Improving the metrics for assessment of injury burden to improve patient stratification

Aim: To develop tissue and physiological based scoring systems using a developed semantic infrastructure to allow the stratification of trauma patients and to facilitate the prediction of clinical outcomes into the re-enablement phase.

Background: Injury severity scoring systems were developed to predict mortality in blunt trauma and then refined to predict all major trauma.  Improved trauma care has led to these measures failing to predict even mortality now.  Both anatomical and physiological scoring systems show limited correlations with clinical outcomes such as ventilator- free days, LOS, nitrogen excretion and muscle loss.  In another study run by the Centre injury severity scores such as ISS, TRISS or SOFA were unable to accurately predict outcomes such as muscle loss or stratify the injured patient cohort.  No data exist on how best to stratify re-enablement programmes, either the timing or type of functional activity to be used.   Early accurate stratification of major trauma will not only inform treating clinicians but will also help remove confounders in clinical trials that are hindering the discovery of effective new treatments in early phase trials.

Method: Employ existing longitudinal, day one to 12 months, physiological, metabolomics and biochemical data from ongoing SRMRC studies and develop a sematic representation model of injury that enables the improved stratification of patients. This model will then be used to predict complex organ failure, extensive ventilator support, sepsis, long term recovery and mortality.

Lead researchers