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EU research results

CORDIS

Personalised Decision Support for Heart Valve Disease

Fact Sheet

Result in Brief

Reporting

Results

Project information

EurValve

Grant agreement ID: 689617

Project website

Start date

1 February 2016
End date

31 January 2019

Funded under:

H2020-EU.3.1.5.

Overall budget:

€ 4 998 012,51
EU contribution

€ 4 998 012,50

Coordinated by:

THE UNIVERSITY OF SHEFFIELD

United Kingdom

Objective

Valvular Heart Disease currently affects 2.5% of the population, but is overwhelmingly a disease of the elderly and consequently on the rise. It is dominated by two conditions, Aortic Stenosis and Mitral Regurgitation, both of which are associated with significant morbidity and mortality, yet which pose a truly demanding challenge for treatment optimisation. By combining multiple complex modelling components developed in recent EC-funded research projects, a comprehensive, clinically-compliant decision-support system will be developed to meet this challenge, by quantifying individualised disease severity and patient impairment, predicting disease progression, ranking the effectiveness of alternative candidate procedures, and optimising the patient-specific intervention plan. This algorithmically-driven process will dramatically improve outcomes and consistency across Europe in this fast-growing patient group, maximising individual, societal and economic outcomes.

Field of Science

/social sciences/sociology/demography/mortality

Programme(s)

H2020-EU.3.1.5. - Methods and data

Topic(s)

PHC-30-2015 - Digital representation of health data to improve disease diagnosis and treatment

Call for proposal

H2020-PHC-2015-single-stage

See other projects for this call

Funding Scheme

RIA - Research and Innovation action

Coordinator

THE UNIVERSITY OF SHEFFIELD

Address

Firth Court Western Bank
S10 2tn Sheffield

United Kingdom

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 566 538,77

Website

Contact the organisation

Participants (12)

Sort alphabetically

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ANSYS FRANCE SAS

France

EU Contribution

€ 253 548,99

STICHTING CATHARINA ZIEKENHUIS

Netherlands

EU Contribution

€ 346 948,66

AKADEMIA GORNICZO-HUTNICZA IM. STANISLAWA STASZICA W KRAKOWIE

Poland

EU Contribution

€ 364 881,40

DEUTSCHES HERZZENTRUM BERLIN

Germany

EU Contribution

€ 427 147,85

UNIVERSITE DE RENNES I

France

EU Contribution

€ 242 171,66

MAX DELBRUECK CENTRUM FUER MOLEKULARE MEDIZIN IN DER HELMHOLTZ-GEMEINSCHAFT (MDC)

Germany

EU Contribution

€ 332 343,75

PHILIPS ELECTRONICS NEDERLAND BV

Netherlands

EU Contribution

€ 732 253,46

Philips GmbH

Germany

EU Contribution

€ 338 106,83

SHEFFIELD TEACHING HOSPITALS NHS FOUNDATION TRUST

United Kingdom

EU Contribution

€ 454 046,96

THERENVA

France

EU Contribution

€ 302 739,49

TECHNISCHE UNIVERSITEIT EINDHOVEN

Netherlands

EU Contribution

€ 280 448,10

UNIVERSITY OF BRISTOL

United Kingdom

EU Contribution

€ 356 836,58

Project information

EurValve

Grant agreement ID: 689617

Project website

Start date

1 February 2016
End date

31 January 2019

Funded under:

H2020-EU.3.1.5.

Overall budget:

€ 4 998 012,51
EU contribution

€ 4 998 012,50

Coordinated by:

THE UNIVERSITY OF SHEFFIELD

United Kingdom

Project information

EurValve

Grant agreement ID: 689617

Project website

Start date

1 February 2016
End date

31 January 2019

Funded under:

H2020-EU.3.1.5.

Overall budget:

€ 4 998 012,51
EU contribution

€ 4 998 012,50

Coordinated by:

THE UNIVERSITY OF SHEFFIELD

DE EN ES FR IT PL

Computer modelling improves treatment of valvular heart disease

It has been estimated that valvular heart disease (VHD) currently affects 2.5 % of the population, presenting significant health risks, sometimes fatal. To improve treatment, EurValve developed a decision support system (DSS) that gives clinicians access to a range of vital patient information.

VHD is an umbrella term for dysfunction with any of the heart’s four valves. In aortic stenosis the aortic valve opening becomes narrow (stenotic), limiting the amount of blood pumped by the heart. In mitral regurgitation the mitral valve does not close completely, meaning that blood can flow backward, reducing the heart’s ability to pump blood. Currently there are almost 4 million deaths annually from VHD in Europe. The EU-supported EurValve project developed a clinically-compliant DSS to help clinicians analyse the likely success of intervention and so optimise treatment. The right treatment, at the right time While VHD symptoms can be reduced by pharmacological intervention, there is no effective treatment for these structural problems, making invasive and risky surgical or catheter-based intervention necessary. Timing is crucial; operating too late risks irreversible heart failure, while too early carries unnecessary risk. To quantify the effects on the heart of a diseased valve and measure the potential benefits of repair or replacement, EurValve adapted an existing computational model, personalised for each subject. Using in-home tracking devices and the Philips Health Watch, the project monitored subjects’ daily activity levels during rest and exercise. The team also identified additional ‘biomarkers’ from analyses of their physiology, including measures of blood flow and blood pressure at a number of locations in the body. The full DSS includes clinical guidelines, risk scores with model-augmented diagnosis and prediction tools, reduced-order modelling for fast analysis, machine learning with physiological modelling, population data (where needed) and a case-based-reasoning module which finds comparable cases. This package can provide a consistent, repeatable, quantitative and auditable record of the clinical decisions made. The project demonstrated scalability by evaluating three pre-intervention and four post-intervention states for over 120 individuals. Additionally, three cases with aortic valve disease and two with mitral valve disease were scrutinised by 45 clinical specialists each randomly assigned three cases. For each case, two separate datasets were generated; one included only conventional diagnostic parameters, while the other included DSS information. The results showed high enthusiasm for the potential of the DSS in borderline cases. “To our knowledge, EurValve has delivered the first viable clinical DSS prototype for heart valve disease, based on computer simulation, operable during a single-session visit. This represents a significant step towards the introduction of advanced computational technology into clinical practice,” says project coordinator Prof. Rodney Hose. Similar models were developed for the mitral valve but have not yet been integrated into a comprehensive DSS. Entering the clinic EurValve contributes directly to the EU Health Policy which aims to: protect and improve the health of EU citizens, support the modernisation of health infrastructure and improve the efficiency of Europe’s health systems. According to Prof. Hose: “The system will principally benefit three groups of patients: those with borderline indications for treatment, those with complex haemodynamic conditions, and those who require valve repair.” In order to get regulatory approval and bring the technology fully into clinical practice, extensive clinical trials still need to be undertaken with one of the project’s SME partners, Therenva, with whom the team are currently enhancing a commercial product, EndoSize®. Several other aspects of the technology are also being commercially exploited, particularly reduced-order modelling to speed up solution times, activity monitoring as a diagnostic aid and machine learning for enhanced data usage.

Keywords

EurValve, heart, Valvular, Aortic Stenosis, valve, Mitral Regurgitation, health, decision support system

Project information

EurValve

Grant agreement ID: 689617

Project website

Start date

1 February 2016
End date

31 January 2019

Funded under:

H2020-EU.3.1.5.

Overall budget:

€ 4 998 012,51
EU contribution

€ 4 998 012,50

Coordinated by:

THE UNIVERSITY OF SHEFFIELD

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Project information

EurValve

Grant agreement ID: 689617

Project website

Start date

1 February 2016
End date

31 January 2019

Funded under:

H2020-EU.3.1.5.

Overall budget:

€ 4 998 012,51
EU contribution

€ 4 998 012,50

Coordinated by:

THE UNIVERSITY OF SHEFFIELD

United Kingdom

Periodic Reporting for period 2 - EurValve (Personalised Decision Support for Heart Valve Disease)

Reporting period: 2017-08-01 to 2019-01-31

Summary of the context and overall objectives of the project

The primary objective of EurValve was to produce potentially-useable (after regulatory approval) clinical Decision Support System (DSS) tools that could assist clinicians in difficult areas where extra data might assist the decision. EurValve developed a computational tool that provided extra, derived, 'biomarkers' (quantifying aortic stenosis and mitral regurgitation) obtained from 3D analyses of the patient's cardiac geometry. These data were added to an existing commercial valve-sizing system that is currently in clinical use.

The target user will be the healthcare professional, who will make the decision on the nature and timing of the intervention. The major advance of this system over current practice is that it integrates and interprets all heterogeneous data available about the patient, integrates population data where needed, and provides a consistent, repeatable, quantitative and auditable record of the information that contributes to the decision process. The DSS system will allow for in silico simulation of different treatment options and thus allow comparison of their immediate haemodynamic outcome.

The project has constructed a DSS for Heart Valve Disease. It includes a number of important developments and innovations. The DSS includes presentation of clinical guidelines, computational model-based diagnostic and prognostic indications and case-based reasoning. The DSS provides quantitative measures of disease severity and patient impairment, complementary to those gathered in the current clinical pathway, and predicts changes under candidate interventions. The potential of the DSS for improving the management and interventional planning was confirmed by a group of independent clinical users in a control experiment. There has been particular clinical interest in the use of activity monitoring to provide a measure of the degree to which the disease might affect everyday living, and to inform the physiological state in the core computational models.

The design of a practical tool for use by cardiovascular clinicians has been a significant step forward in bringing VPH technology close to the clinic. From the translational perspective, there are significant innovations in the integration of clinical data into the computational workflow and the personalisation of model parameters, and in the streamlining of the analysis process so that it is operable, with acceptable accuracy, in timescales of a few minutes. The process from medical image through data integration to computational results is automated, although there is the facility for inspection and manual correction of valve anatomies for difficult cases. Each case of the 120 cases in the EurValve cohort was processed in approximately four minutes.
The onward exploitation trajectory for EurValve is well-identified, and the prospects for a product with appropriate regulatory approval reaching the market in the foreseeable future are good. It is the intention of the developers to continue with this work, and to bring an enhanced EndoSize product with Decision Support to the market.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

WP2 Infrastructure
1. File Store service implemented as the Data Warehouse's architecture.
2. The File Store component implemented a security recommendation of encrypting all contents on the underlying storage resources
3. Dedicated portal was created
4. Web-based visualization component presenting 3D meshes of heart models as output of the segmentation process has been implemented.

WP3 Software Components –Models
1. Estimation of 6 parameters and influencing cardiac output. Estimation based on patient info and ICD codes, model trained with simulated data.
2. Segmentation tools for AV in CT + US TEE/TTE images available via webservice or stand alone cmd + GUI tools.
Parametric model that describes patient with anatomically meaningful parameters + allows 0D embedding and generation of synthetic patients
3. 0D circulation model for heart + tests, parametric 2D model of AV, initial 3D AV model that can be inferred from segmentation
4. Sensitivity analyses and uncertainty quantification toolkit + sensitivity analysis of 0D model. Simulation of test data for (1)
5. Reproduction of detailed ODE cell model, numerical analysis of improved cell excitation and calcium diffusion model. 54 samples measured
6. ROM for parametric 2D AV model, validation and testing of ROM. Automatic generation of iso mesh over heart cycle. Representation of temporal behaviour with SVD modes.

WP4 - Digital Patient Definition, Data collection
Two clinical trials conducted. A Retrospective Study with data from a large patient group, to facilitate the development of the mechanism to infer missing data, and to provide evidence for the generation of the rule sets that will drive the detailed decision support process to inform a ‘machine learning’ process.
The Prospective Study had 120 patients in 3 clinical centres. The computer model was used to simulate each patient’s heart before and after valve replacement, and the results compared with the actual results of the intervention.
Activity monitoring was performed using the Bristol Sphere kit in Sheffield and the Philips Health Watch in all 3 centres.

WP5 Decision Support System
The DSS is built on an existing decision support tool, and has integrated different concepts and patient-specific measures to improve the management of heart valve disease.

WP6 DSS Operation - Clinical
1. Ethical approvals obtained
2. A protocol implemented
3. Imaging standards implemented.
4. Amalgamated set of data defined allowing the storage of clinical parameters in conjunction with analyses performed and modelling output
5. Patients underwent activity monitoring both before and after intervention in STH, CATH and DHZB

WP7 Exploitation
WP7 exploitation ensured that the highest possible impacts of the practical and exploitable outputs are secured. Three reports on exploitation produced.
Dissemination of EurValve includes a website (Eurvalve.eu); 5 newsletters; a flyer; 6 publications and a slide deck. A number of outreach events were attended. A LinkedIn and Twitter group active.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The ambition of EurValve was to develop a Decision Support System (DSS) which went well beyond the state of the art. The DSS developed in EurValve represents a significant advance on current practice, because it combines the power of mechanistic physiological modelling with a level of personalisation that has not hitherto been achieved.

The primary advances beyond the state of the art that were achieved in EurValve are:
•The personalisation of the model using all of the information that is available in the clinical record and in the literature.
•The representation of the physiological envelope of the individual, extrapolating beyond the measurement condition.
•The exploration of the process of inclusion of data from the monitoring of the activity of the individual. This will exploit the rich data that is increasingly becoming available from self-monitoring of lifestyle activity

The EurValve proposal included a categorisation of impacts into multiple areas of applicability, including societal, users, the economy, the EC Work Programme and the call. In general the wider impacts at higher levels of abstraction will naturally benefit from any moves to make adjustments at these more practical levels.

eurvalve-1639-rasterisesmall1.png

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Project information

EurValve

Grant agreement ID: 689617

Project website

Start date

1 February 2016
End date

31 January 2019

Funded under:

H2020-EU.3.1.5.

Overall budget:

€ 4 998 012,51
EU contribution

€ 4 998 012,50

Coordinated by:

THE UNIVERSITY OF SHEFFIELD

Project information

EurValve

Grant agreement ID: 689617

Project website

Start date

1 February 2016
End date

31 January 2019

Funded under:

H2020-EU.3.1.5.

Overall budget:

€ 4 998 012,51
EU contribution

€ 4 998 012,50

Coordinated by:

THE UNIVERSITY OF SHEFFIELD

United Kingdom

Deliverables

Documents, reports (10)

Infrastructure Design Recommendations

Analysis of technologies for infrastructure: recommendations for design

Model-based Learning Report

Decision Support System Specification

Data Status Report

Data availability status report

Software Components Specifications

Data Summary and Review

Comprehensive data summary and review

Clinical Cohort Specification

Clinical Cohort and Inclusion Criteria Specification

Literature and Guidelines Report

Literature and Guidelines Analysis and Recommendations Report

Model-based Data Augmentation Report

Digital Patient Definition

Context-Specific Digital Patient Definition (Valve Assessment and Intervention)

Other (1)

Publications

Peer reviewed articles (12)

CFD- and Bernoulli-based pressure drop estimates: A comparison using patient anatomies from heart and aortic valve segmentation of CT images

Author(s): J?rgen Weese, Angela Lungu, Jochen Peters, Frank M. Weber, Irina Waechter-Stehle, D. Rodney Hose

Published in: Medical Physics, Issue 44/6, 2017, Page(s) 2281-2292, ISSN 0094-2405

DOI: 10.1002/mp.12203

Cloud computing infrastructure for the VPH community

Author(s): Piotr Nowakowski, Marian Bubak, Tomasz Bartyński, Tomasz Gubała, Daniel Harężlak, Marek Kasztelnik, Maciej Malawski, Jan Meizner

Published in: Journal of Computational Science, 2017, ISSN 1877-7503

DOI: 10.1016/j.jocs.2017.06.012

A Comprehensive Study of Activity Recognition Using Accelerometers

Author(s): Niall Twomey, Tom Diethe, Xenofon Fafoutis, Atis Elsts, Ryan McConville, Peter Flach, Ian Craddock

Published in: Informatics, Issue 5/2, 2018, Page(s) 27, ISSN 2227-9709

DOI: 10.3390/informatics5020027

Patient-specific modeling of left ventricular electromechanics as a driver for haemodynamic analysis.

Author(s): Augustin CM, Crozier A, Neic A, Prassl AJ, Karabelas E, Ferreira da Silva T, Fernandes JF, Campos F, Kuehne T, Plank G.

Published in: Europace, 2016, Page(s) 2016;18(suppl 4):iv121-iv129., ISSN 1532-2092

Image-Based Personalization of Cardiac Anatomy for Coupled Electromechanical Modeling

Author(s): A. Crozier, C. M. Augustin, A. Neic, A. J. Prassl, M. Holler, T. E. Fastl, A. Hennemuth, K. Bredies, T. Kuehne, M. J. Bishop, S. A. Niederer, G. Plank

Published in: Annals of Biomedical Engineering, Issue 44/1, 2016, Page(s) 58-70, ISSN 0090-6964

DOI: 10.1007/s10439-015-1474-5

Results of mitral valve repair with an adjustable annuloplasty ring 2 years after implantation

Author(s): Simon H. Sündermann, Markus Czesla, Jörg Kempfert, Thomas Walther, Patrick Nataf, Ehud Raanani, Stephan Jacobs, Ottavio Alfieri, Francesco Maisano, Volkmar Falk

Published in: Heart and Vessels, Issue 32/7, 2017, Page(s) 843-849, ISSN 0910-8327

DOI: 10.1007/s00380-016-0934-7

Beating heart mitral valve surgery: results in 120 consecutive patients considered unsuitable for conventional mitral valve surgery†

Author(s): Miralem Pasic, Simon Sündermann, Axel Unbehaun, Jörg Kempfert, Stephan Jacobs, Volkmar Falk

Published in: Interactive CardioVascular and Thoracic Surgery, 2017, ISSN 1569-9293

DOI: 10.1093/icvts/ivx139

Support for Taverna workflows in the VPH-Share cloud platform

Author(s): Marek Kasztelnik, Ernesto Coto, Marian Bubak, Maciej Malawski, Piotr Nowakowski, Juan Arenas, Alfredo Saglimbeni, Debora Testi, Alejandro F. Frangi

Published in: Computer Methods and Programs in Biomedicine, Issue 146, 2017, Page(s) 37-46, ISSN 0169-2607

DOI: 10.1016/j.cmpb.2017.05.006

Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic healthcare data from MIMIC-III and Bristol, UK

Author(s): Christopher J McWilliams, Daniel J Lawson, Raul Santos-Rodriguez, Iain D Gilchrist, Alan Champneys, Timothy H Gould, Mathew JC Thomas, Christopher P Bourdeaux

Published in: BMJ Open, Issue 9/3, 2019, Page(s) e025925, ISSN 2044-6055

DOI: 10.1136/bmjopen-2018-025925

A dataset for room level indoor localization using a smart home in a box

Author(s): Ryan McConville, Dallan Byrne, Ian Craddock, Robert Piechocki, James Pope, Raul Santos-Rodriguez

Published in: Data in Brief, Issue 22, 2019, Page(s) 1044-1051, ISSN 2352-3409

DOI: 10.1016/j.dib.2019.01.040

Image-Based Personalization of Cardiac Anatomy for Coupled Electromechanical Modeling

Author(s): A. Crozier, C. M. Augustin, A. Neic, A. J. Prassl, M. Holler, T. E. Fastl, A. Hennemuth, K. Bredies, T. Kuehne, M. J. Bishop, S. A. Niederer, G. Plank

Published in: Annals of Biomedical Engineering, Issue 44/1, 2016, Page(s) 58-70, ISSN 0090-6964

DOI: 10.1007/s10439-015-1474-5

DesigningWearable Sensing Platforms for Healthcare in a Residential Environment

Author(s): Xenofon Fafoutis, Antonis Vafeas, Balazs Janko, R. Simon Sherratt, James Pope, Atis Elsts, Evangelos Mellios, Geoffrey Hilton, George Oikonomou, Robert Piechocki, Ian Craddock

Published in: EAI Endorsed Transactions on Pervasive Health and Technology, Issue 3/12, 2017, Page(s) 153063, ISSN 2411-7145

DOI: 10.4108/eai.7-9-2017.153063

Conference proceedings (27)

A Model Execution Environment for Valvular Heart Condition Simulations”

Author(s): M Bubak et al

Published in: GCW Workshop 2017, Issue GCW Workshop 2017, 2017

Similarity measures with attributes selection for Case-Based Reasoning in TAVI

Author(s): Hélène FEUILLATRE, Vincent AUFFRET, Miguel CASTRO Hervé Le Breton, Mireille GARREAU and Pascal HAIGRON

Published in: Surgetica, 2017

SPHERE in a Box: Practical and Scalable EurValve Activity Monitoring Smart Home Kit

Author(s): James Pope; Ryan McConville; Michal Kozlowski; Xenofon Fafoutis; Raul Santos-Rodriguez; Robert Piechocki; Ian Craddock

Published in: Conference: 42nd IEEE Conference on Local Computer Networks (IEEE LCN) Workshops, At Singapore, Issue 42nd IEEE Conference on Local Computer Networks, 2017

Towards Model Execution Environment for Investigation of Heart Valve Diseases

Author(s): M. Bubak, T. Bartynski, T. Gubala, D. Harezlak, M. Kasztelnik, M. Malawski, J. Meizner, P. Nowakowski

Published in: CGW Workshop 2016, 24-26.10.2016, Issue CGW Workshop 2016, 24-26.10.2016, 2016

Eurvalve - Decision Support for Cardiovascular Disease

Author(s): M Kelm

Published in: Biophysical Society Meeting Thematic Meeting, 2018

Clinical Applications of Models in Cardiovascular Disease

Author(s): M Kelm

Published in: Computer assisted radiology and Surgery (CARS), 2018

Uncertainty in Model-Based Treatment Decision Support: Applied to Aortic Stenosis

Author(s): R. Meiburg, M. C. M. Rutten, F. N. van de Vosse

Published in: GCW Workshop 2017, Issue GCW Workshop 2017, 2017

Personalisation of cardiovascular models for patient benefit: from EurValve to hypertension

Author(s): D R Hose

Published in: Scandinavian/Baltic VPH workshop, 2018

Understanding the Quality of Calibrations for Indoor Localisation

Author(s): R McConville, D Byrne, I Craddock, R Piechocki, J Pope, R Santos-Rodrigue, N Twomey

Published in: IEEE 4th World Forum on Internet of Things, 2018

Person identification and discovery with wrist worn accelerometer data

Author(s): R McConville, R Santos-Rodriguez, N Twomey

Published in: Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2018

On-Board Feature Extraction from Acceleration Data for Activity Recognition

Author(s): Atis Elsts, Ryan McConville, Xenofon Fafoutis, Niall Twomey, Robert Piechocki, Raul Santos-Rodriguez, Ian Craddock

Published in: International Conference on Embedded Wireless Systems and Networks, 2018

Semantics, Knowledge and Grids on Big Data in 2017

Author(s): M Bubak

Published in: Advanced Applications: -> e-health SKG 2017, 2017

Advanced Security Services for Computer Simulation Research in Medicine

Author(s): J. Meizner, M. Bubak, T. Bartyński, T. Gubała, D. Harężlak, M. Kasztelnik, M. Malawski, P. Nowakowski

Published in: GCW Workshop 2017, Issue GCW Workshop 2017, 2017

Real-Time Patient-Specific Transaortic Pressure Gradient Computation from Parameterized Aortic Valve Geometries

Author(s): M Hoeijmakers

Published in: WBC2018, 2018

Data Management System for Investigation of Heart Valve Diseases, Workshop on Cloud Services for Synchronisation and Sharing,

Author(s): Marian Bubak, Daniel Harężlak, Steven Wood, Tomasz Bartyński, Tomasz Gubala, Marek Kasztelnik, Maciej Malawski, Jan Meizner, Piotr Nowakowski

Published in: 2017

Architecture for Managing and Querying Collaborative Datasets

Author(s): D. A. Silva Soto, S. M. Wood

Published in: GCW Workshop 2017, Issue GCW Workshop 2017, 2017

Uncertainty in model-based treatment decision support: applied to aortic stenosis

Author(s): R Meiburg

Published in: CMBBE2018, 2018

Model based personalised decision support for heart valve interventions; in session from the mitral circulation to large artery flows; challenges for clinical application

Author(s): D R Hose

Published in: WBC2018, 2018

Towards Real-Time Transaortic Pressure Gradient Computation

Author(s): M Hoeijmakers

Published in: VPH2018, 2018

Towards a Computational Decision Support System for Valve Interventions: the EurValve Project

Author(s): D R Hose

Published in: FRANCOPHONE and EUROPEAN AORTA MASTERCLASS, 2017

Personalized Estimation Methods for Trans-Catheter Aortic Valve Implantation Support.

Author(s): R Meiburg

Published in: 6th Dutch Bio-Medical Engineering Conference, Issue Jan 2017, 2017

EurValve Model Execution Environment in Operation

Author(s): M. Bubak, T. Bartyński, T. Gubała, D. Harężlak, M. Kasztelnik, M. Malawski, J. Meizner, P. Nowakowski

Published in: GCW Workshop 2017, Issue GCW Workshop 2017, 2017

VPH applications in the cloud with the Atmosphere platform – lessons learned

Author(s): Piotr Nowakowski, Marian Bubak, Tomasz Bartyński, Daniel Harężlak, Marek Kasztelnik, Maciej Malawski, Jan Meizner

Published in: Virtual Physiological Human 2016 Conference, 2016

Uncertainty in model-based treatment decision support: applied to aortic stenosis

Author(s): R Meiburg

Published in: WCB2018, 2018

Online Heart Rate Prediction using Acceleration from a Wrist Worn Wearable

Author(s): R Mcconville, G Archer, I Craddock, H ter Horst, R Piechocki, J Pope, R Santos-Rodriguez

Published in: 2018 KDD Workshop on Machine Learning for Medicine and Healthcare, Issue 2018 KDD Workshop on Machine Learning for Medicine and Healthcare, 2018

Patient specific 0D model of the systemic circulation to simulate the effects of valve stenosis and regurgitation

Author(s): Krzysztof Czechowicz, Gareth Archer, Pat Lawford, D. Rod Hose

Published in: VPH2018, 2018

Influence of Similarity Measures in Case-Based Reasoning for the Treatment of Valvular Heart Disease

Author(s): H. Feuillâtre, V. Auffret, M. Castro, H. Le Breton, M. Garreau, P. Haigron

Published in: GCW Workshop 2017, Issue GCW Workshop 2017, 2017

Project information

EurValve

Grant agreement ID: 689617

Project website

Start date

1 February 2016
End date

31 January 2019

Funded under:

H2020-EU.3.1.5.

Overall budget:

€ 4 998 012,51
EU contribution

€ 4 998 012,50

Coordinated by:

THE UNIVERSITY OF SHEFFIELD

Project information

EurValve

Grant agreement ID: 689617

Project website

Start date

1 February 2016
End date

31 January 2019

Funded under:

H2020-EU.3.1.5.

Overall budget:

€ 4 998 012,51
EU contribution

€ 4 998 012,50

Coordinated by:

THE UNIVERSITY OF SHEFFIELD

United Kingdom

Project information

EurValve

Grant agreement ID: 689617

Project website

Start date

1 February 2016
End date

31 January 2019

Funded under:

H2020-EU.3.1.5.

Overall budget:

€ 4 998 012,51
EU contribution

€ 4 998 012,50

Coordinated by:

THE UNIVERSITY OF SHEFFIELD

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Last update: 20 December 2019

Record number: 199897

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Computer modelling improves treatment of valvular heart disease

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Periodic Reporting for period 2 - EurValve (Personalised Decision Support for Heart Valve Disease)

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