A Web-Based Multidrug-Resistant Organisms Surveillance and Outbreak Detection System with Rule-Based Classification and Clustering

Abstract

BackgroundThe 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.ObjectivesTo 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.MethodsElectronic 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.ResultsThe 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). ConclusionThis system automatically identifies MDROs and accurately detects suspicious outbreaks of MDROs based on the antimicrobial susceptibility of all clinical isolates.

Data elements and validation methods used for electronic surveillance of health care-associated infections: A systematic review

“…These formats varied from unstructured, non-coded, and institution-specific coded data to internationally and nationally adopted formats like ICD-9. To determine antibiotics administered, textual medication names 9–11 , and institution-specific codes 12–14 formats were used. ICD-9 4,9–11,15–19 , SNOMED-CT 20–22 and free text from notes were used to determine hospital billing diagnosis and procedures 10,11,17,21 .…”

“…ICD-9 4,9–11,15–19 , SNOMED-CT 20–22 and free text from notes were used to determine hospital billing diagnosis and procedures 10,11,17,21 . Microbiology results were formatted in institution-specific codes 10,11,13,14,17 , textual results 3–5,9,12,16,18,22–30 , and LOINC 22 codes.…”

A Web-Based, Hospital-Wide Health Care-Associated Bloodstream Infection Surveillance and Classification System: Development and Evaluation

“…This data-collection subsystem collects relevant data from HIS and LIS using HL7 standards, which was extended stepwise from the previous version [ 13 ]. For example, data related to age, sex, ward transfer, admission date, and discharge diagnosis are obtained from the hospital administrative system.…”

“…In continuation with our previous efforts in developing a Web-based MDRO surveillance system that automatically identifies and accurately detects suspicious outbreaks of MDROs [ 13 ], implementation of which could save 1 person-day daily, we conducted this study with the aim of developing a Web-based automatic surveillance and classification system for health care-associated bloodstream infection (HABSI), the most common HAIs at NTUH [ 6 ]. In addition, performance of the system was evaluated by comparing the proposed system with a conventional infection control personnel (ICP)-based surveillance system.…”

Healthcare Associated Infections: An Interoperable Infrastructure for Multidrug Resistant Organism Surveillance

“…The overall infrastructure has been planned adopting the service oriented architecture (SOA) paradigm, because it allows designing distributed solutions in which existing and new applications can be aligned and integrated [24][25][26][27][28][29]. This approach was also adopted in the same context for a web-based multidrug-resistant organisms surveillance and outbreak detection system [30]. Tseng et al combined the SOA with HL7 (Health Level Seven) standards.…”