Identification of therapeutically-relevant patient subgroups from clinical and biological data

As a disease, sepsis is important for a multitude of reasons. Severe sepsis is responsible for approximately 50% of admission in the Intensive Care Unit (ICU) and significant annual hospitalisation costs ($14 billion/year) (Cawcutt and Peters, 2014). In addition, sepsis can lead to septic shock, which is sepsis with hypotension or hyperlactatemia, a condition which can be fatal (24-32% mortality rate) (Daviaud et al, 2015). Even though mortality rates have decreased over the past years due to improved clinical care (Daviaud et al, 2015), there is still much room for improvement in terms of accurate and timely diagnosis.
The aim of the project is to develop a method by which clinicians might identify patients with greater predisposition to develop sepsis, or septic shock, as a result of infection by specific invading pathogens. This test will enable clinicians to identify a patient as belonging to a risk group for a particular pathogen, thus allowing them to administer the proper course of antibiotics to treat this pathogen and save both time (since the most effective treatment will be given as soon as possible) and resources (as initial use of broad spectrum antibiotics will be limited, reducing costs).
This will be done by means of bioinformatic analysis of patient's expression data using graph theory. The associations between patients will be discerned, allowing for their sub-categorisation into groups of different susceptibilities. The python programming language will be used to conduct this analysis and the work on this area will be based on previous research conducted by the Baillie lab.