Computer to Clinic: Personalised Fluid-Mechanical Models Applied to Heart Failure
Lead Research Organisation:
King's College London
Department Name: Imaging & Biomedical Engineering
Abstract
Heart Failure (HF) is defined by the heart's reduced ability to pump blood due to a drop in cellular contractility, enlarged anatomy and increased coronary micro-vascular resistance. This loss of pump function accounts for a significant increase in both mortality and morbidity in western society. With the U.K.'s elderly population expanding, HF is rapidly becoming an epidemic. There is currently a 1 in 5 life-time risk of HF and costs associated with acute and long term hospital treatments are accelerating. The significance of the disease has motivated the application of state of the art clinical imaging techniques to aid diagnosis and clinical planning. Measurements of cardiac wall motion, chamber flow patterns and coronary perfusion currently provide high resolution data sets for characterising HF patients. However, the clinical practice of using population-based metrics derived from separate image sets often indicates contradictory treatments plans due to inter-individual variability in pathophysiology. Thus, despite imaging advances, determining optimal treatment strategies for HF patients remains problematic. To exploit the full value of imaging technologies, and the combined information content they produce, requires the ability to integrate multiple types of functional data into a consistent framework. This in turn will support a paradigm shift away from predefined clinical indices determining treatment options and a move towards true personalisation of care based on an individual's physiology.An exciting and highly promising strategy for underpinning this shift is the assimilation of multiple image sets into personalised and biophysically consistent mathematical models. The development of such models provides the ability to capture the multi-factorial cause and effect relationships which link the underlying pathophysiological mechanisms. Furthermore, using a biophysical basis presents unique opportunities to assist with treatment decisions through the derivation of quantities that cannot be imaged but are likely to play a key mechanistic role in HF e.g. tissue stress and pump efficiency.In parallel with imaging advances the approach is also underpinned by the ongoing development of complementary technologies, including improved numerical methods and increased performance per unit cost of computing. This computational progress has accelerated the addition of multi-physics functionality to a range of organ models which have recently been organized into international initiatives such as the IUPS sponsored Physiome and VPH projects. Within these programmes the heart is arguably the most advanced current exemplar of an integrated organ model. As such it represents a promising first candidate with which to focus on an important human disease.My goal during this fellowship will be to focus on personalising and applying these models in clinical and industrial settings for treating HF patients. Model simulations will be focused on quantifying diagnosis, aiding patient selection and guiding interventional planning for specific treatments carried out by leading clinicians based in the cardio-vascular imaging group at Kings College London (KCL). In addition to this direct clinical application of the model, the research will also be focused on the tuning of Left Ventricular Assist Devices (LVADs) which are often connected to the heart in HF to reduce mechanical load by pumping blood from the left ventricle directly into the aorta. Through these applications my aim is to both improve our understanding of this significant cardiovascular disease and demonstrate the potential of biophysical models for improving human healthcare.
Organisations
People |
ORCID iD |
Nicolas Smith (Principal Investigator) |
Publications
Xi J
(2014)
Understanding the need of ventricular pressure for the estimation of diastolic biomarkers.
in Biomechanics and modeling in mechanobiology
Xi J
(2013)
The estimation of patient-specific cardiac diastolic functions from clinical measurements
in Medical Image Analysis
Vigueras G
(2014)
Toward GPGPU accelerated human electromechanical cardiac simulations.
in International journal for numerical methods in biomedical engineering
Tøndel K
(2015)
Quantifying inter-species differences in contractile function through biophysical modelling.
in The Journal of physiology
Tøndel K
(2014)
Insight into model mechanisms through automatic parameter fitting: a new methodological framework for model development.
in BMC systems biology
Smith AF
(2014)
Transmural variation and anisotropy of microvascular flow conductivity in the rat myocardium.
in Annals of biomedical engineering
Smith A
(2014)
Transmural variation and anisotropy of microvascular flow conductivity in the rat left ventricular myocardium (675.4)
in The FASEB Journal
Sinclair MD
(2015)
Measurement and modeling of coronary blood flow.
in Wiley interdisciplinary reviews. Systems biology and medicine
Sinclair M
(2015)
Microsphere skimming in the porcine coronary arteries: Implications for flow quantification.
in Microvascular research
Schuster A
(2014)
Quantitative assessment of magnetic resonance derived myocardial perfusion measurements using advanced techniques: microsphere validation in an explanted pig heart system.
in Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Description | The timing of heart pump is more important than the pump rate |
Exploitation Route | Further collaboration with Berlin heart |
Sectors | Healthcare |
Description | Incorporated into the Berlin Heart design process of LVADs |
First Year Of Impact | 2014 |
Sector | Healthcare |
Impact Types | Economic |