Adaptive, Multi-scale, Data-Infused Biomechanical Models for Cardiac Diagnostic and Prognostic Assessment

Driven by structural and functional abnormalities in the muscle of the heart, Heart Failure (HF) is a complex syndrome affecting around 900,000 people in the UK. HF results in a fundamental reduction in the ability of heart muscle to effectively pump and deliver blood to the body. While this deficiency in pump function is easily observed using medical imaging, dissecting the underlying cause of this reduced performance in terms of its implications for muscle structure and function remain open challenges. Further, predicting how disease will progress or respond to therapy remains an unmet need that would substantially improve patient care.

Using state-of-the art biomechanical modelling, the "Adaptive, Multi-scale, Data-Infused Biomechanical Models for Cardiac Diagnostic and Prognostic Assessment" project will address these challenges by providing a modelling framework for assessment of the heart. Uniting measurements from microscopy, rheology, and medical imaging, this project aims to create biomechanical models that provide detailed information on the structure and function of the heart aiding diagnosis. Further, the platform will provide infrastructure for predictive modelling, simulating the response and adaptation of the heart over time. Biomechanical models will be systematically validated using animal heart models, providing rich data for understanding the biomechanics in vivo and ex vivo. The framework will, further, be directly translated through a novel study in patients with hypertrophic cardiomyopathy, providing a test bed for validation of predictive models of disease progression and response to therapy through virtual surgery.

The central aim of this project is to bring biomechanical modelling and analysis to the clinic. This will be achieved through the development of an adaptive, multiscale, data-infused biomechanical modelling framework that will facilitate diagnostic and prognostic assessment in the failing heart. This framework will provide a new, and rigorously validated, mechanism for assessing the structural and functional characteristics of the heart in vivo. Additionally, the developed framework will provide a platform for predictive modelling that accounts for remodelling responses observed in animal and human studies conducted through the project. Delivery of this technology would provide a new mode for characterising the heart and optimising treatment, presenting pathways for improving clinical outcomes and reducing economic costs.

Impact in Clinical / Basic Research: A core outcome of this project is a biomechanical modelling infrastructure that enables assessments of the heart muscle and predictive simulation of heart function through time. This would provide a valuable tool for research in both clinical and basic science arenas as explained in Academic Beneficiaries and Academic Impact sections. Realising this potential, the developments in this project - which require interdisciplinary input - extend to a broad range of potential end users, enabling the cross-pollination of ideas through training workshops and education programmes (see Pathways to Impact). I will also utilise the assembled network of project partners, working in a range of complementary fields, to ensure that outcomes of this work are maximised.

Economic Impact: The proposed work builds a fundamental biomechanical infrastructure for diagnostic and prognostic modelling. Achieving the aims of this work, my team will demonstrate the capacity to use personalised biomechanical models to assist in patient stratification as well as perform predictive modelling of patient outcomes due to progression of disease or surgical intervention. These demonstrative first results would garner significant interest. While the economic impact of the work is likely to follow the grant, I will liaise through our department's commercialisation officer (Neil Simrick) to explore potential opportunities. I will also explore my connections with Siemens (and Siemens Image guided therapy), to maximise the potential benefits of this work.