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A Web-based multidrug-resistant organisms surveillance and outbreak detection system with rule-based classification
Yi-Ju Tseng1, Jung-Hsuan Wu, Xiao-Ou Ping
1Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
Journal of Medical Internet Research
|December 1, 2012
Summary
A new Web-based system automatically identifies multidrug-resistant organisms (MDROs) and detects outbreaks using antimicrobial susceptibility data. This tool aids in combating the global antimicrobial resistance crisis.
Area of Science:
- Infectious Diseases
- Medical Informatics
- Clinical Microbiology
Background:
- Multidrug-resistant organisms (MDROs) pose a significant global health threat.
- Combating antimicrobial resistance necessitates preventing transmission and optimizing antimicrobial use.
Purpose of the Study:
- Develop a Web-based system for automated integration and analysis of antimicrobial susceptibility data.
- Incorporate rule-based classification and cluster analysis for MDROs.
- Implement control chart analysis to enhance outbreak detection.
Main Methods:
- Classified electronic microbiological data from a large teaching hospital based on predefined MDRO criteria.
- Utilized hierarchical clustering with upper control limits (UCL) for outbreak detection.
- Evaluated system performance using vancomycin-resistant enterococcal outbreaks and a prospective surveillance database.
Main Results:
- Optimal UCLs for MDRO outbreak detection were identified using germ, patient, and incident patient criteria with clustering.
- Clustering significantly improved performance indicators across all criteria compared to non-clustered methods.
- The system demonstrated high accuracy in detecting MDRO outbreaks, with AUC values up to 0.93.
Conclusions:
- The developed system effectively automates the identification of MDROs.
- The system accurately detects suspicious MDRO outbreaks by analyzing antimicrobial susceptibility data of clinical isolates.
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