A web-based multidrug-resistant organisms surveillance and outbreak detection system with rule-based classification and clustering

Yi Ju Tseng, Jung Hsuan Wu, Xiao Ou Ping, Hui Chi Lin, Ying Yu Chen, Rung Ji Shang, Ming Yuan Chen, Feipei Lai, Yee Chun Chen*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

10 Scopus citations


Background: The emergence and spread of multidrug-resistant organisms (MDROs) are causing a global crisis. Combating antimicrobial resistance requires prevention of transmission of resistant organisms and improved use of antimicrobials. Objectives: To develop a Web-based information system for automatic integration, analysis, and interpretation of the antimicrobial susceptibility of all clinical isolates that incorporates rule-based classification and cluster analysis of MDROs and implements control chart analysis to facilitate outbreak detection. Methods: Electronic microbiological data from a 2200-bed teaching hospital in Taiwan were classified according to predefined criteria of MDROs. The numbers of organisms, patients, and incident patients in each MDRO pattern were presented graphically to describe spatial and time information in a Web-based user interface. Hierarchical clustering with 7 upper control limits (UCL) was used to detect suspicious outbreaks. The system's performance in outbreak detection was evaluated based on vancomycin-resistant enterococcal outbreaks determined by a hospital-wide prospective active surveillance database compiled by infection control personnel. Results: The optimal UCL for MDRO outbreak detection was the upper 90% confidence interval (CI) using germ criterion with clustering (area under ROC curve (AUC) 0.93, 95% CI 0.91 to 0.95), upper 85% CI using patient criterion (AUC 0.87, 95% CI 0.80 to 0.93), and one standard deviation using incident patient criterion (AUC 0.84, 95% CI 0.75 to 0.92). The performance indicators of each UCL were statistically significantly higher with clustering than those without clustering in germ criterion (P < .001), patient criterion (P = .04), and incident patient criterion (P < .001). Conclusion: This system automatically identifies MDROs and accurately detects suspicious outbreaks of MDROs based on the antimicrobial susceptibility of all clinical isolates.

Original languageEnglish
Article numbere131
JournalJournal of Medical Internet Research
Issue number5
StatePublished - 2012
Externally publishedYes


  • Cluster analysis
  • Infection control
  • Information systems
  • Multidrug resistance
  • Surveillance
  • Web-based services


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