Shared Multidrug Resistance Patterns in Chicken-Associated Escherichia coli Identified by Association Rule Mining

• Main Authors: Casey L. Cazer, Mohammad A. Al-Mamun, Karun Kaniyamattam, William J. Love, James G. Booth, Cristina Lanzas, Yrjö T. Gröhn
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2019-04-01
• Series: Frontiers in Microbiology
• Subjects: association rule mining; antimicrobial resistance; Escherichia coli; machine learning; multidrug resistance; foodborne bacteria
• Online Access: https://www.frontiersin.org/article/10.3389/fmicb.2019.00687/full

Using multiple antimicrobials in food animals may incubate genetically-linked multidrug-resistance (MDR) in enteric bacteria, which can contaminate meat at slaughter. The U.S. National Antimicrobial Resistance Monitoring System tested 14,418 chicken-associated Escherichia coli between 2004 and 2012 for resistance to 15 antimicrobials, resulting in >32,000 possible MDR patterns. We analyzed MDR patterns in this dataset with association rule mining, also called market-basket analysis. The association rules were pruned with four quality measures resulting in a <1% false-discovery rate. MDR rules were more stable across consecutive years than between slaughter and retail. Rules were decomposed into networks with antimicrobials as nodes and rules as edges. A strong subnetwork of beta-lactam resistance existed in each year and the beta-lactam resistances also had strong associations with sulfisoxazole, gentamicin, streptomycin and tetracycline resistances. The association rules concur with previously identified E. coli resistance patterns but provide significant flexibility for studying MDR in large datasets.