• DocumentCode
    710769
  • Title

    Modeling nosocomial transmission of carbapenem-resistant bacteria

  • Author

    Stern, Jeremy ; Hewitt, Sam ; Guilfoyle, Michael ; Mishra, Chetan ; Mathers, Amy ; Lobo, Jennifer ; Brown, Donald ; Barnes, Laura

  • Author_Institution
    Univ. of Virginia, Charlottesville, VA, USA
  • fYear
    2015
  • fDate
    24-24 April 2015
  • Firstpage
    176
  • Lastpage
    181
  • Abstract
    Patient acquisition of carbapenem resistant bacteria in hospitals is a serious problem that leads to adverse outcomes for infected patients. The most common carbapenem resistance mechanism in US hospitals is a mobile gene called Klebsiella pneumoniae carbapenemase (KPC), which can move between bacterial species. Previous research has demonstrated that patient-to-patient transmission cannot fully account for all cases of KPC-positive bacterial transmission in hospital settings. The study in this paper considers environmental reservoirs as an additional source of transmission. We conduct this analysis with logistic regression and random forest models of nosocomial transmission that includes both environmental factors and clinical data. Results show that infection rates are not uniformly distributed throughout the hospital and that differences in room layout play a role. However, proximity to KPC-positive Enterobacteriaceae environmental reservoirs does not significantly correlate to patient acquisition.
  • Keywords
    cellular biophysics; genetics; hospitals; learning (artificial intelligence); medical administrative data processing; microorganisms; regression analysis; KPC-positive bacterial transmission; KPC-positive enterobacteriaceae environmental reservoirs; Klebsiella pneumoniae carbapenemase; US hospitals; carbapenem-resistant bacteria; clinical data; environmental factors; hospital settings; infected patients; infection rates; logistic regression; mobile gene; nosocomial transmission modeling; patient acquisition; patient-to-patient transmission; random forest models; room layout; Data models; Hospitals; Immune system; Logistics; Microorganisms; Predictive models; Reservoirs; Klebsiella pneumoniae; carbapenemase; logistic regression; nosocomial infection; random forest;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems and Information Engineering Design Symposium (SIEDS), 2015
  • Conference_Location
    Charlottesville, VA
  • Print_ISBN
    978-1-4799-1831-7
  • Type

    conf

  • DOI
    10.1109/SIEDS.2015.7116969
  • Filename
    7116969