Oliver Redfern
BSc PhD MBBS
Clinical Researcher
Oliver is a post-doctoral researcher in the Critical Care Research Group (CCRG), based in the Kadoorie centre. His primary research interest is in developing effective systems to identify and optimise the management of patients who acutely deteriorate on hospital wards.
He works on the Hospital Alerting via Electronic Noticeboard (HAVEN), which uses statistical and machine learning techniques to improve the identification of deteriorating patients. He also leads the statistical modelling workstream for an NIHR-funded project to derive evidence-based protocols for vital sign monitoring in hospitals.
Prior to working in the CCRG, Oliver undertook a PhD and post-doctoral work in protein bioinformatics with Professor Christine Orengo FRS at University College London. He then trained in medicine at the University of London before completing foundation training in south London. After working in clinical outcomes research at the University of Portsmouth, he joined the CCRG in June 2018.
Key publications
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Detecting Deteriorating Patients in Hospital: Development and Validation of a Novel Scoring System
Journal article
Pimentel MAF. et al, (2021), American Journal of Respiratory and Critical Care Medicine
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Nurse staffing, nursing assistants and hospital mortality: retrospective longitudinal cohort study
Journal article
Griffiths P. et al, (2019), BMJ Quality & Safety, 28, 609 - 617
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Predicting in-hospital mortality and unanticipated admissions to the intensive care unit using routinely collected blood tests and vital signs: Development and validation of a multivariable model
Journal article
Redfern OC. et al, (2018), Resuscitation, 133, 75 - 81
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The association between nurse staffing levels and the timeliness of vital signs monitoring: a retrospective observational study in the UK
Journal article
Redfern OC. et al, (2019), BMJ Open, 9, e032157 - e032157
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A comparison of the ability of the National Early Warning Score and the National Early Warning Score 2 to identify patients at risk of in-hospital mortality: A multi-centre database study
Journal article
Pimentel MAF. et al, (2019), Resuscitation, 134, 147 - 156
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Are long nursing shifts on hospital wards associated with sickness absence? A longitudinal retrospective observational study
Journal article
Dall’Ora C. et al, (2019), Journal of Nursing Management, 27, 19 - 26
Recent publications
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Atrial fibrillation after cardiac surgery: identifying candidate predictors through a Delphi process.
Journal article
Bedford J. et al, (2024), BMJ Open, 14
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Development of a national maternity early warning score: centile based score development and Delphi informed escalation pathways
Journal article
Gerry S. et al, (2024), BMJ Medicine, 3, e000748 - e000748
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PARADISE (Statistical Analysis Plan): Predicting Atrial Fibrillation after Cardiac Surgery - the PARADISE Score. A Clinical Prediction Rule for Postoperative Atrial Fibrillation in Patients Undergoing Cardiac Surgery
Other
REDFERN O. et al, (2024)
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Safer and more efficient vital signs monitoring protocols to identify the deteriorating patients in the general hospital ward: an observational study.
Journal article
Briggs J. et al, (2024), Health Soc Care Deliv Res, 12, 1 - 143
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COVID pandemic impact on hypertension management in North-East London: an observational cohort study using electronic health records
Preprint
Rison SCG. et al, (2023)
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Inequities in hypertension management: observational cross-sectional study in North East London using electronic health records
Journal article
Rison S. et al, (2023), British Journal of General Practice, BJGP.2023.0077 - BJGP.2023.0077