Statistical inference for continuous variables and critical illness monitoring

Critical illness is defined by the evidence of acute organ failure needing monitoring and/or support, either with drugs or machines. Close monitoring inevitably generates large amounts of data from multiple sources. These data are used to make clinical decisions by the bedside. Data are generated at different frequencies e.g. vital signs such as heart rate and oxygen levels may be monitored continuously at the bedside. Blood gas analysis may be undertaken every 4-6 hours, other blood tests may be performed 12-24 hourly. Many of the variables are inter-connected and treatments may have predictable effects on some of the variables. We propose the use of Bayesian multi-level modelling and Bayesian networks using real patient data (>50000 patients from UCLH and >5000 patients from Great Ormond Street Hospital) to generate continuous estimations of intermittently sampled values and model for the missingness mechanism. These wil be applied to modelling the pH changes continuously and how haemoglobin binds to oxygen, based on data from the ventilator, vital signs, drug used and blood tests. The work will be part of the UCL CHIMERA hub (www.ucl.ac.uk/chimera) which uses a multi-disciplinary approach to enhance the understanding of human physiology using real patient data.

Research Areas:
1. Statistics and applied probability 2. Operational Research