Research within the Centre for Personalised Medicine
Acute Kidney Injury
As health care increases in complexity, the interaction between long term medical conditions, medication and inter-current illness can result in acute kidney injury (AKI). Nationally, in the UK and Ireland, it is estimated that one in five emergency admissions into hospital are associated with AKI. Of the estimated 100,000 AKI associated deaths in the UK, 1/4 to 1/3 might be prevented with earlier recognition and management. The resource and economic burden upon the healthcare economy is considerable (resulting in an estimated additional cost of £500 million alone).
In an area where there are barriers to healthcare access, and where the best outcome can be secured with timely intervention, this study of AKI will provide greater understanding of various factors that influence outcome. It will also help our understanding of how improvements in diagnostic processes and the stratification of patients improve care.
Through the collection of baseline data (physiological/laboratory) and patient outcome measures, we are using a data analytics approach to identify determinants of risk of developing AKI. We are also investigating the role of novel biomarkers in determining and stratifying patients who are at risk of developing AKI so that early interventions can be delivered if required. This research will aim to ensure optimal management of those patients at risk of AKI.
Primary Coronary Intervention in MyocardialInfarction
The Electrocardiogram (ECG) has been a crucial diagnostic tool for around 70 years particularly in the recognition of patients with heart attacks. Despite its long established role, there is a growing body of research to show that both the medical profession and allied healthcare professionals have difficulty in accurately interpreting the ECG such that crucial diagnoses such as heart attacks are missed. This problem with ECG interpretation has been recognised as an issue for a considerable period of time such that computer based algorithms have been developed by several companies to assist humans with ECG interpretation. In spite of significant advancements in the computer based algorithms, the machine interpretation as well as the human interpretation remains sub-optimal.
The main aims of this project is to improve the diagnostic accuracy of both the human and the machine in the ECG interpretation of acute heart attacks and to explore ways to improve clinical decision making. To achieve this, we are evaluating the various factors that influence clinical decision-making in the Primary Percutaneous Coronary Intervention pathway (PPCI – a treatment pathway for cardiac patients). Through a number of specific projects we are examining the human and machine approach to ECG interpretation. It is envisaged that the results of this project will optimise the clinical decision making pathway associated with ECG interpretation and develop new commercially attractive solutions to this ongoing area of need.
Emergency surgery cases account for 10% of hospital admissions in the North-West of Ireland and internationally. The Emergency Surgery Research Cluster is currently investigating the pattern, presentation and management of emergency surgery cases. The focus is on clinical care pathway redesign, biomarkers and point-of-care (POC) diagnostics for patients through the following steps: (a) baseline data collection, (b) data analysis to identify clinical outcome determinants [including the role of novel biomarkers], clinical care pathway redesign [including the integration of POC diagnostics and decision support software] followed by (c) prospective clinical evaluation of the redesigned care pathway and (d) translation to clinical and commercial utility.
The project is highly interdisciplinary, combining the computational expertise at the ISRC at UU, extensive knowledge in biology at Northern Ireland Centre for Stratified Medicine, and the Emergency Surgery clinical expertise at Letterkenny University Hospital and Altnagelvin Hospital. The results of the project will translate into an Emergency Surgery Registry that allows easily accessible clinical and economic data relating to emergency surgery cases. It is expected that the re-designed care pathways resulting from the work of this research cluster will reduce mortality, morbidity and cost of care.
Unscheduled Care in Diabetes
The overall aim of this work plan is to facilitate and enhance decision support for people with diabetes, their families and health care staff in order that care may be safely provided within community settings.
- To identify the most frequent reasons for unscheduled admission to hospital through a retrospective chart review.
- To observe patients admitted to three emergency departments for unscheduled care in order to identify interventions that may enable urgent care needs to be managed safely within a community setting.
- To test two interventions, but these cannot be specified prior to conducting Phase1.
- Explore the additional value offered by the measurement of a range of acute inflammatory markers.
- Evaluate the ways in which point-of –care measurement of capillary blood samples can assist the management of people requiring urgent unscheduled care.
- Develop capacity of the research team across a range of research methodologies.
- Multi-professional collaborations underpinned by knowledge transfer will be developed across the 3 sites, to include clinicians, business partners, interactive technical communication and software developers.
- To raise the profile of the partner organisations through the production of effective decision making support, testing of interventions to reduce urgent care admissions, high quality publications and a diverse dissemination strategy.
Two interventions that offer potential solutions to patients being admitted for unscheduled care are being tested. The robustness of the design and methods of the studies and also the outcomes in terms of improved clinical and patient decision making relating to urgent care needs are the principle measures of success. These outcomes may include IT such as point of care testing, online algorithms or educational programmes for people with diabetes, their carers or health care professionals or initiatives in health service delivery such as with the ambulance services or telemedicine. Our aim is to reduce the number of people with diabetes being admitted to hospital for unscheduled care by 10%.
Data Analytics and Modelling for Dementia Diagnosis
The aim of this research cluster is to make use of large and heterogeneous data and develop advanced computational techniques to improve the diagnosis accuracy of dementia, particularly Alzheimer’s Disease. We are developing a model that will identify early markers of the disease, differentiate among disease sub/types, and provide risk predictions. Early markers of the disease will include identifying new biomarkers. This will in turn improve clinical decision support for pre-clinical to clinical stages.
The project is highly interdisciplinary, combining the computational expertise at the ISRC at UU, extensive knowledge in biology at UU-Stratified Medicine and NUIG; dementia and clinical expertise at Altnagelvin Hospital, WHSCT and LUH and software development and data management expertise from industrial partners. In collaboration with our enterprise partners, we are developing an innovative decision support system incorporating a one-stop information platform (a cloud for storing, accessing and computing large datasets) and user friendly mobile apps to support point-of-care diagnostics. Improved diagnostic accuracy will allow redesign of care pathways into primary care (GP) from secondary care, and reduction in healthcare cost. The next generation of interdisciplinary researchers (2 PhD students and 1 Research Associate) will be trained in advanced mathematical, computational and technological skills to solve complex problems in health and biomedical sciences.
Shared Conceptual Framework
The term biomarker, in this programme of work, represents any biological, lifestyle or clinical measure that could inform clinical decision to improve risk, diagnosis or treatment and care in any of the CPM disease areas - cardiovascular, diabetes, acute kidney injury, dementia and emergency surgery. Hence, biomarkers may include presenting symptoms, proteomic, genomic, metabolomic or microbiomic measurement in body fluids or tissues, anthropological measurements and/or physiological indicators such as BP, ECG, or EEG monitoring. Each of the five research clusters are using a diversity of information that is specific to that disease area. This information is being identified and agreed by the central biomarker research team and the research cluster leads. Common biomarkers across disease clustster areas will allow the biomarker team to identify possible overlaps in disease areas. A key objective of the project is to use personalised medicine approaches to improve how patients are diagnosed, treated and cared for the in the health services.
Speaking of her current role, Coral said "I am so excited to be working on a project that includes some of the most senior clinicians in the CPM disease areas, as well as senior academics and private industry. Not only will we get a chance to work within multidisciplinary teams, we will also get the chance to work across jurisdictions. The project will work with clinicians and academics in Derry/Londonderry, Highlands and Islands in Scotland and Letterkenny in the ROI. A key objective of this project is to use personalised medicine approaches to improve how patients are diagnosed, treated and cared for in the health services. This is a great opportunity for researchers and clinicians to work together to decide key areas of improvement, study designs and the clinical utility of biomarkers we will be exploring.
Coral will also look for common biomarkers that are collected across all disease clusters, and explore possible overlap in disease.
Clinical Care Pathways
The term clinical care pathway refers to the totality of the process whereby patients are assessed, diagnostic testing is undertaken, a clinical diagnosis made, a clinical management plan formulated and delivered with appropriate monitoring and reassessment. Clinical decision making at each step of the pathway [e.g. what diagnostic tests to perform, what treatment option to choose] is very complex and must take into account diagnostic and therapeutic [related to the limitations of currently available tests and treatments], patient choice and cost effectiveness.
We are currently undertaking a literature review to describe existing best practice care pathways relevant to the disease specific research clusters [primary coronary intervention, emergency surgery, acute kidney injury, unscheduled care in diabetes, dementia]. The important quality of care outcome metrics [key performance indicators (KPIs)] for each care pathway will then be identified and defined. Using the data from the research cluster teams, clinical care pathways will be redesigned leading to optimal evidence based care pathways for patients in the five disease areas.
Point of Care Testing
Point-of-care testing [POC testing] refers to any diagnostic test performed on body fluids carried out at the patient bedside as distinct from central laboratory testing. The availability of an early test result has the potential to expedite clinical decision making and therefore contribute to improved patient outcomes. As an overarching theme, the potential for POC testing to be incorporated into redesigned care pathways in the disease specific research clusters is being investigated.
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