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Moving the tipping point: the decision to anticoagulate patients with atrial fibrillation


Project Description

Atrial fibrillation (AF) is a heart condition that causes an irregular and often abnormally fast heart rate, and leads to a high risk of stroke and death. Treating AF patients with an increased risk of stroke is highly challenging. Although the risk of AF-related stroke can be lowered with anticoagulant drugs, these drugs increase the risk of bleeding. Mathematical modelling is ideally suited to the representation of the complex profile of risk factors present in an individual patient, the investigation of outcomes under different treatment strategies, and the determination of the optimal drug treatment strategy. The rates of strokes and bleeding events associated with different drugs have been measured in many large cohort studies and large-scale drug trials. Using these data, we can construct and parametrise Markov models, and then simulate the life histories of individual patients in order to determine the expected number of quality-adjusted life-years (QUALYs) under different treatment regimes. The optimal treatment for a given patient can be found using the Markov decision process framework.

Aims:
• Develop a data-driven Markov model for stroke and bleeding events, based on high-quality clinical data.
• Develop a data-driven Markov decision process, using the drug clinical trial data, for comparison of treatment strategies using aspirin, warfarin and non-vitamin K antagonist oral anti-coagulants (NOACs).
• Determine ‘tipping points’ - levels of stroke risk where the optimal treatment switches from one strategy to another.
This is a three-year project stemming from strategic collaboration for the advancement of cardiovascular and stroke research. This self-funded studentship would be part of the new Liverpool Centre for Cardiovascular Science (LCCS), where our partners include Liverpool John Moores University, Liverpool Heart & Chest Hospital, Liverpool Health Partners, and the University of Liverpool.

Supervisory team
The prospective PhD student will be supervised by Dr Robert Wilkinson, Dr Ivo Siekmann, Professor Gregory Lip, and Professor Paulo J. Lisboa.
Dr Wilkinson is a Lecturer of Applied Mathematics. His research interests are stochastic processes on networks and infectious diseases. Dr Siekmann, who is a Senior Lecturer of Applied Mathematics, applies stochastic models to various problems ranging from coagulation and cell signalling to ecology. Professor Lip, is Director of the LCCS, and is an international research leader in atrial fibrillation (AF) [http://expertscape.com/ex/atrial+fibrillation]. Professor Lisboa is the head of LJMU’s Department of Applied Mathematics and Engineering and Technology Research Institute. His research focus is in advanced data analysis for decision support.

The prospective PhD student
The successful candidate will have excellent programming skills in at least one of the following languages: Matlab, R, Python, C, Java, Fortran. They will ideally have some experience with Markov chain models and good knowledge of the theory of stochastic processes, as well as a strong interest in applying mathematical models to clinical questions. Applicants should have a good first degree (2:1 or above) in a relevant discipline. A Master’s degree in a relevant discipline is desirable. The prospective PhD student will be based at Liverpool John Moores University. A programme of formal research training will be provided.

Application process
For an informal discussion about this opportunity, please email Dr Robert Wilkinson () or Dr Ivo Siekmann (). To apply, please email Dr Robert Wilkinson (). Please include a covering letter, which details your suitability for the project, and a CV with contact details for two referees. Applicants must be available for interview either face to face or via videoconference.

Funding Notes

UK, EU and International students can apply for this studentship. This is a self-funded opportunity with a requirement for the student or sponsor to cover the cost of full tuition and bench fees. Applicants should expect to commence study in January or September 2020.

References

When applying on the website, please always quote the reference RWilkinson1

How good is research at Liverpool John Moores University in Computer Science and Informatics?

FTE Category A staff submitted: 9.70

Research output data provided by the Research Excellence Framework (REF)

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