Computer Algorithms To Detect Bloodstream Infections

• Main Authors: William E. Trick, Brandon M. Zagorski, Jerome I. Tokars, Michael O. Vernon, Sharon F. Welbel, Mary F. Wisniewski, Chesley Richards, Robert A. Weinstein
• Format: Article
• Language: English
• Published: Centers for Disease Control and Prevention 2004-09-01
• Series: Emerging Infectious Diseases
• Subjects: surveillance; bloodstream infection; information system; computer data processing; algorithms; infection control
• Online Access: https://wwwnc.cdc.gov/eid/article/10/9/03-0978_article

We compared manual and computer-assisted bloodstream infection surveillance for adult inpatients at two hospitals. We identified hospital-acquired, primary, central-venous catheter (CVC)-associated bloodstream infections by using five methods: retrospective, manual record review by investigators; prospective, manual review by infection control professionals; positive blood culture plus manual CVC determination; computer algorithms; and computer algorithms and manual CVC determination. We calculated sensitivity, specificity, predictive values, plus the kappa statistic (κ) between investigator review and other methods, and we correlated infection rates for seven units. The κ value was 0.37 for infection control review, 0.48 for positive blood culture plus manual CVC determination, 0.49 for computer algorithm, and 0.73 for computer algorithm plus manual CVC determination. Unit-specific infection rates, per 1,000 patient days, were 1.0–12.5 by investigator review and 1.4–10.2 by computer algorithm (correlation r = 0.91, p = 0.004). Automated bloodstream infection surveillance with electronic data is an accurate alternative to surveillance with manually collected data.