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BJA Advance Access published online on May 17, 2006
British Journal of Anaesthesia, doi:10.1093/bja/ael113
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© The Board of Management and Trustees of the British Journal of Anaesthesia 2006. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org

Postgraduate Issue Review Article

Integrated monitoring and analysis for early warning of patient deterioration

L. Tarassenko 1 *, A. Hann 1, and D. Young 2

1 Department of Engineering Science, Parks Road, University of Oxford, Oxford OX1 3PJ, UK
2 Nuffield Department of Anaesthetics, University of Oxford, Adult Intensive Care Unit, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK

* To whom correspondence should be addressed.
L. Tarassenko, E-mail: lionel.tarassenko{at}eng.ox.ac.uk


  Abstract

Recently there has been an upsurge of interest in strategies for detecting at-risk patients in order to trigger the timely intervention of a Medical Emergency Team (MET), also known as a Rapid Response Team (RRT). We review a real-time automated system, BioSign, which tracks patient status by combining information from vital signs monitored non-invasively on the general ward. BioSign fuses the vital signs in order to produce a single-parameter representation of patient status, the Patient Status Index. The data fusion method adopted in BioSign is a probabilistic model of normality in five dimensions, previously learnt from the vital sign data acquired from a representative sample of patients. BioSign alerts occur either when a single vital sign deviates by close to ±3 standard deviations from its normal value or when two or more vital signs depart from normality, but by a smaller amount. In a trial with high-risk elective/emergency surgery or medical patients, BioSign alerts were generated, on average, every 8 hours; 95% of these were classified as ‘True’ by clinical experts. Retrospective analysis has also shown that the data fusion algorithm in BioSign is capable of detecting critical events in advance of single-channel alerts.

Keywords: critical care; data fusion; early warning; signal analysis.
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