Computer-Assisted Surveillance for Detecting Clonal Outbreaks of Nosocomial Infection

Whole-house surveillance for healthcare-associated infection is no longer the recommended practice because of the large personnel time investment required. We developed a computer-based tracking system using microbiologic data as an aid in detecting potential outbreaks of healthcare-associated infections on a hospital-wide basis. Monthly total isolates of 25 clinically significant hospital pathogens were tallied from 1991 to 1998 to form a database for future comparison. Two different algorithm tools (based on increases of organism numbers over baseline) were applied to determine alert thresholds for suspected outbreaks using this information. The first algorithm (2SD) defined an alert as two standard deviations above the mean monthly number of isolates. The second (MI) defined an alert as either a 100% increase from the baseline organism number over 2 months or a ≥50% increase (compared to baseline) during a three-consecutive-month period. These two methods were compared to standard infection control professional surveillance (ICP) for the detection of clonal outbreaks over 12 months. Overall, a total of seven clonal outbreaks were detected during the 1-year study. Using standard methods, ICP investigated nine suspected outbreaks, four of which were associated with clonal microbes. The 2SD method signaled a suspected outbreak 15 times, of which three were clonal and ICP had detected one. The MI method signaled a suspected outbreak 30 times; four of these were clonal, and ICP had detected one. The sensitivity and specificity values for ICP, 2SD, and MI for detecting clonal outbreaks were 57, 43, and 57% and 17, 83, and 67%, respectively. Statistical methods applied to clinical microbiology laboratory information system data efficiently supplement infection control efforts for outbreak detection.