Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - PIC (Personalised In-Silico Cardiology)

Teaser

Cardiovascular diseases (CVD) have a huge impact on society in terms of mortality, morbidity and healthcare costs, being responsible for 1.9 million deaths in the EU annually (42% of all deaths) with a total cost of €169 billion. Improving healthcare systems in Europe in a...

Summary

Cardiovascular diseases (CVD) have a huge impact on society in terms of mortality, morbidity and healthcare costs, being responsible for 1.9 million deaths in the EU annually (42% of all deaths) with a total cost of €169 billion. Improving healthcare systems in Europe in a period of ageing population and tightening financial constraints mandates a shift towards personalised and preventive management of disease. We need tailored and earlier treatments to increase the efficacy and efficiency of the healthcare system, as well as the quality of life of patients.

Recent scientific progress has created an exceptional capacity to simulate in-silico (i.e. on a computer) the heart and its interaction with the circulatory system. Patient-specific in-silico models provide a structured, reproducible and predictive framework for interpreting and integrating clinical data. This provides the pathway for developing personalised and preventive management strategies for cardiovascular diseases. In addition, recent advances in data science (i.e. machine learning, data mining) enable the extraction of novel insights and knowledge from the large repositories of clinical data of our health information systems.

PIC is the European ITN that is training a cohort of 15 future innovation leaders able to articulate and materialise the vision of Personalised In-silico Cardiology where healthcare is guided by in-silico models. These models become virtual reconstructions of an individual, or avatars, to evaluate current health status and therapy options. PIC fellows are building both mechanistic and statistical models from clinical data (WP1), enabling the extraction of biomarkers for better diagnosis and prognosis of the individual patient. PIC fellows are applying models to maximise the value of clinical data (WP2) to inform diagnosis, and to optimise clinical devices & drug choices (WP3) to deliver a personalised therapy.

The vision of a Personalised In-silico Cardiology is materialised in the definition of 15 inter-related projects for each of the fellows (F1 to F15), setting the scope of the research into four main modelling aspects of the heart (anatomy, mechanics, electrophysiology, and fluid dynamics) and four cardiac conditions (heart failure, cardiomyopathies, arrhythmias and flow obstructions). The specific objectives for the project are:
- WP1 focuses on the in-silico modelling technology. Its objectives are to develop the simulation methodologies, and to obtain robust biomarkers by cardiac model personalization. Interpreting clinical data through biophysical models allows the extraction of the underlying physiological parameters that best explain the data.
- WP2 focuses on the data. Its objectives are to use insilico cardiac models to reduce errors in clinical data, reduce invasiveness, and to maximise its diagnostic and prognostic value.
- WP3 focuses on the technologies for therapy: clinical devices and cardiac drugs. Its objective is to personalize these technologies through the adoption of the in-silico methodology and its predictions.
- WP4 focuses on the clinical translation of the in-silico technology. Its objective is to evaluate in specific CVD problems the envisioned improved care through better data, diagnosis and therapies.

Work performed

PIC is the European ITN that is training the cohort of 15 of the future innovation leaders able to articulate and materialise the vision of a Personalised In-silico Cardiology (PIC). It is addressing specific challenges originated by cultural and structural barriers between sectors and disciplines, articulating a fluent dialogue and work between clinicians and engineers.

The 15 PIC fellows were hired as planned, and the training programme is hitting all its milestones and deliverables. The consortium had internal guidelines and harmonised recruitment strategy defined, and we managed to bring all 15 PIC fellows to our kick-off meeting despite not all being hired yet at that time. We do follow a supervisory strategy based on bi-annual progress meetings, that is contributing to both the early identification and solution of risks, and to the construction of mutual trust and exchange of ideas. These meetings are documented in the ESR Reports about Career Development Plans (CDP), Supervision Quality Reports (SQR) and Progress Review Committee Minutes (PRC), which are regularly followed up with reminders from the Project coordinator (from KCL), which is the bulk of our deliverables.

The barriers between sectors have been opened through the personal mutual trust built both among the PIC fellows and the supervisors. Sharing the vision of the Personalised In-silico Cardiology has been the main enabler to bring the sectors and disciplines together. The main success indicator of the quality of the training is the external recognition received by fellows in scientific meetings, with a solid track of publications and awards as described in this report. Publications reflect also the tight collaboration among academic, clinical, industrial and regulatory partners – a white paper titled “The digital twin to enable the vision of precision cardiology”, integrating all beneficiaries of the consortium and under review in the major journal in the field of cardiology, is the main exponent of this collaboration.

The main outreach events were our two international Summer Schools, one in Oslo and one in Barcelona, hitting attendance numbers much larger than we ever expected (around 50 in both occasions), and thus creating a unique opportunity for our fellows and other researchers to network with word leaders in the area of computational cardiology. These events have also been very useful to articulate synergies with “sister Marie Skłodowska-Curie Actions”, such as AFibNet (g.a. 675351) or CardioFunXion (g.a. 642676).

Final results

\"PIC is working to deliver a better healthcare based on computational models of the heart, exploiting the in-silico reasoning power of machines based on both inductive (i.e. based on statistical modles) and deductive (i.e. based on mechanistic models) logic. The 15 PIC fellows are working towards this overarching goal, addressing different specific problems as part of their research projects.

The PIC training programme is preparing the 15 PIC fellows to face current and future societal challenges associated with the management of cardiovascular diseases. PIC is enabling an early detection of cardiac disease through modelbased diagnostic biomarkers, and the design of personalised therapies through predictive models. In-silico methodologies enable the optimization of clinical protocols, from data acquisition to device parameters and intervention choices, and the improved efficiency in the development of novel cardiac therapies and drugs.

There are two main training events planned for the rest of the project, a workshop in Maastricht this November focused on the “Efficient use of animal and virtual models in evaluation” , and our last Summer School in Oxford next May focused on the hoirzontal skills of entrepreneurship and career perspectives for PIC fellows, with the highlight of the \"\"Science and technology start-up week-end\"\" organised in conjunction with the Said Business School. The last training goals are related to the standarization and regulation procedures in medical industry, and intellectual propection and commercialization.\"

Website & more info

More info: https://picnet.eu/.