IAHS STUDENTSHIP: Predicting the trajectories of health care resource utilisation and risks of exacerbations in Chronic Obstructive Pulmonary Disease (COPD) using linked electronic health records

Chronic obstructive pulmonary disease (COPD) is a progressive lung disease requiring timely diagnosis and assessment of the severity as well as intensive treatment and health care support to prevent disease progression. According to a recent National Institute for Health and Care Excellence (NICE) report, there are approximately 1 million people in the UK with diagnosed COPD and another 2 million with undiagnosed COPD. It is the fourth leading cause of death worldwide and fifth in the UK.

While smoking is the most important risk factor for COPD, several comorbid and pathophysiological conditions along with environmental factors are linked to COPD-associated exacerbations particularly hospital admissions in the UK. For clinicians, assessment of the frequency and length of health care resource utilisations (HCRU), the time to severe exacerbations and identifying strategies for the management of disease and preventing further progression are all vital.

However, COPD is clinically heterogeneous and therefore standard clinical measures and phenotyping may not assess the risk fully. The electronic health record (EHR) data are increasingly being used for several decision-making processes and are considered a potential resource for predictive modelling and risk assessment. The National Health Service (NHS) routinely manages multiple sources of linked EHR of patients, for example, demography, GP visits, pathophysiology and biochemical parameters, pharmacy, hospital and emergency admissions. More importantly, it is possible to identify longitudinal cohorts of patients with the exact date of exacerbation events which provides valuable information on disease dynamics; a critical element for a heterogeneous disease like COPD. Adopting appropriate strategies tailored to the individual patient may deliver improved healthcare leading to a personalised medicine-based approach to the management of COPD and reduction of burden on healthcare resources.

Harnessing these multiple sources of primary health care information could potentially contribute to the diagnosis, monitoring and evaluating the prognosis of COPD patients. There are, however, several methodological and technical challenges as these would involve the integration of high-volume and high-dimensional complex multi-source data along with the application of diverse mathematical, statistical and computational tools. In collaboration with an industry partner, we recently studied the association of baseline patient-level demography, physical, clinical and other pathophysiological predictors with the frequency and rate of exacerbations among COPD patients at subsequent time points and assessing the relevant health care resource allocation costs. The proposed PhD project will extend the work further to developing predictive analytical tools to identify the risk of severe exacerbations integrating multiple sources of EHR data with advanced modelling framework.

The project will use novel methodological techniques to establish the complex relationship between multiple sources of EHR COPD-related data. It will evaluate the distributional properties of the data, explore novel predictive modelling strategies in conventional as well as diverse machine learning and deep learning frameworks and examine their potential applications. The project will target several key gaps in the current methodologies with implications in wider disease contexts.

The project will provide an excellent opportunity for a prospective student to work in one of the most active and exciting areas of medical research: the student will develop key knowledge and skills of handling high-volume and high-dimensional complex EHR data, learn novel and advanced modelling tools particularly those related to high-dimensional data analysis and apply these tools to real clinical datasets.

Candidate Background:

• Applicants should hold a minimum of a 2:1 UK Honours degree (or international equivalent) in a relevant subject. Those with a 2:2 UK Honours degree (or international equivalent) may be considered, provided they have (or are expected to achieve) a Distinction or Commendation at master’s level.

We encourage applications from all backgrounds and communities, and are committed to having a diverse, inclusive team.

For further project information please contact Dr Mintu Nath ([Email Address Removed]).

Candidates should contact the lead supervisor to discuss the project in advance of submitting an application, as supervisors will be expected to provide a letter of support for suitable applicants.

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APPLICATION PROCEDURE:

International applicants are eligible to apply for this studentship but will have to find additional funding to cover the difference between overseas and home fees (approximately £14,000 per annum).

• Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php
• You should apply for Applied Health Science (PhD) to ensure your application is passed to the correct team for processing.
• Please clearly note the name of the supervisor and exact project title on the application form. If you do not mention the project title and the supervisor on your application, it will not be considered for the studentship.
• Applicants should hold a minimum of a 2:1 UK Honours degree (or international equivalent) in a relevant subject. Those with a 2:2 UK Honours degree (or international equivalent) may be considered, provided they have (or are expected to achieve) a Distinction or Commendation at master’s level.
• Your application must include: A personal statement, an up-to-date copy of your academic CV, and clear copies of your educational certificates and transcripts.
• Please note: you DO NOT need to provide a research proposal with this application
• General application enquiries can be made to [Email Address Removed]