The clinical outcome modelling research carried out in Portsmouth is led by Professor David Prytherch and Professor Jim Briggs. Our collaborators include Portsmouth Hospitals NHS Trust (PHT), University of Oxford, Oxford University Hospitals NHS Trust, University of Southampton, Bournemouth University and The Learning Clinic. Our approach is extremely inter-disciplinary, but embedded in all we do are the fundamental principles that information must be acquired by reliable means and reasoned about rigorously; all applied in a clinical context.
We collect and use clinical data to model adverse patient outcome. The models enable clinicians to predict which patients are at risk of deterioration, and medically intervene. Our research has built on work done in the late 1990s and up to 2003 to develop models (P-POSSUM) of outcomes in surgery (Prytherch, Whiteley, Higgins, Weaver, et al, 1998). P-POSSUM was a success and has been widely adopted, but is only applicable to surgical cases. This led us to investigate ways to model outcomes in general medicine cases, using pathology data. We have shown that biochemistry and haematology outcome models (BHOM) can be used to identify patients at risk of mortality with very high discrimination (Prytherch, Sirl, Schmidt, Featherstone, et al, 2005; Prytherch, Briggs, Weaver, Schmidt and Smith, 2005).
Other monitoring and surveillance systems (e.g., Dr Foster, CHKS and HES) require coded administrative data only available after discharge. Our techniques add clinical context to these, and have obvious uses in clinical governance and clinical performance management as well as direct patient care. Our approach only uses data routinely collected and available immediately after a patient's admission to hospital.
We know that serious physiological abnormalities frequently precede primary events (defined as in-hospital deaths, cardiac arrests, and unanticipated intensive care unit admissions) (Kause, Smith, Prytherch, Parr, et al, 2004). The P-POSSUM / BHOM work led to our collaboration with The Learning Clinic Ltd (TLC). In return, TLC provided a means to collect vital signs data quickly and accurately in an electronic format. As a result we have access to probably the biggest database of vital signs data anywhere in the world.
Using that and related data we have shown that:
Our EWS models can be applied to any patient under clinical care, but are increasingly used to allow nurses to determine which of their patients are deteriorating and when to summon assistance (e.g. a doctor), without causing too many false alarms (which would overburden hospital resources).
We are currently working on two externally funded projects: