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 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.

[1]  S. O'Brien,et al.  Do CuSums have a role in routine communicable disease surveillance? , 1997, Public health.

[2]  Gooch Jj,et al.  Computerized system analyzes epidemiological data. , 1976 .

[3]  C L Bartlett,et al.  An evaluation of surveillance methods for detecting infections in hospital inpatients. , 1993, The Journal of hospital infection.

[4]  L. Peterson,et al.  Molecular Epidemiology of Vancomycin-Resistant Enterococci: A 2-Year Perspective , 1999, Infection Control & Hospital Epidemiology.

[5]  W J Martone,et al.  National nosocomial infections surveillance system (NNIS): description of surveillance methods. , 1991, American journal of infection control.

[6]  R M Gardner,et al.  Computerized identification of patients at high risk for hospital-acquired infection. , 1992, American journal of infection control.

[7]  S. Lemmen,et al.  Assessment of a selective surveillance method for detecting nosocomial infections in patients in the intensive care department. , 2003, American journal of infection control.

[8]  A. Sprague,et al.  A Data Mining System for Infection Control Surveillance , 2000, Methods of Information in Medicine.

[9]  D Birnbaum.  Analysis of hospital infection surveillance data. , 1984, Infection control : IC.

[10]  F. Saulnier,et al.  Computerized colonization-surveillance based on antimicrobial susceptibility patterns , 1987, European Journal of Epidemiology.

[11]  W. Trick,et al.  The use of economic modeling to determine the hospital costs associated with nosocomial infections. , 2003, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[12]  L. Peterson,et al.  Hunting Health Care-Associated Infections from the Clinical Microbiology Laboratory: Passive, Active, and Virtual Surveillance , 2002, Journal of Clinical Microbiology.

[13]  R. Gaynes,et al.  An overview of nosocomial infections, including the role of the microbiology laboratory , 1993, Clinical Microbiology Reviews.

[14]  J. Hardin,et al.  Association rules and data mining in hospital infection control and public health surveillance. , 1998, Journal of the American Medical Informatics Association : JAMIA.

[15]  R B Schifman,et al.  Surveillance of nosocomial infections by computer analysis of positive culture rates , 1985, Journal of Clinical Microbiology.

[16]  A. Dobson,et al.  The application of statistical process control charts to the detection and monitoring of hospital-acquired infections. , 2001, Journal of quality in clinical practice.

[17]  M. Blaser,et al.  Surveillance for the detection of nosocomial infections and the potential for nosocomial outbreaks. II. Development of a laboratory-based system. , 1985, American journal of infection control.

[18]  R. Wenzel,et al.  Prevention and Control of Nosocomial Infections , 1997 .

[19]  L. Stern,et al.  Automated outbreak detection: a quantitative retrospective analysis , 1999, Epidemiology and Infection.

[20]  Elaine Larson,et al.  A systematic audit of economic evidence linking nosocomial infections and infection control interventions: 1990-2000. , 2002, American journal of infection control.

[21]  L. Peterson,et al.  Medical and economic benefit of a comprehensive infection control program that includes routine determination of microbial clonality. , 1999, American journal of clinical pathology.

[22]  M. Pfaller,et al.  The clinical microbiology laboratory and infection control: emerging pathogens, antimicrobial resistance, and new technology. , 1997, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[23]  L. Peterson,et al.  An Outbreak of Bloodstream Infections in an Outpatient Hemodialysis Center , 2002, Infection Control & Hospital Epidemiology.

[24]  M. Blaser,et al.  Surveillance for the detection of nosocomial infections and the potential for nosocomial outbreaks. I. Microbiology culture surveillance is an effective method of detecting nosocomial infection. , 1984, American journal of infection control.