TY - JOUR AU - Trick, William E. AU - Zagorski, Brandon M. AU - Tokars, Jerome I. AU - Vernon, Michael O. AU - Welbel, Sharon F. AU - Wisniewski, Mary F. AU - Richards, Chesley AU - Weinstein, Robert A. T1 - Computer Algorithms To Detect Bloodstream Infections T2 - Emerging Infectious Disease journal PY - 2004 VL - 10 IS - 9 SP - 1612 SN - 1080-6059 AB - 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. KW - surveillance KW - bloodstream infection KW - information system KW - computer data processing KW - algorithms KW - infection control KW - central-venous catheter KW - research KW - United States DO - 10.3201/eid1009.030978 UR - https://wwwnc.cdc.gov/eid/article/10/9/03-0978_article ER - End of Reference