Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Inpatient mobility to predict hospital-onset Clostridium difficile: a network approach

Kristen Bush, Hugo Barbosa, Samir Farooq, Samuel J. Weisenthal, Melissa Trayhan, Robert J. White, Gourab Ghoshal, View ORCID ProfileMartin S. Zand
doi: https://doi.org/10.1101/404160
Kristen Bush
aRochester Center for Health Informatics at the University of Rochester Medical Center, 265 Crittenden Blvd - 1.207, Rochester, NY 14642, USA
bClinical and Translational Science Institute, University of Rochester Medical Center, 26 Crittenden Blvd, Rochester, NY, 14642, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hugo Barbosa
cDepartment of Physics and Astronomy, University of Rochester, 500 Wilson Blvd, Rochester, NY 14627, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Samir Farooq
aRochester Center for Health Informatics at the University of Rochester Medical Center, 265 Crittenden Blvd - 1.207, Rochester, NY 14642, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Samuel J. Weisenthal
aRochester Center for Health Informatics at the University of Rochester Medical Center, 265 Crittenden Blvd - 1.207, Rochester, NY 14642, USA
bClinical and Translational Science Institute, University of Rochester Medical Center, 26 Crittenden Blvd, Rochester, NY, 14642, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Melissa Trayhan
aRochester Center for Health Informatics at the University of Rochester Medical Center, 265 Crittenden Blvd - 1.207, Rochester, NY 14642, USA
bClinical and Translational Science Institute, University of Rochester Medical Center, 26 Crittenden Blvd, Rochester, NY, 14642, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Robert J. White
aRochester Center for Health Informatics at the University of Rochester Medical Center, 265 Crittenden Blvd - 1.207, Rochester, NY 14642, USA
bClinical and Translational Science Institute, University of Rochester Medical Center, 26 Crittenden Blvd, Rochester, NY, 14642, USA
dDepartment of Medicine, Division of Nephrology, University of Rochester Medical Center, 601 Elmwood Avenue, Rochester, NY 14642, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Gourab Ghoshal
cDepartment of Physics and Astronomy, University of Rochester, 500 Wilson Blvd, Rochester, NY 14627, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Martin S. Zand
aRochester Center for Health Informatics at the University of Rochester Medical Center, 265 Crittenden Blvd - 1.207, Rochester, NY 14642, USA
bClinical and Translational Science Institute, University of Rochester Medical Center, 26 Crittenden Blvd, Rochester, NY, 14642, USA
dDepartment of Medicine, Division of Nephrology, University of Rochester Medical Center, 601 Elmwood Avenue, Rochester, NY 14642, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Martin S. Zand
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

With hospital-onset Clostridium difficile Infection (CDI) still a common occurrence in the U.S., this paper examines the relationship between unit-wide CDI susceptibility and inpatient mobility and creates a predictive measure of CDI called “Contagion Centrality”. A mobility network was constructed using two years of patient electronic health record (EHR) data within a 739-bed hospital (Jan. 2013 - Dec. 2014; n=72,636 admissions). Network centrality measures were calculated for each hospital unit (node) providing clinical context for each in terms of patient transfers between units (edges). Daily unit-wide CDI susceptibility scores were calculated using logistic regression and compared to network centrality measures to determine the relationship between unit CDI susceptibility and patient mobility. Closeness centrality was a statistically significant measure associated with unit susceptibility (p-value < 0.05), highlighting the importance of incoming patient mobility in CDI prevention at the unit-level. Contagion Centrality (CC) was calculated using incoming inpatient transfer rates, unit-wide susceptibility of CDI, and current hospital CDI infections. This measure is statistically significant (p-value <0.05) with our outcome of hospital-onset CDI cases, and captures the additional opportunities for transmission associated with inpatient transfers. We have used this analysis to create an easily interpretable and informative clinical tool showing this relationship and risk of hospital-onset CDI in real-time. Quantifying and visualizing the combination of inpatient transfers, unit-wide risk, and current infections help identify hospital units at risk of developing a CDI outbreak, and thus provide clinicians and infection prevention staff with advanced warning and specific location data to concentrate prevention efforts.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
Back to top
PreviousNext
Posted September 20, 2018.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Inpatient mobility to predict hospital-onset Clostridium difficile: a network approach
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Inpatient mobility to predict hospital-onset Clostridium difficile: a network approach
Kristen Bush, Hugo Barbosa, Samir Farooq, Samuel J. Weisenthal, Melissa Trayhan, Robert J. White, Gourab Ghoshal, Martin S. Zand
bioRxiv 404160; doi: https://doi.org/10.1101/404160
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Inpatient mobility to predict hospital-onset Clostridium difficile: a network approach
Kristen Bush, Hugo Barbosa, Samir Farooq, Samuel J. Weisenthal, Melissa Trayhan, Robert J. White, Gourab Ghoshal, Martin S. Zand
bioRxiv 404160; doi: https://doi.org/10.1101/404160

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Epidemiology
Subject Areas
All Articles
  • Animal Behavior and Cognition (4109)
  • Biochemistry (8813)
  • Bioengineering (6517)
  • Bioinformatics (23456)
  • Biophysics (11788)
  • Cancer Biology (9205)
  • Cell Biology (13318)
  • Clinical Trials (138)
  • Developmental Biology (7433)
  • Ecology (11407)
  • Epidemiology (2066)
  • Evolutionary Biology (15145)
  • Genetics (10433)
  • Genomics (14041)
  • Immunology (9169)
  • Microbiology (22152)
  • Molecular Biology (8808)
  • Neuroscience (47558)
  • Paleontology (350)
  • Pathology (1428)
  • Pharmacology and Toxicology (2491)
  • Physiology (3730)
  • Plant Biology (8079)
  • Scientific Communication and Education (1437)
  • Synthetic Biology (2220)
  • Systems Biology (6037)
  • Zoology (1252)