Multidrug resistance (MDR) has become a significant challenge in antimicrobial therapy, with bacteria developing the ability to resist multiple antimicrobial agents simultaneously. Multiple antimicrobials can drive the emergence of multidrug resistance where bacteria already resistant to one drug gain an advantage and acquire additional resistance through mutation, recombination, or horizontal gene transfer. Consequently, the patterns in multidrug resistance can provide critical insights for optimizing antimicrobial stewardship and infection control. Our team is interested in using the Pfizer-ATLAS and Venatorx–GEARS surveillance datasets to delineate the multidrug resistance patterns in the ESKAPE pathogens over 20 years from 2004-2024. First, the team will conduct descriptive data analytics by examining various factors, including antibiotic resistance profiles, age groups, gender, geographical regions, temporal trends, and phenotypic characteristics, specifically focusing on ESKAPE pathogens. This will be followed by association rule mining to discover the ‘associations’ in the development of multidrug resistance across different antibiotics and various factors to explore the evolution of MDR patterns over a period of time. Finally, a dashboard that shows a network representation of the MDR pattern evolution as a function of time will be built. We also wish to showcase the relationship between shared multidrug-resistant patterns and various demographic factors. Overall, our project aims to improve our understanding of MDR pattern evolution, aiding healthcare professionals in making informed decisions about antibiotic use.
Center for Global Research Data