Publications Aims About Projects Steering Committee

Avert-IT is an EU-funded project to develop a mechanism, for use within intensive and high-dependency care units, which will have the ability to monitor and predict the likelihood of arterial hypotension (low blood pressure) adverse events. The full project title is "Advanced Arterial Hypotension Adverse Event prediction through a Novel Bayesian Neural Network" and has been running since January 2008.

Background
Intensive Care patients can experience adverse events associated with sudden episodes of low blood pressure. These adverse events may impact all of the main organs resulting in longer lengths of stay, increased care costs and reducing quality of outcomes. Existing technologies enable clinicians to know when these events have occurred and treat the effects. Medical therapies and management for treating adverse events such as low blood pressure exist but clinicians don’t have a reliable way to predict the occurrence, so there’s no opportunity for early intervention.

Research indicates average lengths of stay in intensive care could be significantly reduced if these adverse events can be avoided through prediction and earlier intervention. By reducing the cost of intensive care intensive care services (typically > 1000 Euro's per day) could lead potentially to savings across the EU in the billions of euros, annually.

As arterial hypotension is a common form of adverse event, a model for predicting these offers potential for improving outcomes across a wide range of conditions and or illnesses.

Objectives
The main scientific objective of the project is the determination of the weighted association between multiple patient parameters and subsequent arterial hypotension. The association will then be used to define the novel Bayesian neural network, which will be trained against the existing BrainIT dataset (collected from 22 centres across Europe), before undertaking a clinical trial to demonstrate the Avert-IT project concept.

The main technological objective will be the development of an IT-based decision support system ("HypoPredict") appropriate for deployment within intensive and high dependency care units. The system will be capable of:

• Automatically and continually monitoring at least four in-vivo patient parameters (eg: ECG, arterial blood pressure, Oxygen saturation and core temperature), together with open interfaces providing input of key demographic data (age, gender etc.) and periodic data (clinical pathology results etc.) related to the patient.
  
  
• Outputting a continuous Hypotension Prediction Index (HPi) which will be updated on a minute by minute basis upon any change detected in the patient parameter input set.

Current Status
Having successfully trained the BANN on existing cleaned data acquired from the BrainIT group (www.brainit.org), the final year of the project was focused on collecting data from 30 patients in an observational study to test that the AVERT-IT technology meets the clinician’s minimum requirements for sensitivity (>30%) and false positive rate (FPR) (< 10%) for prediction of arterial hypotension (low blood pressure).  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. These encouraging results have allowed us to proceed to a Phase 2 study where we aim to collect a further 46 patients for definitive assessment of the system and model performance.

 

 

In the figure above - the ROC curve from the research phase is shown by the black line. The point estimates for mean sensitivity and false positive rate at the threshold values 0.1 to 0.9 are shown on the solid red curve. The prospective clinical study results (red line) are very close to the research results particularly in the range of interest with a threshold setting of between 0.3 and 0.4 giving a specificity range of 46.03% and 40.09% and a specificity range of 87.13% and 92.57% respectively.

The BANN hypopredict technology is showing promise. Our current two stage sequential clinical trial under way in six BrainIT centres (Glasgow, Barcelona, Heidelberg, Monza, Uppsala and Vilnius) which will be complete by the middle of 2011 will provide definitive results on the sensitivity and specificity achieved with this technology when used in a live intensive care clinical environment. We are optimistic that this research will provide the basis for future research assessing the clinical utility of this type of medical decision support upon reducing the length of stay for patients managed in the intensive care environment.