A whole-heart model of multiscale soft tissue mechanics and fluid structure interaction for clinical applications (Whole-Heart-FSI)

Heart disease is the leading cause of disability and death in the UK and worldwide, resulting in enormous health care costs. Risk prediction on an individual patient basis is imperfect. Advanced medical development has already saved many lives, particularly in systolic heart failure. However, there is currently no treatment option for diastolic heart failure (with preserved ejection fraction) due to its complexity of multiple mechanisms and co-modality. Structural heart diseases, such as myocardial infarction (MI- commonly known as heart attack) and mitral regurgitation (MR, a leakage of blood through the mitral valve to left atrium in systole), where biomechanical factors are crucial, are often precursors to heart failure. MI can eventually lead to dilated heart failure despite immediate treatments post-MI. MR can induce pulmonary hypertension and oedema and subsequently, right heart overload and heart failure. The grand challenge is for these situations the heart simply cannot be modelled as an isolated left ventricle (as in most of the current studies); flow-structure interaction (FSI), heart-valve interaction, multiscale soft tissue mechanics, and tissue growth and remodelling (G&R) all play important roles in the progression of the structural diseases.

This project is set up to meet this challenge by delivering a multiscale computational framework to include Whole-Heart FSI with G&R. Making use of the novel mathematical tools (constitutive laws, G&R, upscaling and statistical inference) developed by SofTMech, I will build a realistic four-chamber heart model that include heart-valve, chamber-chamber, heart-blood, and heart-circulation interactions, which will be powerful enough to model MI, MR and their pathological consequences. This work will be in close collaboration with my clinical, industrial and academic collaborators. The model will quantify which factors lead to adverse G&R and what variations are to be expected as the disease progresses. We will also identify significant biomechanical markers (e.g. constitutive parameters, energy indices, stress/strain evolution). The predictive values of these biomechanical parameters will be assessed against other established predictors of adverse remodellings, such as duration of ischaemia, final coronary flow grade after a primary percutaneous coronary intervention, and microvascular obstruction revealed by MRI. Thus, this project will generate new testable hypotheses and will be a significant step up towards more consistent decision-support for clinicians, since increasingly the pace and complexity of medical advances outstrip the ability of individual clinicians to cope with. Due to the statistical emulation and uncertainty quantification built into the project, the model predictions will be fast and quantified with error bounds on the outcome of alternative treatments. Consequently, we will also address the critical aspect of convincing clinicians that information obtained from simulations will be correct and relevant to their daily practice. The proposed research is right within the Healthcare Technologies "Optimising Treatment" and "Developing Future Therapies" priority areas, as well as targeting "New Connections from Mathematical Sciences", and "Statistics and Applied Probability" of Mathematical Sciences.

Academic impact: This research, combining whole-heart multiscale modelling and fast emulation, will lead to a translational framework that scientists, engineers, and clinicians one day can use to plan and develop new intervention strategies. The work will also generate a valuable database of key classifiers in geometry and material properties associated with disease progression and identify how various interactions (chamber-chamber, chamber- valve, chamber-circulation) affect the heart function and stress/strain distributions, what drives the growth law, which parameters are strongly associated with eventual heart failure, and where we should focus our attention on in terms of treatment and prevention. For example, this heuristic approach will enable clinical scientists to study the effects of abnormalities of remote vessels of the circulation networks (which may be easier to monitor) to the heart function, and vice versa, or for companies to design better grafts that will reduce the compliance mismatch between the aorta and the heart. The energy budget analysis of the FSI within the heart will provide useful scalar measures for the complex 3D dynamic fluid fields, and thus, quantify interventions associated with the minimum energy dissipation. Importantly, we will build novel emulators to speed up the modelling process and assess model uncertainties, and move towards real-time clinical-support systems. This project will broaden the research base of SofTMech by both implementing and expanding the models developed for the left ventricle and provide multidisciplinary training for two RAs, as well as for PhD students.

Socio-Economic impact: The project will improve the quality of healthcare in the UK and beyond, and enhance the UK's global competitiveness by addressing the most important healthcare problems. Heart disease is the leading killer in the world, responsible for 30% of all deaths each year. MI is a leading cause of premature morbidity and mortality worldwide. Determining which patients will experience heart failure and sudden cardiac death after an acute MI is notoriously difficult for clinicians. The occurrence of MR increases with age (up to 10% of the population aged over 75), the burden of MR is likely to increase significantly in the future due to the ageing population. Severe MR leads to dilatation and failure of the left ventricle (LV), enlargement of the left atrium (LA), atrial fibrillation, pulmonary hypertension, stroke and even sudden death. This poses severe economic and medical challenges for the UK healthcare system. The research will generate a range of new mathematical and computational models for studying some common heart diseases. For example, developing predictive and quantitative models of healing processes post-MI will facilitate translational medical research to enhance diagnosis, treatment, and prevention. By developing test- and data-based modelling, we will examine how mitral dysfunction affects the function of the heart through FSI and the energy budget, and which parameters are strongly associated with adverse remodelling due to MR leading to heart failure. This fundamental research will significantly advance our understanding of disease pathogenesis, diagnosis and responses to therapy, and hence move clinical research forward.

Whole-Heart FSI will be directly supported by Terumo Aortic, GSK, Dassault Systemes Simulia, NHS Scotland, Scottish Health Innovations Ltd, hospitals (Berry and Danton), SofTMech, the Medical University of Graz, Kings College of London, and Ecole Polytechnique. It will embark on extensive networking activities and expand cutting-edge heart research in the UK.