The Critical Care Research Group undertakes a programme of research which focuses on the identification of early patient deterioration and long-term clinical outcomes of patients who have been admitted to an Intensive Care Unit.
Led by Peter Watkinson, the group has a successful history of conducting research in and around the Critical Care environment. We are based at the Kadoorie Centre for Critical Care Research & Education. A highly multi-disciplinary team, we include clinicians, nurses, physiotherapists, biomedical engineers and statisticians.
The group is funded through a combination of project grants, individual fellowships and its ongoing involvement with the NIHR Biomedical Research Centre, Oxford (within the Technology and Digital Health Theme). Examples of recent funding include an NIHR Health Technology Assessment grant to investigate anti-coagulation during renal replacement therapy in 69,000 critically ill patients from 181 ICUs in England and Wales (Renal Replacement Anticoagulation Management, RRAM), and Wellcome funding to investigate COVID-19 disease risk in 8.3 million people.
Themes of ongoing research include: the use of routine electronic health care data to improve the timely identification of the deteriorating patient, the use of wearable and non-touch technology to improve inpatient monitoring, the complex care requirements of ICU survivors and the longer-term consequences of critical illness on mental and physical health.
The group has strong and ongoing collaborations with the Department of Engineering (Lionel Tarassenko and David Clifton groups), Centre for Statistics in Medicine, ICNARC and the universities of Southampton and Portsmouth. These collaborations bring additional expertise in the fields of artificial intelligence, advanced statistical modelling and clinical trial management.
The group enjoys the support of several NHS Trusts and their clinical teams in supporting its research activities. The Oxford University Hospitals NHS Foundation Trust, Royal Berkshire NHS Foundation Trust ICU, South Warwickshire NHS Foundation Trust and Portsmouth Hospitals NHS Trust are amongst many groups that have provided instrumental support for our group. The group hosts postgraduate projects and provides research secondment opportunities for doctors and allied health professionals. It is heavily involved in the NIHR academic foundation and academic clinical fellowship programmes, providing academic training to junior clinicians.
To see a full list of current and past research studies run by the group please click here.
A multi-centre project to design and develop an advanced electronic early warning score to better identify patients who may need treatment on the intensive care unit. The HAVEN project was funded by the Health Innovation Challenge Fund, a joint venture supported by the Wellcome Trust and the Department of Health.
The FOBS study aims to develop an evidence-based protocol for how frequently observations should be made that will be both safe and achievable across all acute NHS hospitals.
The Renal Replacement Anticoagulant Management (RRAM) study will research the advantages and disadvantages of the two anticoagulant methods for patients with a kidney injury and treated in an ICU.
Critical care Atrial Fibrillation Evaluation (CAFE) study will bring together the best evidence on which to base improved guidelines for treatment of patients who develop atrial fibrillation on an ICU.
The Short and Long-term Cardiovascular Consequences of Critical Illness (C3 Study) aims to collect data about the care of patients admitted to ICUs and link this data with NHS long-term follow-up data. Using this linked data we hope to identify the factors that increase patients’ long-term risks of heart problems or strokes and identify those patients at highest risk.
The REcovery FoLlowing Intensive CarE Treatment (REFLECT) research programme is funded by a grant awarded by the NIHR Research for Patient Benefit scheme. Data were collected from three NHS hospitals using mixed methods including case record review of 300 patients who died following discharge from ICU, and interviews with 55 patients, family members and staff about their experiences of post-ICU ward care. For each problem in care identified, stakeholder meetings were held to examine the underlying reasons and potential changes to care. A complex intervention aimed at improving post-ICU ward care will be developed based on these results.
The introduction of SEND to Oxford University Hospitals NHS Foundation Trust has paved the way for real-time and intelligent automated recognition of patients whose observations indicate they may be at increased risk of undiagnosed chronic disease, such as hypertension. SHINE is an observational diagnostic accuracy study, using bespoke computer algorithms to screen patients admitted to the Oxford hospitals for hypertension. Patients are then followed up after discharge to investigate whether their in-hospital blood pressure measurements accurately predict their blood pressure measurements at home.
Developing a cost-effective COPD self-management system that patients can use in hospital and at home (EDGE2)
EDGE2 is a cohort study involving people with Chronic Obstructive Pulmonary Disease (COPD) who are each provided with a digital home monitoring system. We are investigating the feasibility of combining data collected by this digital system with data collected during hospital admissions. The overarching aim is to develop a cost-effective self-management system that can be integrated in clinical care pathways that benefits both patients and the NHS.
Mapping Of Lower Limb skIn pErfusion (MOLLIE) is a collaborative study between the Critical Care Research Group and the Oxford Biomedical Signal Processing Group. We monitor skin perfusion changes using several types of non-contact cameras. By identifying the best methods of tracking these changes in skin blood flow, we hope to develop ways of monitoring critically ill patients using non-contact methods.
The Critical Care Research Group undertakes a number of studies, that are adopted by the NIHR local research network portfolio, and are investigating a range of common conditions in ICU patients including sepsis, coagulopathy, ARDS, and delirium. Study designs include bench-to-bedside mechanistic studies, observational studies and large, pragmatic randomised controlled trials.
Sometimes in hospital, patients are not detected as becoming unwell quickly enough. This may mean that they are less likely to survive than if the worsening of their illness had been picked up sooner. One reason for this may be that hospital staff are unable to monitor patients’ vital signs frequently enough to help them decide if a patient is becoming more unwell. Currently, for nurses to monitor patients, they are either attached to a static machine by wires at the patient’s bedside or staff have to visit the patient every few hours to manually measure blood pressure, heart rate and respiratory rate amongst other readings. New technology means we are now able to monitor patients using small devices which attach to the wrist, finger or chest. This means that nursing staff can continually obtain data whilst leaving patients free to move around without restriction. The Virtual HDU study is a phased project refining, testing and implementing a system using these devices to allow the clinical staff to use these continuous vital signs to improve patient care.
Six-month outcomes after surviving treatment for COVID-19 disease on an intensive care unit in England (OPTIC-19)
Across England, Wales and Northern Ireland, over 10,000 patients have now been treated for severe coronavirus disease 2019 (COVID-19) on an intensive care unit (ICU). Around 60% survived to leave hospital. We do not know how survivors’ severe COVID-19 infection, or the treatment they received on the ICU, will impact their long term health. Understanding what happens to these patients can help us make sure they receive suitable care from their GP and other NHS services after they leave hospital.
Predicting AF after Cardiac Surgery - the PARADISE Score. A Clinical Prediction Rule for Post-operative Atrial Fibrillation in Patients Undergoing Cardiac Surgery (PARADISE)
The PARADISE study will develop two reliable prediction models to identify which patients are at greatest risk of developing Atrial Fibrillation (AF) following heart surgery. One will predict the risk at assessment prior to surgery, and the second will predict who may develop AF after surgery. (Two models are needed as changes during surgery may alter the risk of AF).
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Critical Care Research Group
John Radcliffe Hospital
+44 (0)1865 231448