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Peter Watkinson, Nuffield Department of Clinical Neurosciences

Stephen Gerry, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences 


One in every 10,000 women giving birth in the UK dies during or up to six weeks after pregnancy. For every woman that dies, 100 will have a severe morbidity or ‘near-miss’, meaning that over 7,000 UK women have severe morbidity as a consequence of pregnancy annually, often with lifelong sequelae. Adverse maternal outcomes investigations consistently show the severity of women’s illness is not recognised. Earlier recognition of deterioration would allow early intervention to prevent women progression to severe morbidity or mortality, but existing early warning scores are based on expert opinion only and have not been evaluated. With the advent of electronic recording of vital signs (heart rate, respiratory rate, blood pressure and temperature) linked to electronic patient records, there is the opportunity to develop an enhanced Modified Obstetric Early Warning Score by incorporating additional patient information, using standard statistical methods or machine learning. Incorporating the variation in vital signs through maternity along with specific factors known to predict poor individual outcomes should substantially improve early deterioration prediction. The aim of this DPhil will be to develop and test this enhanced Modified Obstetric Early Warning Score.


This project will involve detailed analysis and interpretation of electronic hospital record data using both standard statistical methods and machine learning, as well as literature review. The student will work within a multi-disciplinary team and will gain research experience in epidemiological and statistical methodology, study design, data collection and analysis. Regular research meetings and workshops will be held which the candidate will be expected to attend and to present research findings.


The project will be based at the National Perinatal Epidemiology Unit (NPEU), Nuffield Department of Population Health, working jointly with the Critical Care Research Group based at the Kadoorie Centre for Critical Care Research & Education, Nuffield Department of Clinical Neurosciences. The project will also involve working closely with A/Professor David Clifton at the Institute of Biomedical Engineering with the possibility of associated placement work. The project will provide a range of training opportunities in study design, primary data collection, data linkage, machine learning and statistical analysis and interpretation of large datasets.


Candidates should have a background in a biomedical or mathematical discipline and postgraduate training in epidemiology, statistics or a relevant clinical discipline. Candidates should have an interest in advancing their quantitative skills as well as an interest in maternal and child health, critical care or public health.