Analysis of Multidrug Resistance in Staphylococcus aureus with a Machine Learning-Generated Antibiogram.

TitleAnalysis of Multidrug Resistance in Staphylococcus aureus with a Machine Learning-Generated Antibiogram.
Publication TypeJournal Article
Year of Publication2021
AuthorsCazer CL, Westblade LF, Simon MS, Magleby R, Castanheira M, Booth JG, Jenkins SG, Gröhn YT
JournalAntimicrob Agents Chemother
Date Published2021 03 18
KeywordsAnti-Bacterial Agents, Drug Resistance, Bacterial, Drug Resistance, Multiple, Humans, Machine Learning, Microbial Sensitivity Tests, New York, Staphylococcal Infections, Staphylococcus aureus

Multidrug resistance (MDR) surveillance consists of reporting MDR prevalence and MDR phenotypes. Detailed knowledge of the specific associations underlying MDR patterns can allow antimicrobial stewardship programs to accurately identify clinically relevant resistance patterns. We applied machine learning and graphical networks to quantify and visualize associations between resistance traits in a set of 1,091 isolates collected from one New York hospital between 2008 and 2018. Antimicrobial susceptibility testing was performed using reference broth microdilution. The isolates were analyzed by year, methicillin susceptibility, and infection site. Association mining was used to identify resistance patterns that consisted of two or more individual antimicrobial resistance (AMR) traits and quantify the association among the individual resistance traits in each pattern. The resistance patterns captured the majority of the most common MDR phenotypes and reflected previously identified pairwise relationships between AMR traits in 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.

Alternate JournalAntimicrob Agents Chemother
PubMed ID33431415
PubMed Central IDPMC8097487
Grant ListT32 OD011000 / OD / NIH HHS / United States
Related Faculty: 
Lars Westblade, Ph.D.

Pathology & Laboratory Medicine 1300 York Avenue New York, NY 10065 Phone: (212) 746-6464
Surgical Pathology: (212) 746-2700