Personaised In-silico Cardiology (PIC)

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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.

Healthcare provision can conceptually be simplified into three main processes: acquisition of clinical data, diagnosis & therapy planning, and delivery of treatment & intervention. Current technology allows a rich data acquisition, the use of sophisticated devices to monitor patients and deliver care. However clinical practice is guided by the use of averaged (population-based) metrics to define therapy strategies, missing many of the opportunities for disease prevention and tailoring of care for the individual patient.

In this context, 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 will train a cohort of 15 future innovation leaders able to articulate and materialise the vision of Personalised In-silico Cardiology (see Fig. 1) 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 will build 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 will apply 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.

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