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

Journal: American journal of infection control

PMID: 29661634

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.

Authors

Cole Beeler

Indiana University School of Medicine, Indianapolis, IN. Electronic address: cbeeler@iupui.edu.
Lana Dbeibo

Indiana University School of Medicine, Indianapolis, IN.
Kristen Kelley

Infection Prevention for IU Health, Indianapolis, IN.
Levi Thatcher

Health Catalyst, Salt Lake City, UT.
Douglas Webb

Infection Prevention for IU Health, Indianapolis, IN.
Amadou Bah

Infection Prevention for IU Health, Indianapolis, IN.
Patrick Monahan

Department of Biostatistics, Indiana University, Indianapolis, IN.
Nicole R Fowler

Department of Medicine, Indiana University, Indianapolis, IN.
Spencer Nicol

Health Catalyst, Salt Lake City, UT.
Alisa Judy-Malcolm

IU Health, Indianapolis, IN.
Jose Azar

Indiana University School of Medicine, IU Health, Indianapolis, IN.

Keywords

Academic Medical Centers Adolescent Adult Aged Aged, 80 and over Bacteremia Case-Control Studies Catheter-Related Infections Catheterization, Central Venous Child Child, Preschool Epidemiologic Methods Female Humans Infant Infant, Newborn Machine Learning Male Middle Aged Retrospective Studies Risk Assessment Young Adult

External Resources

View on PubMed Access via DOI PubMed (29661634)

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

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals