Literature DB >> 16707529

Integrated monitoring and analysis for early warning of patient deterioration.

L Tarassenko1, A Hann, D Young.   

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.

Entities:  

Mesh:

Year:  2006        PMID: 16707529     DOI: 10.1093/bja/ael113

Source DB:  PubMed          Journal:  Br J Anaesth        ISSN: 0007-0912            Impact factor:   9.166


  40 in total

Review 1.  Health technology assessment review: remote monitoring of vital signs--current status and future challenges.

Authors:  Vishal Nangalia; David R Prytherch; Gary B Smith
Journal:  Crit Care       Date:  2010-09-24       Impact factor: 9.097

2.  Defining the incidence of cardiorespiratory instability in patients in step-down units using an electronic integrated monitoring system.

Authors:  Marilyn Hravnak; Leslie Edwards; Amy Clontz; Cynthia Valenta; Michael A Devita; Michael R Pinsky
Journal:  Arch Intern Med       Date:  2008-06-23

Review 3.  Systematic review of paediatric alert criteria for identifying hospitalised children at risk of critical deterioration.

Authors:  Susan M Chapman; Michael P W Grocott; Linda S Franck
Journal:  Intensive Care Med       Date:  2009-11-26       Impact factor: 17.440

4.  Gleaning knowledge from data in the intensive care unit.

Authors:  Michael R Pinsky; Artur Dubrawski
Journal:  Am J Respir Crit Care Med       Date:  2014-09-15       Impact factor: 21.405

Review 5.  Monitoring cardiorespiratory instability: Current approaches and implications for nursing practice.

Authors:  Eliezer Bose; Leslie Hoffman; Marilyn Hravnak
Journal:  Intensive Crit Care Nurs       Date:  2016-02-28       Impact factor: 3.072

6.  Evaluating performance of early warning indices to predict physiological instabilities.

Authors:  Christopher G Scully; Chathuri Daluwatte
Journal:  J Biomed Inform       Date:  2017-09-20       Impact factor: 6.317

Review 7.  A review of recent advances in data analytics for post-operative patient deterioration detection.

Authors:  Clemence Petit; Rick Bezemer; Louis Atallah
Journal:  J Clin Monit Comput       Date:  2017-08-21       Impact factor: 2.502

8.  Clinician-Driven Design of VitalPAD-An Intelligent Monitoring and Communication Device to Improve Patient Safety in the Intensive Care Unit.

Authors:  Luisa Flohr; Shaylene Beaudry; K Taneille Johnson; Nicholas West; Catherine M Burns; J Mark Ansermino; Guy A Dumont; David Wensley; Peter Skippen; Matthias Gorges
Journal:  IEEE J Transl Eng Health Med       Date:  2018-03-05       Impact factor: 3.316

9.  Utility of commonly captured data from an EHR to identify hospitalized patients at risk for clinical deterioration.

Authors:  Abel Kho; David Rotz; Kinan Alrahi; Wendy Cárdenas; Kristin Ramsey; David Liebovitz; Gary Noskin; Chuck Watts
Journal:  AMIA Annu Symp Proc       Date:  2007-10-11

10.  Robust parameter extraction for decision support using multimodal intensive care data.

Authors:  G D Clifford; W J Long; G B Moody; P Szolovits
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2009-01-28       Impact factor: 4.226

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.