EPSRC Reference: |
EP/Z531182/1 |
Title: |
EPSRC Centre for Future PCI Planning |
Principal Investigator: |
McGinty, Dr S |
Other Investigators: |
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Researcher Co-Investigators: |
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Project Partners: |
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Department: |
School of Engineering |
Organisation: |
University of Glasgow |
Scheme: |
Standard Research TFS |
Starts: |
01 August 2024 |
Ends: |
31 July 2028 |
Value (£): |
1,275,541
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EPSRC Research Topic Classifications: |
Continuum Mechanics |
Numerical Analysis |
Statistics & Appl. Probability |
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EPSRC Industrial Sector Classifications: |
No relevance to Underpinning Sectors |
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Related Grants: |
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Panel History: |
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Summary on Grant Application Form |
Percutaneous Coronary Intervention (PCI) is a common clinical procedure used to treat obstructive coronary artery disease, one of the leading causes of death. The overwhelming majority of patients will receive drug-eluting stent devices that act as a supporting scaffold and deliver drugs to counteract renarrowing. While this technology has been truly revolutionary, hundreds of thousands of patients worldwide annually still require an invasive repeat procedure, representing a huge economic burden on society and increasing pressure on health care resources. The key issue is that it is currently not feasible to quantitatively predict the immediate effect of a specific intervention and if/when a patient will suffer from renarrowing in the longer-term. Tools that enable optimisation of the procedure on a patient-specific basis are therefore urgently needed to improve patient outcomes and alleviate the resource burden on healthcare providers.
Critical to optimising the procedure is assessment of the individual patient's level of disease. Advances in medical imaging technology now make it possible to visualise the degree of obstruction and, crucially, the composition of the underlying plaque, potentially providing clinicians with a wealth of information to inform and plan PCI. However, decisions are presently left to operator experience and there are no definitive guidelines for how to optimise PCI for a given patient, particularly in complex cases.
In recent years, we have seen significant developments in computational models of PCI, that have the potential to inform PCI strategy in the future. However, they suffer from limitations and significant methodological advances are required before they can be routinely integrated within the clinic. These primarily relate to increasing the realism and accuracy of the models, improving their robustness, predictive power and speed of computation. This last point is critical, with the exorbitant run times of current computational models significantly hampering timely decision support and genuine impact in the clinic.
The EPSRC Centre for Future PCI Planning will address these challenges by developing a computational decision support tool to assist clinicians with PCI planning. Advances in mathematical modelling of fluid-structure interaction, lesion preparation, drug delivery and growth & remodelling, allied to statistical inference, emulation, uncertainty quantification and optimisation will enable us to create computational tools able to answer key clinical questions like:
1) What will a given patient's artery look like immediately after device deployment?
2) How should the plaque be modified prior to stent deployment, and what specialist tools should be used to do this?
3) What length and diameter of stent should be used, and what should be the balloon deployment inflation pressure?
4) What is the optimal placement of the stent?
5) In the case of complex bifurcation lesions, where potentially multiple stents and balloons are deployed, what is the optimal technique?
6) To what extent is the artery likely to renarrow, over what time course, and how can the PCI strategy be optimised to avoid this?
7) Can we effectively plan PCI solely on pre-procedural imaging such as Computed Tomography?
Working together with world-leading International Centres, and a range of leading imaging and medical device companies, the EPSRC Centre for Future PCI Planning will develop novel and robust mathematical and statistical methodologies, supported by large clinical data sets, to create the novel, fast and accurate tools that will help realise our vision of integrating computational tools for PCI planning within the clinic.
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Key Findings |
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Potential use in non-academic contexts |
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Impacts |
Description |
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Summary |
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Date Materialised |
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Sectors submitted by the Researcher |
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Project URL: |
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Further Information: |
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Organisation Website: |
http://www.gla.ac.uk |