Assessing patient risk of central line-associated bacteremia via machine learning

Cole Beeler, Lana Dbeibo, Kristen Kelley, Levi Thatcher, Douglas Webb, Amadou Bah, Patrick Monahan, Nicole R. Fowler, Spencer Nicol, Alisa Judy-Malcolm, Jose Azar

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

Background: Central line-associated bloodstream infections (CLABSIs) contribute to increased morbidity, length of hospital stay, and cost. Despite progress in understanding the risk factors, there remains a need to accurately predict the risk of CLABSIs and, in real time, prevent them from occurring. Methods: A predictive model was developed using retrospective data from a large academic healthcare system. Models were developed with machine learning via construction of random forests using validated input variables. Results: Fifteen variables accounted for the most significant effect on CLABSI prediction based on a retrospective study of 70,218 unique patient encounters between January 1, 2013, and May 31, 2016. The area under the receiver operating characteristic curve for the best-performing model was 0.82 in production. Discussion: This model has multiple applications for resource allocation for CLABSI prevention, including serving as a tool to target patients at highest risk for potentially cost-effective but otherwise time-limited interventions. Conclusions: Machine learning can be used to develop accurate models to predict the risk of CLABSI in real time prior to the development of infection.

Original languageEnglish (US)
Pages (from-to)986-991
Number of pages6
JournalAmerican Journal of Infection Control
Volume46
Issue number9
DOIs
StatePublished - Sep 2018

Keywords

  • Central line-associated bloodstream infection (CLABSI)
  • Infection control
  • Infection prediction
  • Machine learning
  • Nosocomial infection
  • Quality improvement

ASJC Scopus subject areas

  • Epidemiology
  • Health Policy
  • Public Health, Environmental and Occupational Health
  • Infectious Diseases

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