Background
1997 – Formation of BrainIT
- Formed around the time of the Williamsburg ICP Meeting
- Need for standardised data collection and shared databases on both compliance and secondary insults.
- Group interested in brain monitoring technology, specifically the Spiegelberg compliance monitor.
- Per Enblad, Pelle Nilsson, Tim Howells, Giuseppe Citerio, Juan Sahuquillo, Iain Chambers, Karl Kienning, Charlie Contant and Ian Piper
2001 – 2002: BrainIT -1 - FP5 QLRI-2000-00454 Infrastuctures Programme - Networking Meetings to Define Core Dataset.
- One year project expanded the group from the 5 members interested in compliance measurement to 22 centres capable of collecting research data.
- Defined a core-dataset standard for the collection of high resolution intensive care patient data.
- Conducted a paper based pilot data collection exercise to determine the feasibility of collecting the core dataset in all centres.
- Technical sub-committee
- Designed the interface protocols required in each centre to collect the BrainIT data.
- Discussed and designed a flexible database format to hold the BrainIT group data.
2003 – 2006: BrainIT-2 - FP5 QLGT-2002-00160 Infrastuctures Programme
- Developed new software methods including several new tools for collection of the BrainIT core dataset.
- Acquired patient data from 22 Neuro intensive care centres from 11 EU countries
- Successfully completed a prospective data collection of data from 262 patients using these new tools.
2008-2012: AvertIT - FP7 IST-2007-217049 ICT Programme - AVERT-IT- A Bayesian Neural Network Predicting Hypotension
- Aim to develop a mechanism, for use within intensive and high-dependency care units, to monitor and predict the likelihood of arterial hypotension (low blood pressure).
- Determined the weighted association between multiple patient parameters and subsequent arterial hypotension.
- Association used to define the novel Bayesian neural network (BNN), which was trained against the existing BrainIT dataset
- Undertook an observational study on 60 patients to test the BANN in a live clinical environment.
- This work was successfully completed and the calculated sensitivity and specificity from using the BANN system in a live clinical environment were found to be 40.09% and 92.57% respectively.
2015 – Chief Scientists Office (CSO) – Prof. Chris Williams, University of Edinburgh
- Found that approx. 30% of potential arterial hypotension events are not quantifiable due to either missing data or artifact from blood sampling, patient handling or other clinical interventions.
- Investigated approaches to automatically detecting and cleaning blood pressure artifact from the raw data.
- Developed two models for detection of artifact in blood pressure time-series data:
- Factorial Switching Linear Dynamical Systems (FSLDS) model
- Discrimitive Switching Linear Dynamical Systems (DSLDS) model.