Computer-Assisted Surveillance for Detecting Clonal Outbreaks of Nosocomial Infection

Abstract

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 hospitalwide 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.

Automated DNA Sequence-Based Early Warning System for the Detection of Methicillin-Resistant Staphylococcus aureus Outbreaks

“…The evaluation of an early warning system, however, is difficult because there is no accepted “gold standard” and it is likely that no system will be completely reliable [31]. Therefore, we chose to combine epidemiological and molecular typing data with statistical analysis to provide an objective measure of performance between the varied approaches.…”

High diversity of airborne fungi in the hospital environment as revealed by meta-sequencing-based microbiome analysis

“…Thus, the surveillance of hospital infections with this method is insufficient for a broad coverage of airborne pathogens and does not reflect the real hospital environment. Recent technological advances have driven the development of large-scale tests performed with a single collection that complement and, in some cases, replace the traditional methods131415 of detection. In particular, the development of microbiome sequence analysis not only produces higher sensitivities and shorter turnaround times than the traditional methods but may also identify suspended pathogens that are difficult to culture, such as fungi161718.…”

Extensive Hospital-Wide Spread of a Multidrug-Resistant Enterobacter cloacae Clone, with Late Detection Due to a Variable Antibiogram and Frequent Patient Transfer

“…Computerized tools for surveillance might improve our practice (2,5,12,13). Retrospective application of the computer-assisted surveillance system using the algorithms and criteria of Hacek et al would have produced an alert 6 months before the outbreak was actually detected.…”

“…Recently, Hacek et al developed two algorithm tools (based on increases of organism number over baseline) to analyze laboratory data for possible clonal dissemination (5). The first algorithm (2SD) defined an alert as two standard deviations (SDs) above the mean monthly number of isolates.…”

“…Determinants for unnoticed spread were investigated retrospectively. A recently described computer-assisted surveillance method and an "inhouse"-developed method were retrospectively applied to the database of the microbiology laboratory to determine whether this unnoticed outbreak could have been identified at an earlier stage (5).…”