Accurately predicting risk of central line-associated bloodstream infection

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This study sought to identify if machine learning could be utilized to accurately predict which patients with a central line will develop a CLABSI” Schoonover et al (2017).

BACKGROUND:

Approximately 41,000 patients in the hospital and 37,000 patients receiving hemodialysis that have central lines end up with a central line-associated blood stream infection (CLABSI). Decreases in the incidence of CLABSI nationwide are slowing. Accurately predicting risk for CLABSI could provide the ability to intervene more quickly, decreasing the incidence of CLABSI. This study sought to identify if machine learning could be utilized to accurately predict which patients with a central line will develop a CLABSI.

Reference:

Schoonover, H., Kelley, K. and Thatcher, L. (2017) Accurately Predicting Risk of Central Line-Associated Bloodstream Infection—Application of Machine Learning to Predict and Minimize Incidence of Central Line-Associated Bloodstream Infection. American Journal of Infection Control. 45(6), Supplement, p.S46.

DOI: http://dx.doi.org/10.1016/j.ajic.2017.04.069

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