Managing human disease and illness is one of the greatest challenges of the next century. Who are the people who are high-risk? How do we best diagnose, treat and rehabilitate them? And how should we allocate the right resources in the right places at the right times?
A high-powered international partnership of researchers and clinicians, led by Liverpool John Moores University, is set to take on the challenge after winning a €10m grant through the European Union’s Horizon funding scheme.
Using data science, the multidisciplinary team which includes six hospitals, eight universities, four companies and a charity, aims to develop personalised, integrated, multi-scale computational models and decision-support tools for stroke related to atrial fibrillation.
Atrial fibrillation is the most common heart arrhythmia worldwide and patients have a five-fold increased risk for ischaemic stroke.
Gregory Lip, Professor of Cardiovascular Medicine and Director of the Liverpool Centre for Cardiovascular Science at LJMU said the project would help clinicians predict AF in high-risk individuals as well as facilitate diagnosis and manage the rehabilitation of patients.
He said: “This novel and innovative project utilises data science to develop personalised approaches to the care pathway for stroke patients with atrial fibrillation. The latter is the commonest cardiac rhythm disorder in the population, and atrial fibrillation related strokes have a high mortality and disability.”
Drs Sandra Ortega-Martorell and Ivan Olier, of the School of Computer Science and Mathematics, who are leading on the project, said the one-size-fits-all approach of healthcare systems could be improved by using the power of data to personalise the individual pathways of patients.
And they propose to do so by applying a principle from data science called ‘virtual twinning’ or ‘data twinning’.
A virtual twin, in healthcare terms, refers to the digital representation of patients based on their characteristics and medical and health status history. Virtual twins enable the testing of potential treatment plans to find the optimal one for each patient, which can help determine the best course for its physical twin counterpart.
Such technology can be used to improve patient outcomes by enabling more accurate diagnoses, more personalised treatment plans, and more efficient resource allocation.
“It is a living, intelligent and evolving model which can optimise the processes and continuously predict future statuses and outcomes,” explains Ivan.
And by personalising services, the hope is that systems of this kind can optimise the treatment of each patient, says Sandra.
“It could not only improve care from the patients point of view but also avoid hospitals and healthcare providers carrying out inefficient actions thus saving them time and resources,” she said.
Other benefits include the avoidance of human bias in terms of the gender, ethnicity of other characteristics of the patients.
The five-year project, called TARGET, brings together diverse expertise from 10 countries (Austria, Belgium, France, Germany, Greece, The Netherlands, Romania, Spain, Sweden, and the UK. Hospital partners include the Liverpool Heart and Chest Hospital and the Liverpool University Hospital Foundation Trust.
The LJMU team synergises staff members from three research centres within LJMU: the Data Science Research Centre (DSRC), the Liverpool Centre for Cardiovascular Sciences (LCCS), and the Research Institute for Sport and Exercise Sciences (RISES).
It brings expertise in the development of novel artificial intelligence (AI) and digital twins technology (Olier and Ortega-Martorell); pathophysiology (Dawson and Thijssen); cardiovascular care and patient involvement (Lotto and Smith); clinical biochemistry (McDowell); musculoskeletal modelling and the biomechanics of the musculoskeletal system (Maganaris and Baltzopoulos); and a clinical world leader in atrial fibrillation and stroke (Lip).