Despite continuous advances in diagnosis and treatment, cardiovascular diseases (CVDs) remain the main cause of death worldwide. CVDs account for about 17.9 million annual deaths1 – 31% of all deaths worldwide – and greatly reduce the quality of life of affected patients, challenging the sustainability of modern healthcare systems. In Europe, CVDs are responsible for 30.4% and 25.3% of deaths before the age of 65, in men and women, respectively.2
Many cardiovascular drugs have shown limited efficacy on general populations. Personalised medicine approaches offer solutions to improve risk assessment, early diagnosis and patient-tailored treatment protocols. Data-driven, multi-cohort approaches are needed to link molecular, imaging, functional and clinical data. However, such integration presents a formidable challenge in terms of data storage and access frameworks, interoperability and IT architectures, especially across diverse jurisdictions.
European Society of Cardiology. Cardiovascular Disease Statistics 2017. Eur Heart J 2017; 39(7):508-579.
euCanSHare is a joint EU-Canada project to establish a cross-border data sharing and multi-cohort cardiovascular research platform.
Specifically, the project will integrate data infrastructures, IT solutions and data sources from EU, Canada and other countries into a web-based data access system with functionalities for increased efficiency in cardiovascular data-driven research. euCanSHare integrates more than 35 Canadian and European cohorts making up over 1 million records and actively seeks to expand to other regions.
euCanSHare key objectives are
implement a FAIR (Findable, Accessible, Interoperable and Re-usable) data platform for enhanced data sharing and big data research in cardiology
leverage the power of multi-cohort and multi-omics data in personalised cardiovascular medicine, addressing image analysis and bioinformatics, biomarker identification and quantification, knowledge discovery and risk assessment
apply legal frameworks to enable compliant data sharing across countries in line with Open Science tenets
provide the euCanSHare platform with capabilities to extend over time and thus create the largest network of cohorts and researchers in cardiovascular personalised medicine for academia, companies and public authorities
- Platform design, definition of user requirements and technical specifications
- Delineation of the legal and ethical framework to allow data sharing across EU and Canada
- Preliminary users engagement: definition of incentives to attract participants
- Implementation of platform interfaces and services, including user-designed functionalities, data harmonisation and analysis
- Initial user tests and feedback gathering
Four pilot-test research studies, feedback gathering and system refinements:
- Two research studies on multi-domain risk predictors in cardiovascular disease, integrating imaging, genetics, lifestyle and sex
- One public health study to compare risk estimates across countries and regions
- One industry-relevant study on drug target discovery to adjust commercial platform requirements
- Workshops with hands-on sessions to foster the expansion of the platform with new additional cohorts and users
Publications and materials
Devriendt, T., Borry, P., & Shabani, M. (2021). "Factors that influence data sharing through data sharing platforms: A qualitative study on the views and experiences of cohort holders and platform developers". Plos one, 16(7), e0254202.
The paper describes the barriers and concerns for the sharing of cohort data, including credit and recognition, the potential misuse of data, loss of control, lack of resources, socio-cultural factors and ethical and legal barriers, and the implications for data sharing platforms.
Hageman, S., Pennells, L., Ojeda, F., Kaptoge, S., Kuulasmaa, K., de Vries, T., ... & Völzke, H. "SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe." European Heart Journal.
This study developed, validated and illustrated an updated prediction model (SCORE2) to estimate 10-year fatal and non-fatal cardiovascular disease (CVD) risk in individuals without previous CVD or diabetes aged 40–69 years in Europe.
Devriendt, T., Shabani, M., & Borry, P. (2021). "Data Sharing in Biomedical Sciences: A Systematic Review of Incentives." Biopreservation and Biobanking.
This article provides a systematic review of the current and proposed incentive mechanisms for researchers in biomedical sciences and discusses their strengths and weaknesses.
Schmidt, C. O., Struckmann, S., Enzenbach, C., Reineke, A., Stausberg, J., Damerow, S., ... & Richter, A. (2021). "Facilitating harmonized data quality assessments. A data quality framework for observational health research data collections with software implementations in R." BMC Medical Research Methodology, 21(1), 1-15.
This work introduces a data quality framework for observational health research data collections with supporting software implementations to facilitate harmonized data quality assessments.
Richter, A., Schmidt, C. O., Krüger, M., & Struckmann, S. (2021). "dataquieR: assessment of data quality in epidemiological research." Journal of Open Source Software, 6(61), 3093.
dataquieR is an R package to conduct data quality assessments in data collections designed for research, making strong use of metadata that specify the requirements of the study data. Particular focus is placed on the influence of observers, examiners, and devices on the measurement process.
Raisi-Estabragh, Z., Gkontra, P., Jaggi, A., Cooper, J., Augusto, J., Bhuva, A. N., ... & Petersen, S. E. (2020). "Repeatability of Cardiac Magnetic Resonance Radiomics: A Multi-Centre Multi-Vendor Test-Retest Study." Frontiers in cardiovascular medicine, 7, 289.
The study evaluates the repeatability of cardiac magnetic resonance (CMR) radiomics features on test-retest scanning using a multi-centre multi-vendor dataset with a varied case-mix
Cetin, I., Raisi-Estabragh, Z., Petersen, S. E., Napel, S., Piechnik, S. K., Neubauer, S., ... & Lekadir, K. (2020). "Radiomics signatures of cardiovascular risk factors in cardiac MRI: Results from the UK Biobank." Frontiers in cardiovascular medicine, 7.
The paper addresses the performance of Cardiovascular magnetic resonance (CMR) radiomics models for identifying changes in cardiac structure and tissue texture due to cardiovascular risk factors.
Raisi-Estabragh, Z., Harvey, N. C., Neubauer, S., & Petersen, S. E. (2020). "Cardiovascular magnetic resonance imaging in the UK Biobank: a major international health research resource." European Heart Journal-Cardiovascular Imaging.
The article considers how we may best utilize the UKB CMR data to advance cardiovascular research and review notable achievements to date
Cetin, I., Raisi-Estabragh, Z., Petersen, S. E., Napel, S., Piechnik, S. K., Neubauer, S., ... & Lekadir, K. (2020). "Radiomics signatures of cardiovascular risk factors in cardiac MRI: Results from the UK Biobank." Frontiers in Cardiovascular Medicine, 7.
The article analyses and confirms the feasibility and potential of CMR radiomics for deeper image phenotyping of cardiovascular health and disease
De Sutter, E., Zaçe, D., Boccia, S., Di Pietro, M. L., Geerts, D., Borris, P., & Huys, I. (2020). "Implementation of Electronic Informed Consent in Biomedical Research and Stakeholders' Perspectives: Systematic Review." Journal of medical Internet research, 22(10), p.e19129.
The review provides an overview of the ethical, legal, regulatory, and user interface perspectives of multiple stakeholder groups in order to assist responsible implementation of electronic informed consent in biomedical research.
Bovenberg, J., Peloquin, D., Bierer, B., Barnes, M., & Knoppers, B. M. (2020). How to fix the GDPR's frustration of global biomedical research. Science, 370(6512), 40-42.
The article examines whether there is room under the GDPR for EU biomedical researchers to share data from the EU with the rest of the world to facilitate biomedical research, and proposes solutions for consideration by either the EU legislature, the EU Commission, or the EDPB in its planned Guidance on the processing of health data for scientific research, urging the EDPB to revisit its recent Guidance on COVID-19 research
Zemrak, F., Raisi-Estabragh, Z., Khanji, M. Y., Mohiddin, S. A., Bruder, O., Wagner, A., ... & Nothnagel, D. (2020). "Left Ventricular Hypertrabeculation Is Not Associated With Cardiovascular Morbity or Mortality: Insights From the Eurocmr Registry." Frontiers in cardiovascular medicine, 7, 158.
The study assessed the association of LV trabeculation extent with cardiovascular morbidity and all-cause mortality in patients undergoing clinical cardiac magnetic resonance (CMR) scans across 57 European centers from the EuroCMR registry
Raisi‐Estabragh, Z., Biasiolli, L., Cooper, J., Aung, N., Fung, K., Paiva, J. M., ... & Rayner, J. J. (2020). "Poor bone quality is associated with greater arterial stiffness: insights from the UK Biobank." Journal of Bone and Mineral Research.
The article considers the potential mediating effect of a range of blood biomarkers and cardiometabolic morbidities and evaluates differential relationships by sex, menopause status, smoking, diabetes, and obesity, and considers whether associations with arterial compliance explained association of SOS with ischemic cardiovascular outcomes
Lekadir, K., Leiner, T., Young, A. A., & Petersen, S. E. (2020). Current and Future Role of Artificial Intelligence in Cardiac Imaging. Frontiers in Cardiovascular Medicine, 7.
This editorial provides a perspective of current and potential future role of AI in cardiac imaging
Zhuang, X., Xu, J., Luo, X., Chen, C., Ouyang, C., Rueckert, D., ... & Liu, Y. (2020). Cardiac segmentation on late gadolinium enhancement MRI: a benchmark study from multi-sequence cardiac MR segmentation challenge. arXiv preprint arXiv:2006.12434.
This paper presents the selective results from the Multi-Sequence Cardiac MR (MS-CMR) Segmentation challenge, in conjunction with MICCAI 2019.
Devriendt, T., Shabani, M., & Borry, P. (2020). "Data sharing platforms and the academic evaluation system". EMBO reports, e50690.
The article describes current efforts for Open Science and making it part of the academic evaluation system.
Raisi-Estabragh, Z., Cooper, J., Judge, R., Khanji, M. Y., Munroe, P. B., Cooper, C., ... & Petersen, S. E. (2020). "Age, sex and disease-specific associations between resting heart rate and cardiovascular mortality in the UK BIOBANK." PloS one, 15(5), e0233898.
The article defines the sex, age, and disease-specific associations of resting heart rate (RHR) with cardiovascular and mortality outcomes in 502,534 individuals from the UK Biobank over 7–12 years of prospective follow-up
Raisi-Estabragh, Z., Kenawy, A. A., Aung, N., Cooper, J., Munroe, P. B., Harvey, N. C., ... & Khanji, M. Y. (2020). "Variation in left ventricular cardiac magnetic resonance normal reference ranges: systematic review and meta-analysis." European Heart Journal-Cardiovascular Imaging.
The article determines population-related and technical sources of variation in cardiac magnetic resonance (CMR) reference ranges for left ventricular (LV) quantification through a formal systematic review and meta-analysis
Raisi-Estabragh, Z., Izquierdo, C., Campello, V. M., Martin-Isla, C., Jaggi, A., Harvey, N. C., ... & Petersen, S. E. (2020). "Cardiac magnetic resonance radiomics: basic principles and clinical perspectives." European Heart Journal-Cardiovascular Imaging, 21(4), 349-356
The article provides an overview of radiomics concepts for clinicians, with particular consideration of application to CMR, and reviews existing literature on CMR radiomics, discusses challenges, and considers directions for future work
Martin-Isla, C., Campello, V. M., Izquierdo, C., Raisi-Estabragh, Z., Baeßler, B., Petersen, S. E., & Lekadir, K. (2020). "Image-based cardiac diagnosis with machine learning: A review." Frontiers in Cardiovascular Medicine, 7, 1.
Cardiac imaging plays an important role in the diagnosis of cardiovascular disease (CVD). Until now, its role has been limited to visual and quantitative assessment of cardiac structure and function. However, with the advent of big data and machine learning, new opportunities are emerging to build artificial intelligence tools that will directly assist the clinician in the diagnosis of CVDs. This paper presents a thorough review of recent works in this field and provide the reader with a detailed presentation of the machine learning methods that can be further exploited to enable more automated, precise and early diagnosis of most CVDs.
Campello, V. M., Martín-Isla, C., Izquierdo, C., Petersen, S. E., Ballester, M. A. G., & Lekadir, K. (2019, October). "Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI." In International Workshop on Statistical Atlases and Computational Models of the Heart (pp. 290-299). Springer, Cham.
Accurate segmentation of the cardiac boundaries in late gadolinium enhancement magnetic resonance images (LGE-MRI) is a fundamental step for accurate quantification of scar tissue. However, while there are many solutions for automatic cardiac segmentation of cine images, the presence of scar tissue can make the correct delineation of the myocardium in LGE-MRI challenging even for human experts. As part of the Multi-Sequence Cardiac MR Segmentation Challenge, we propose a solution for LGE-MRI segmentation based on two components. First, a generative adversarial network is trained for the task of modality-to-modality translation between cine and LGE-MRI sequences to obtain extra synthetic images for both modalities. Second, a deep learning model is trained for segmentation with different combinations of original, augmented and synthetic sequences. Our results based on three magnetic resonance sequences (LGE, bSSFP and T2) from 45 different patients show that the multi-sequence model training integrating synthetic images and data augmentation improves in the segmentation over conventional training with real datasets. In conclusion, the accuracy of the segmentation of LGE-MRI images can be improved by using complementary information provided by non-contrast MRI sequences.
Jagodzinski, A., Johansen, C., Koch-Gromus, U., Aarabi, G., Adam, G., Anders, S., ... & Betz, C. S. (2019). "Rationale and Design of the Hamburg City Health Study." European journal of epidemiology, 1-13.
The Hamburg City Health Study (HCHS) is a large, prospective, long-term, population-based cohort study and a unique research platform and network to obtain substantial knowledge about several important risk and prognostic factors in major chronic diseases. A random sample of 45,000 participants between 45 and 74 years of age from the general population of Hamburg, Germany, are taking part in an extensive baseline assessment at one dedicated study center. Participants undergo 13 validated and 5 novel examinations primarily targeting major organ system function and structures including extensive imaging examinations. The protocol includes validate self-reports via questionnaires regarding lifestyle and environmental conditions, dietary habits, physical condition and activity, sexual dysfunction, professional life, psychosocial context and burden, quality of life, digital media use, occupational, medical and family history as well as healthcare utilization. The assessment is completed by genomic and proteomic characterization. Beyond the identification of classical risk factors for major chronic diseases and survivorship, the core intention is to gather valid prevalence and incidence, and to develop complex models predicting health outcomes based on a multitude of examination data, imaging, biomarker, psychosocial and behavioral assessments. Participants at risk for coronary artery disease, atrial fibrillation, heart failure, stroke and dementia are invited for a visit to conduct an additional MRI examination of either heart or brain. Endpoint assessment of the overall sample will be completed through repeated follow-up examinations and surveys as well as related individual routine data from involved health and pension insurances. The study is targeting the complex relationship between biologic and psychosocial risk and resilience factors, chronic disease, health care use, survivorship and health as well as favorable and bad prognosis within a unique, large-scale long-term assessment with the perspective of further examinations after 6 years in a representative European metropolitan population.
Cetin, I., Petersen, S. E., Napel, S., Camara, O., Ballester, M. Á. G., & Lekadir, K. (2019, April). "A radiomics approach to analyze cardiac alterations in hypertension." In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) (pp. 640-643). IEEE.
Hypertension is a medical condition that is well-established as a risk factor for many major diseases. For example, it can cause alterations in the cardiac structure and function over time that can lead to heart related morbidity and mortality. However, at the subclinical stage, these changes are subtle and cannot be easily captured using conventional cardiovascular indices calculated from clinical cardiac imaging. In this paper, we describe a radiomics approach for identifying intermediate imaging phenotypes associated with hypertension. The method combines feature selection and machine learning techniques to identify the most subtle as well as complex structural and tissue changes in hypertensive subgroups as compared to healthy individuals. Validation based on a sample of asymptomatic hearts that include both hypertensive and non-hypertensive cases demonstrate that the proposed radiomics model is capable of detecting intensity and textural changes well beyond the capabilities of conventional imaging phenotypes, indicating its potential for improved understanding of the longitudinal effects of hypertension on cardiovascular health and disease.
Baker, D. B., Knoppers, B. M., Phillips, M., van Enckevort, D., Kaufmann, P., Lochmuller, H., & Taruscio, D. (2018). "Privacy-preserving linkage of genomic and clinical data sets." IEEE/ACM transactions on computational biology and bioinformatics, 16(4), 1342-1348.
The capacity to link records associated with the same individual across data sets is a key challenge for data-driven research. The challenge is exacerbated by the potential inclusion of both genomic and clinical data in data sets that may span multiple legal jurisdictions, and by the need to enable re-identification in limited circumstances. Privacy-Preserving Record Linkage (PPRL) methods address these challenges. In 2016, the Interdisciplinary Committee of the International Rare Diseases Research Consortium (IRDiRC) launched a task team to explore approaches to PPRL. The task team is a collaboration with the Global Alliance for Genomics and Health (GA4GH) Regulatory and Ethics and Data Security Work Streams, and aims to prepare policy and technology standards to enable highly reliable linking of records associated with the same individual without disclosing their identity except under conditions in which the use of the data has led to information of importance to the individual's safety or health, and applicable law allows or requires the return of results. The PPRL Task Force has examined the ethico-legal requirements, constraints, and implications of PPRL, and has applied this knowledge to the exploration of technology methods and approaches to PPRL. This paper reports and justifies the findings and recommendations thus far.
Nguyen, M. T., Goldblatt, J., Isasi, R., Jagut, M., Jonker, A. H., Kaufmann, P., ... & Tassé, A. M. (2019). "Model consent clauses for rare disease research." BMC medical ethics, 20(1), 55.
Rare Disease research has seen tremendous advancements over the last decades, with the development of new technologies, various global collaborative efforts and improved data sharing. To maximize the impact of and to further build on these developments, there is a need for model consent clauses for rare diseases research, in order to improve data interoperability, to meet the informational needs of participants, and to ensure proper ethical and legal use of data sources and participants’ overall protection. A global Task Force was set up to develop model consent clauses specific to rare diseases research, that are comprehensive, harmonized, readily accessible, and internationally applicable, facilitating the recruitment and consent of rare disease research participants around the world. Existing consent forms and notices of consent were analyzed and classified under different consent themes, which were used as background to develop the model consent clauses. The IRDiRC-GA4GH MCC Task Force met in September 2018, to discuss and design model consent clauses. Based on analyzed consent forms, they listed generic core elements and designed the following rare disease research specific core elements; Rare Disease Research Introductory Clause, Familial Participation, Audio/Visual Imaging, Collecting, storing, sharing of rare disease data, Recontact for matching, Data Linkage, Return of Results to Family Members, Incapacity/Death, and Benefits.
PRESS RELEASES AND EDUCATIONAL PUBLICATIONS
"Call for cardiovascular scientists to contribute data to multinational platform" (Press release, 06/04/2021)
This press release announces the launch of the public euCanSHare platform.
"GA4GH GDPR Brief: The Concept of Personal Data in the GDPR"
This brief article by Alexander Bernier (McGill University’s Centre of Genomics and Policy) clarifies the concept of personal data in the context f the GDPR, identifiability and re-identification risks
"La maquina que predice quien enfermara" (El Pais, 27 November 2019)
A newspaper article with interview with the PC (SPANISH)
"euCanSHare: EU-Canada joint data platform to facilitate multi-study cardiovascular research" (Press release, 12/12/2018)
A press release annoucing project inception, rationale and mission, produced and circulated right after the project kick-off meeting (Brussels, 11 December 2018)
Deliverable 4.4 - Bioinformatics Toolbox (30/11/2020)
This deliverable demonstrates the applicability of a bioinformatical tool (part of a larger toolbox) that can either analyse external data through an upload mechanism or offer the automatic analysis of internal server-housed data
Deliverable 4.3 - Cardiac image analyser (30/11/2020)
This deliverable presents the Cardiac Image Analyzer, the first open-source image analysis platform for cardiac magnetic resonance images (CMR)
Deliverable 3.4 - Guidelines and protocol for data deposition (30/11/2020)
This deliverable describes the guidelines and protocols for medical data deposition for the Euro-BioImaging medical imaging archive
Deliverable 3.2 - Data Management Plan v2 (30/11/2020)
This deliverable ummarizes the required updates on the euCanSHare’s Data Management Plan, after the first half of the project execution
Deliverable 2.2 - Data distribution protocols and interfaces (30/11/2020)
This deliverable reviews the efforts dedicated to the development and implementation of interoperability channels among euCanSHare components, as well as the creation of uniformed connections with external consumers and services
Deliverable 1.3 - Comparative cross-mapping table detailing which participating cohorts are compliant with euCanSHare requirements (30/11/2020)
This deliverable provides a holistic review of the consent materials, governance documentation, and data use permissions of 31 participating euCanSHare cohorts
Deliverable 1.2 - Policy "Points to Consider" tool to guide research projects, policy makers (30/11/2020)
This deliverable reports an analysis of the ethico-legal requirements enshrined in data privacy law and research ethics guidance in Canada and the European Union
Deliverable 4.1 - Opal software integration to euCanSHare (30/11/2019)
This deliverable reports on the integration of the OBiBa software suite (Opal, Mica and Agate) and its harmonization and cataloguing resources (harmonization guidelines and metadata standards) in euCanSHare
Deliverable 2.1 - Initial Infrastructure framework and documentation (30/11/2019)
This deliverable describes the technological foundations of the cloud-based euCanSHare data portal, including the computational infrastructure for data management and analysis, the authentication and authorization system and user interfaces
Deliverable 3.1 - Data management plan (31/05/2019)
This deliverable describes data handled within euCanSHare and the data handling model, including data collection, secure long-term storage, integration and interoperability, accessibility and exploitation
Deliverable 6.1 - Project website (28/02/2019)
This deliverable reports the process of design, collective refinement and implementation of euCanSHare public website, along with up-to-date images and infographics