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Adaptive modelling for clinical kidney research across geographically different populations

School of Medicine, Medical Sciences & Nutrition

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Dr D Blana , Dr Simon Sawhney No more applications being accepted Competition Funded PhD Project (UK Students Only)
Aberdeen United Kingdom Applied Mathematics Bioinformatics Biomedical Engineering Data Analysis Epidemiology Statistics

About the Project

Acute kidney injury (AKI) is a sudden worsening of kidney function that is central to the care of people who are critically unwell in hospital. Symptoms are frequently hidden or non-specific, but AKI is common (1 in 7 hospital admissions), serious (fourfold increased hospital mortality) and hospital care alone costs the NHS £1 billion/year. AKI remains important even after hospital discharge with 1 in 3 emergency hospital readmissions, long term increased mortality, kidney failure, and cardiovascular events.

Prognostic models can be used to inform health policy and clinical decision-making, but differences in health, health care, health care recording, and changes over time, mean that the findings of clinical research may have limited validity in different regions and health systems. Current inconsistencies in operationalising and reporting kidney health, diseases and health outcomes is one barrier to the transparency of clinical research. We have therefore set up collaborations across regional populations in the UK, North America and Northern Europe with the aim to harmonise kidney care and research.

The aim of this project is to develop strategies to improve the robustness of clinical kidney research using statistical or engineering methods, and evaluating them across different geographical populations. Moreover, to address issues of mis-calibration when used in different clinical contexts, the project will develop and evaluate suitable adaptation methods.

The student will work in the Aberdeen Centre for Health Data Science (ACHDS). We are a dynamic and multi-disciplinary group of engineers, scientists and clinicians that aim to improve health and care with data. The supervisory team includes Dr Dimitra Blana, Biomedical Engineer and Lecturer in ACHDS with extensive experience in modelling of dynamic systems, and Dr Simon Sawhney, Senior Clinical Lecturer in Nephrology and expert in kidney disease clinical research. In Grampian, we host a world-leading kidney data-linkage platform, covering over two decades of healthcare across community, emergency, outpatient, and inpatient settings. The student will have the opportunity to work with our collaborators, to evaluate their methods across different regional populations.

Application procedure:

This is a four-year fully funded PhD studentship for UK candidates. We encourage students to apply early, as applications will close when a suitable candidate is found.

This project is advertised in relation to the research areas of the APPLIED HEALTH SCIENCE. Formal applications can be completed online: You should apply for the Degree of Doctor of Philosophy in Applied Health Science, to ensure that your application is passed to the correct person for processing.

Note clearly 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, then it will not be considered for the studentship.

Funding Notes

This project is funded by the School of Medicine, Medical Sciences & Nutrition. Full funding is available to UK candidates only. Overseas candidates can apply for this studentship but will have to find additional funding to cover the difference between overseas and home fees (approximately £16,625 per annum).
Candidates should have (or expect to achieve) a minimum of a 2.1 Honours degree in Data Science or a related subject.


Sawhney S, Beaulieu M, Black C, et al. Predicting kidney failure risk after acute kidney injury among people receiving nephrology clinic care. Nephrol Dial Transplant. 2020;35(5):836-845. doi: 10.1093/ndt/gfy294.
Davis E, Laski T, et al. Calibration drift in regression and machine learning models for acute kidney injury. J. Am. Med. Inform. Assoc. 24(6), 2017, 1052–1061. doi: 10.1093/jamia/ocx030
Sawhney, S., Robinson, H. A., Van Der Veer, S. N. et al. Acute kidney injury in the UK: A replication cohort study of the variation across three regional populations. BMJ Open. 2018, 8(6):e019435

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