Using a Bayesian network to model colonisation with Vancomycin Resistant Enterococcus (VRE)

Rajmokan, Mohana, Morton, Anthony, Mengersen, Kerrie, Hall, Lisa, & Waterhouse, M. (2011) Using a Bayesian network to model colonisation with Vancomycin Resistant Enterococcus (VRE). In Third Annual Conference of the Australasian Bayesian Network Modelling Society (ABNMS2011), 21-24 November 2011, Queensland University of Technology, Brisbane, QLD.

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Objective: Effective management of multi-resistant organisms is an important issue for hospitals both in Australia and overseas. This study investigates the utility of using Bayesian Network (BN) analysis to examine relationships between risk factors and colonization with Vancomycin Resistant Enterococcus (VRE). Design: Bayesian Network Analysis was performed using infection control data collected over a period of 36 months (2008-2010). Setting: Princess Alexandra Hospital (PAH), Brisbane. Outcome of interest: Number of new VRE Isolates Methods: A BN is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG). BN enables multiple interacting agents to be studied simultaneously. The initial BN model was constructed based on the infectious disease physician‟s expert knowledge and current literature. Continuous variables were dichotomised by using third quartile values of year 2008 data. BN was used to examine the probabilistic relationships between VRE isolates and risk factors; and to establish which factors were associated with an increased probability of a high number of VRE isolates. Software: Netica (version 4.16). Results: Preliminary analysis revealed that VRE transmission and VRE prevalence were the most influential factors in predicting a high number of VRE isolates. Interestingly, several factors (hand hygiene and cleaning) known through literature to be associated with VRE prevalence, did not appear to be as influential as expected in this BN model. Conclusions: This preliminary work has shown that Bayesian Network Analysis is a useful tool in examining clinical infection prevention issues, where there is often a web of factors that influence outcomes. This BN model can be restructured easily enabling various combinations of agents to be studied.

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ID Code: 60811
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400)
Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > CLINICAL SCIENCES (110300) > Infectious Diseases (110309)
Divisions: Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Current > Schools > School of Public Health & Social Work
Copyright Owner: Copyright 2011 please consult the authors
Deposited On: 16 Jun 2013 23:08
Last Modified: 13 Jul 2013 07:26

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