Big data and mechanistic evaluation of left heart failure

Applications are invited for a BHF 4-year MRes/PhD studentship starting in October 2022 at the National Heart and Lung Institute (NHLI) in partnership with the Bioengineering Department at Imperial College.

Students will join a well established doctoral training program with bespoke teaching activities, seminars, mentors and workshops.

The Cardiovascular Sections of the National Heart and Lung Institute, Imperial College, are located within the Main Campus at South Kensington, the Brompton Campus and Hammersmith/White City Campus. Bioengineering is also located at Main Campus at South Kensington and at White City. Students will have the opportunity to work in state of the art facilities within a highly developed research environment where our ambition is to translate research findings to help those with cardiovascular diseases. All students benefit from a full programme of training in research and transferable skills organised through the Graduate School, the quality of which has been recognised several times at the Times Higher Education (THE) Awards.

Project summary

The BHF 4-year MRes/PhD studentships typically comprises a 1-year MRes in Biomedical Research, followed by a 3-year PhD. During the MRes year, students undertake two laboratory projects which will prepare them for the PhD.

Supervisors

• Professor Jamil Mayet, Professor of Cardiology, NHLI and Head of Cardiovascular NIHR
• Health Informatics Collaborative (HIC)
• Dr Choon Hwai Yap, Senior Lecturer, Dept of Bioengineering, Imperial College London
• Dr Amit Kaura, Cardiology Registrar, NIHR HIC Lead Researcher, Department of Cardiology
• Dr Guang Yang, UKRI Future Leaders Fellow, NHLI, Imperial College London

Overview

Heart failure is one of the most highly prevalent medical conditions that is associated with frequent hospitalization, high cost burden, and significantly impaired quality of life. In this project, we will:

• utilize a large dataset and machine learning to identify novel and improved measures that can better indicate heart function to help with heart failure diagnosis and prognosis.
• devise better ways to classify patients into categories relevant to treatment and management, to improve patient outcomes.
• perform advanced biomechanical modelling of the heart to validate these novel measures and classification approaches, to understand the mechanism for their better performance.

Scientific quality

The project will utilise data from the NIHR Health Informatics Collaborative (HIC), a partnership of 29 NHS Trusts, and guiding members of the public, working together to facilitate the re-use of NHS data. To date, the datasets generated have extracted structured data, including 600 variables categorised into demographics, blood tests, diagnosis, vital observations, medications, operations, echocardiography, and survival status. The first dataset comprised over 250,000 patients who had a troponin measured (ClinicalTrials.gov, NCT03507309), with several hypotheses tested. For this project, we will extract patient data between 2010 and 2021. Additionally, at Imperial, we have images for over 40,000 echocardiograms and 20,000 cardiac MRI scans linked by patient identifiers to the NIHR HIC dataset. These readily accessible datasets and image banks will allow study outputs to be generalisable across all ages, genders and ethnicities.

Our group has developed the methodology for to use big data and statistical approaches to emulate “target clinical trials” in the area of cardiology. Such emulated trials have the potential to enable identification of effective treatment strategies without the high costs and study design inflexibility associated with randomised controlled trials.

We further possess a validated, advanced computational model of the heart that is capable of conducting mechanistic investigations of how various cardiac characteristics (anatomy, contractility, vascular resistances, etc.) have an impact on cardiac function, and advanced machine learning algorithms that can enhance classification accuracy in biomedical problems.

Translation potential & expected value

The results have the potential to catalyse a change in the diagnostic criteria for heart failure, redefine different types of heart failure and provide evidence on the best treatment strategy across these subgroups. Findings will subsequently be tested with actual clinical trials in patients with heart failure with the aim of getting class I recommendations in clinical guidelines. The trials will recruit patients with heart failure with mildly reduced or preserved LV function, according to our new diagnostic criteria, and randomised to heart failure prognostic medication (dictated by the results of the PhD project) or no treatment. We will use the NIHR HIC infrastructure to follow-up patients recruited to future trials.

Training

• Professor Mayet will train the student in research ethics framework, critical appraisal, cardiac imaging, and research to clinical translation.
• Dr Yap will train the student in cardiac biomechanics theory and computational simulations.
• Dr Kaura will train the student in big data analytics, including data exploration, data wrangling, programming, modelling and results communication.
• Dr Yang is an expert in machine learning, and will train the student on classification techniques.

Applicant Requirements

Applicants must hold, or expect to obtain, a first or upper second-class honours degree or equivalent in an appropriate subject from a recognised academic institution. Candidates must fulfil College admissions criteria and meet BHF residency requirements.

Desirable

• Bachelors or Masters degree in Medicine, Physiology, or Engineering (Biomedical or Computational), or other degrees relevant to the proposed work.
• Experience with big data analytics and medical data statistics. 
• Programming experience in Matlab, Python and/or R.

How to Apply

To apply, please email Jaya Rajamanie ([Email Address Removed]) with the following documents.

• Your CV
• The names and addresses of at least two academic referees.
• A personal statement of no more than 1,000 words explaining your interest in the project and please ensure that you specify your degree classification for your undergraduate and postgraduate degrees.

Selected candidates will get a tour of the relevant campus. Please assume that your application has not been successful if you have not heard from us within a month of the closing date.

Closing date for all applications: 20^th February 2022

Interviews will be held online at the end of March.