Exploring the evolution of multidrug resistance patterns in ESKAPEE pathogens using association mining: Key to antibiotic stewardship?
Abstract
Background Shared Multidrug resistance (sMDR) is a major challenge in antimicrobial therapy, particularly among ESKAPEE pathogens, as these bacteria develop resistance to multiple antimicrobial agents via mutations, recombination, or gene transfer. These antimicrobial resistance (AMR) mechanisms evolve and require constant surveillance. Understanding sMDR patterns in large antimicrobial surveillance datasets is complex because of the multifaceted nature of the data. Methods We explored Pfizer-Atlas (2004 – 2022) and Venatorx-GEARS (2018-2022) datasets from the Vivli platform, focusing on ESKAPEE pathogens. Descriptive data analysis, time-trend analysis, and association rule generation through the Apriori algorithm were performed using Python 3.10 libraries pandas, matplotlib, seaborn, scipy.stats, and mlxtend libraries. The best rule set that explored sMDR patterns was visualized in a network format using NetworkX: 3.2 package. Results “MERIT- Multidrug ESKAPEE Resistance Insights and Tracker” dashboard was created for user-friendly visualization of antimicrobial surveillance datasets, trends in antimicrobial susceptibility profile (ASP) over years, interactive widgets to see the ASP by country and by pathogens, and Network to see significant rules as a result of association rule mining in categories by age, year and country. Conclusion Time trend analysis revealed a decline in meropenem and piperacillin-tazobactam resistance to Enterococcus faecium and doripenem for…
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