Analysis of Multidrug Resistance in Staphylococcus aureus with a Machine Learning-Generated Antibiogram
Casey L. Cazer, Lars F. Westblade, Matthew S. Simon, Reed Magleby, Mariana Castanheira, James G. Booth, Stephen G. Jenkins, Yrjö T. Gröhn
Abstract
Associations between β-lactams and other antimicrobial classes (macrolides, lincosamides, and fluoroquinolones) were common, although the strength of the association among these antimicrobial classes varied by infection site and by methicillin susceptibility. Association mining identified associations between clinically important AMR traits, which could be further investigated for evidence of resistance coselection. For example, in skin and skin structure infections, clindamycin and tetracycline resistance occurred together 1.5 times more often than would be expected if they were independent from one another. Association mining efficiently discovered and quantified associations among resistance traits, allowing these associations to be compared between relevant subsets of isolates to identify and track clinically relevant MDR.