Tefkros Chimonas

Email: xk19002@bristol.ac.uk

LinkedIn: https://www.linkedin.com/in/tefkros-chimonas-a18588234/

Project title: A single centre, unblinded, naturalistic pilot study to evaluate the feasibility, safety and efficacy of implementing a pragmatic dual-threshold adaptive deep brain stimulation workflow intended for general clinical use. 

Supervisory team: Professor Alan Whone, Professor Kenton O’Hara, Dr Hanna Kristiina Isotalus.

 

Project summary

Parkinson’s disease (PD) is a progressive neurodegenerative disorder with a rising incidence resembling the characteristics of a pandemic. It largely affects motor ability with symptoms like tremor, bradykinesia, rigidity and postural instability. There is no current cure for it, hence, personalized management tailored to the specific needs and characteristics of each patient is crucial to optimize therapeutic outcomes and improve their quality of life.

Deep Brain Stimulation (DBS) involves the implantation of stimulation electrodes into specific areas of the brain that regulate motor function, to help modulate abnormal electrical brain activity associated with PD and manage symptoms. Conventional DBS (cDBS), however, is limited by the lack of responsiveness to symptom fluctuations and needs to be regularly adjusted manually by a trained clinician for optimal symptom control. Adaptive DBS (aDBS) has just received CE marking and FDA approval to automatically and dynamically adjust stimulation parameters in real-time based on physiological feedback from the affected brain regions of each patient, offering a personalized approach to treatment.

My PhD research project is part of a single-centre, unblinded naturalistic pilot study to evaluate the feasibility, safety and efficacy of implementing a pragmatic dual-threshold aDBS clinical workflow intended for general clinical use, in collaboration with Medtronic and the North Bristol NHS Trust (NBT).  The study will be split into two phases. Phase 1 will involve programming and optimizing cDBS (on Parkinson’s patients already implanted with Medtronic’s Percept DBS device under the standard of care at NBT) based on standard clinical evaluation and objective data derived from physiological feedback to recruit stabilized patients for phase 2 – a characterization of the pragmatic workflow of aDBS in real-world settings.

The project will take a multidisciplinary approach involving a qualitative analysis of patient, carer and clinical staff experiences with the aDBS workflow and device through semi-structured interviews, as well as a quantitative analysis of clinical assessment and device data to evaluate aDBS efficacy in managing PD symptoms and ultimately improving quality of life. At the same time, the study will evaluate the practical aspects of implementing aDBS in a real-world clinical setting, including staff training needs and the number of programming visits required to optimise aDBS.  Furthermore, the study will employ a lumbar wearable device equipped with sensors to monitor the motor symptoms of participants and correlate mobility data with physiological brain feedback recorded from the aDBS device itself. The Unified Parkinson’s Disease Rating Scale (UPDRS) will be used to assess any changes in Parkinsonian symptoms, while other PD assessments like a non-motor symptom scale, a sleep quality scale, an occupational performance measure, and a patient symptom diary, will be used to assess any changes in the participant’s quality of life.

Bio

Just before embarking on the Digital Health and Care CDT journey, I completed my MEng Engineering Mathematics degree here at the University of Bristol. The course relied heavily on the concept of mathematical modelling and computational simulations across a variety of fields, exploring the art of applying maths to complex real-world problems, combining mathematical theory, practical engineering and scientific computing to address today’s technological challenges. Ofcourse, one of the fields we touched upon was healthcare and how we can use technology to enhance and support it.

I developed a keen interest in healthcare-related modules and projects, as they enabled me to see how all these mathematical models and simulations I was working on could have real-life implications, solving real-life health issues. For example, my Master’s thesis involved investigating the chemotactic behavior of sperm cells, using mathematical and computational simulations, to uncover the effect that their swimming techniques have on their performance in navigating towards successful fertilization. Advances in such investigations hold the exciting potential of enabling the use of targeted drug delivery, for instance, by developing novel artificial micro-swimmers that mimic this behavior through complex biological environments or improve diagnostics and management of male infertility.

Another eye-opening experience that sparked my research interests in this field was an internship I undertook at the university which simulated the environment and nature of research work being carried out at PhD level. Working closely with a research associate at the University, the main task was to develop a simulation for bacterial biofilm growth and subsequent antibiotic treatment to optimize and discover the most effective treatment strategy for chronic infections. I was fascinated by the development of these models and their potential to revolutionize and provide important insight to improve healthcare and optimize its delivery targeting real-life health issues. In a few words, I was hooked and I knew that digital health was a career path I wanted to follow. So, here I am today pursuing to explore the many exciting opportunities and possibilities that this CDT has to offer while seeking to create something impactful and meaningful for the greater good.

Bio

Just before embarking on the Digital Health and Care CDT journey, I completed my MEng Engineering Mathematics degree at the University of Bristol. The course relied heavily on the concept of mathematical modelling and computational simulations across a variety of fields, exploring the art of applying maths to complex real-world problems, combining mathematical theory, practical engineering and scientific computing to address today’s technological challenges. Ofcourse, one of the fields we touched upon was mathematical modelling in healthcare and how we can use it to enhance and support it.

I developed a keen interest in healthcare-related modules and projects, as they enabled me to see how all these mathematical models and simulations I was working on could have real-life implications, solving real-life health issues. For example, my Master’s thesis involved investigating the chemotactic behaviour of sperm cells, using mathematical and computational simulations, to evaluate the effect that their swimming technique has on their performance in navigating towards successful fertilization. Advances in such investigations hold the exciting potential of enabling the use of targeted drug delivery, for instance, by developing novel artificial micro-swimmers that mimic this chemotactic behaviour through complex biological environments or even improve diagnostics and management of male infertility.

Another eye-opening experience that sparked my research interests in the digital health field was a research internship I undertook at the university. Working closely with a research associate at the University, the main task was to develop a computational simulation of bacterial biofilm growth and antibiotic treatment to discover the most effective treatment strategies for chronic infections. I was fascinated by the development of these models and their potential to revolutionize and provide important insight for real-life health issues, improve healthcare and optimize its delivery. In a few words, I was hooked and I knew that digital health was a career path I wanted to follow. So, here I am today pursuing to explore the many exciting opportunities that this CDT has to offer while seeking to contribute to something impactful for the greater good.