Under these circumstances 
there will not be a linear relationship between disease severity, modelled benefit and 
ultimate prognosis.   
The EurValve study was designed to make use of data that could be readily available in clinical 
practice. In clinical practice there are many technical, operative and patient factors which are 
largely unpredictable (including procedural complications), and processing of the data for 
target clinical publication is still under way. Nevertheless a number of interesting and 
important trends been identified, including the correlation illustrated in Figure 13. Equally 
interesting from a clinical perspective is the apparent lack of correlation between some 
parameters which might have been expected to be related.  
It is difficult for clinicians to assess the severity of disease based on patient reported 
symptoms and capacity to exercise. The randomised clinical experiment has confirmed how 
useful clinicians would find objective data to guide decision making and assessing outcome. 
In deliverable 1.2 section 1.2.4.6 figure 6 we see how the EurValve Smart Home in a Box can 
show a trajectory. The project employed a number of ways of assessing patient activity, and 
one might expect that there is a correlation between them. In the pre and post intervention 
period there does not appear to be a strong correlation in the aortic stenotic patients.  
In the post-operative period, three measures of activity, or activity potential, were compared 
(six minute walk, watch data, and ‘Smart Home in a box’ technology from the University of 
Bristol). The latter was performed in the Sheffield cohort only, for logistical reasons. The six 
minute walk test is a clinically validated measure and has prognostic significance in valvular 
heart disease. This test was therefore used as the comparator. Figure 14 illustrates the 

 
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correlation between the distance walked in the six minute walk test and the measured step 
count and time spent outside the house (measured using the Bristol device) in the Sheffield 
aortic valve cohort. It also shows an apparent lack of correlation of the six minute walk test 
with clinical classification of disease including NYHA class and WHO QoL score. This suggests 
that these devices may have a role in monitoring patients following their intervention. Not 
surprisingly a correlation was also noted between step count pre-and post-intervention, 
indicating that a patient who was more active pre-intervention was also likely to be more 
active post-intervention. 
 
Figure 14: Correlation between measures of disease and exercise capacity 
As determined by the six minute walk test 
Sleep is an important part of health. EurValve developed a number of ways of assessing sleep 
quality, efficiency, frequency and duration. Data produced correlated with subjective reports 
and both the Philips Health watch and the Smart Home in a box produced similar outputs, 
increasing the confidence in the devices’ accuracy. The Pearson correlation coefficient 
between these measures was statistically significant at an R2 value of 0.854. 
The seeking of correlations between the primary model outputs of left ventricular work and 
power is still under way, and will continue to the point of publication in target clinical journals. 
Figure 15 illustrates a promising result in terms of correlation between the predicted change 
in left ventricular work and the measured increase in step count, as measured by the Philips 
watch. Unfortunately the numbers are small because the inclusion of the watch data was an 
opportunistic development in EurValve, not anticipated in the original DoW. This means that 
pre-intervention watch data is available on only a subset of the recruited cohort because their 

 
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interventions took place before the device was on stream. Nevertheless, as indicated many 
times in the EurValve reporting, this is one of the most exciting and clinically-relevant 
innovations of EurValve and has sparked very strong clinical interest.  
 
Figure 15: Correlation between computed change in cardiac work 
Using the valve-image based protocol, and change in step count for a small subset (eight patients) of the aortic valve 
cohort 
 
 

 
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9 BIOPSIES 
Endo-myocardial biopsies have been obtained at DHZB in Berlin only, in order to allow an 
analysis of proteomic data by MDC and the parameterisation of a 0d cell model that can be 
further developed into a detailed biophysical cell model. The details have been reported in 
D3.2 Software components Beta Release. Comprehensive results are reported in D3.4. 
 
Aortic Valve Disease  
- 
29 left ventricular samples have been analysed. 
 
Mitral Valve Disease the analysis position is as follows: 
- 
17 left ventricular biopsies have been analysed 
- 
26 atrial samples have been analysed.  
 
 
 

 
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10 MOBILE HEALTH TRACKERS 
The clinical sites have significant interest in the additional clinical opportunities to make use 
of data from mobile health tracker data (Bristol device, and the Philips Health Watch). 
Specifically, in the process of evaluating a particular treatment strategy, the devices may allow 
the detection of more subtle changes, which could be of interest in further clinical trial 
designs. It remains unclear, but worthy of further investigation, the extent to which the 
acquisition of such data can be of use in a predictive context of benefit to decision support in 
the future. This has been comprehensively reported in D4.6. 
 
 

 
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11 GENDER ASPECTS OF CARDIOVASCULAR DISEASE 
As gender aspects of cardiovascular disease have been described for several decades, we 
further investigated how these aspects integrate in individualised patient-specific profiles.  
Despite a slight recruitment bias towards male patients (58%, in line with prevalence), the 
size of the cohort can allow useful comparison of gender-specific aspects of valvular heart 
disease. 
 
 

 
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DEFINITIONS 
List of Key Words and Abbreviations 
BMI 
 
Body Mass Index 
BSA 
 
Body Surface Area 
CABG  
Coronary artery bypass grafting 
CCS 
 
Canadian Cardiovascular Society grading of Angina 
COPD  
Chronic obstructive pulmonary disease 
CT 
 
Computed Tomography 
DICOM Digital Imaging and Communications in Medicine Standard 
dPmax  
Max Pressure Drop 
dPmean 
Mean Pressure Drop 
DSS 
 
Decision Support System 
eCRF  
Electronic Case Report Forms 
ED 
 
End diastole 
EF 
 
Ejection fraction 
ES 
 
End systole 
FS 
 
Fractional shortening 
ICD 
 
International classification of disease 
JSON  
Javascript object notation 
LV 
 
Left ventricle 
LVEDD Left ventricle end diastolic diameter 
LVOT  
Left ventricular outflow tract 
LVPWD 
Left ventricle posterior wall diameter 
MR(I)  
Magnetic Resonance (Imaging) 
NYHA  
New York Heart Association Heart Failure Classification 
RV 
 
Right ventricle 
s/p 
 
Status Post 
STL 
 
Stereolithography 
STS 
 
Society of Thoracic Surgeons Risk Score 
TAVI 
 
Transcatheter aortic valve implantation 
WP  
 
Working plan 
 
 
 

 
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APPENDIX A: EURVALVE DATA SUMMARY 
For completeness we here summarise the acquisition and use of data within EurValve, and the Deliverables in which resulting analyses are reported. 
T# 
Task Name 
Lead 
Data 
Reporting 
WP2 
Data Collection and Sharing Infrastructure 
CYFRONET 
Patient-level test data for development 
D2.x 
2.1 
Data warehouse; data collection and publication suite 
STHFT 
Clinical test data for ArQ 
D2.3, D1.3 
2.2 
Model execution environment 
CYFRONET 
Clinical and modelling test data 
D2.4, D1.3 
2.3 
Integrated security and data encryption 
CYFRONET 
Clinical and modelling test data 
D2.5, D1.3 
2.4 
Real-time Multiscale Visualisation 
CYFRONET 
Results data, imaging and numerical 
D2.5, D1.3 
2.5 
Platform quality assurance 
CYFRONET 
Clinical test and live data 
D2.3, D1.3 
  
  
  
  
  
WP3 
Software Components 
PHILIPS 
Patient-level and population data 
D3.x 
3.1 
Machine learning tools 
PEN 
Population data; WP6 output data 
D3.2, D3.4 
3.2 
Image segmentation tools 
PHILIPS 
Patient-level multimodality imaging data 
D3.2, D3.4 
3.3 
Systems models 
USFD 
EHR, processed images, proteomics 
D3.2, D3.4 
3.4 
Variation and sensitivity analysis tools 
TUE 
Entire results set, EurValve cohort 
D3.2, D3.4 
3.5 
Proteomics data analysis tools 
MDC 
Proteomics libraries 
D3.2, D3.4 
3.6 
Reduced-order modelling tools 
ANSYS 
Parameterised geometries 
D3.2, D3.3, D3.4 
  
  
  
  
  
WP4 
Digital Patient Definition; Data Collection 
DHZB 
All clinical data, EurValve cohort 
D4.x 
4.1 
Digital patient definition 
DHZB 
Example clinical data 
D4.1 
4.2 
Study design; inclusion criteria 
DHZB 
Example clinical data 
D4.2 
4.3 
Literature data 
PEN 
(Comprehensive literature review) 
D4.3 
4.4 
Environmental data 
UBRIS 
EurValve cohort: activity data 
D4.5, D4.6, D1.3 
4.5 
Identification, recruitment, data for clinical cohort 
DHZB 
EurValve cohort: all clinical data 
D4.4, D4.7, D1.3 
 
 

 
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T# 
Task  Name 
Lead 
Data 
Reporting 
WP5 
Decision Support System 
THERENVA 
Full EurValve clinical data access 
D5.x 
5.1 
CDSS specification 
THERENVA 
(Data schema) 
D5.1 
5.2 
Integration of CDSS with computational infrastructure 
CYFRONET 
Example clinical data 
D5.2 
5.3 
CDSS beta version 
THERENVA 
Live EurValve clinical datasets 
D5.2 
5.4 
Case-based reasoning 
LTSI 
CBR clinical data libraries 
D5.3 
5.5 
CDSS final version 
THERENVA 
Access and operation across all data 
D5.4, D1.3 
  
  
  
  
  
WP6 
Operation on Study Cohort; Evaluation of CDSS 
DHZB 
Active operation across EurValve cohort 
D6.x 
6.1 
Operation on study cohort 
USFD 
Entire EurValve cohort data 
D6.1 
6.2 
Rule set derivation, study cohort 
PHILIPS 
Entire EurValve cohort data 
D6.2 
6.3 
Evaluation of CDSS on study cohort 
DHZB 
Entire EurValve cohort data 
D6.3 
6.4 
Evaluation of CDSS against clinical guidelines 
DHZB 
Entire EurValve cohort data 
D6.4 
6.5 
Evaluation of combined infrastructure/DSS platform  
CYFRONET 
Entire EurValve cohort data 
D6.4 
 

 
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