Web-based surveillance system for the trends, risks and preventions of hospital-acquired multidrug resistant organisms (MDRO) infections

Abstract

Background: Infections caused by multidrug-resistant organisms (MDRO) are associated with high mortality, morbidities and unexpected additional healthcare cost. The emergence of MDRO has become a major global concern as they have compromised our ability to treat infectious diseases. Infection which appear in a patient under medical care in the hospital or other healthcare facility which was absent at the time of admission was defined as hospital acquired infection (HAI). It is the most frequent adverse event in healthcare delivery worldwide, including Malaysia. Using the advancement of information technology, a data analytic system is proposed and developed to monitor epidemiological trends and the spread of MDRO in healthcare settings. Methods and materials: An evidence-based algorithm was used to combine and analyse a variety of structured and unstructured data from the molecular, patients and environmental/practice databases. PFGE were performed to determine the clonal relatedness by using the standard protocol from PulseNet. The banding patterns of PFGE were fragmentised and converted into numerical form. Data retrieved from both patients and hospital environment were inserted into the system and used for risk factor identification as well as infection control decision making. After the analysis, patients’ outcome was matched and a model of transmission pathway was generated to predict the infection risks among patients. Results: Each PFGE banding patterns were converted into numerical data and served as an input data for the specific profile classification. These input data was integrated into a graph for the development comprehensive pathogen transmission features, which were designed based on the linkage and relationship between patient and environmental culture's specific numerical profile; thus, the transmission route could be successfully identified. Conclusion: The system has successfully identified a few episodes of pathogen distribution and transmission. It is expected to be potentially useful and crucial for clinician and infection control unit in clinical settings, especially in terms of real-time surveillance of pathogens dissemination.