RESUMO
Antimicrobial resistance within Staphylococcus pseudintermedius poses a significant risk for the treatment of canine pyoderma and as a reservoir for resistance and potential zoonoses, but few studies examine long-term temporal trends of resistance. This study assesses the antimicrobial resistance prevalence and minimum inhibitory concentration (MIC) trends in S. pseudintermedius (n=1804) isolated from canine skin samples at the Cornell University Animal Health Diagnostic Center (AHDC) between 2007 and 2020. Not susceptible (NS) prevalence, Cochran-Armitage tests, logrank tests, MIC50 and MIC90 quantiles, and survival analysis models were used to evaluate resistance prevalence and temporal trends to 23 antimicrobials. We use splines as predictors in accelerated failure time (AFT) models to model non-linear temporal trends in MICs. Multidrug resistance was common among isolates (47%), and isolates had moderate to high NS prevalence to the beta-lactams, chloramphenicol, the fluoroquinolones, gentamicin, the macrolides/lincosamides, the tetracyclines, and trimethoprim-sulfamethoxazole. However, low levels of NS to amikacin, rifampin, and vancomycin were observed. Around one third of isolates (38%) were found to be methicillin resistant S. pseudintermedius (MRSP), and these isolates had a higher prevalence of NS to all tested antimicrobials than methicillin susceptible isolates. Amongst the MRSP isolates, one phenotypically vancomycin resistant isolate (MIC >16⯵g/mL) was identified, but genomic sequence data was unavailable. AFT models showed increasing MICs across time to the beta-lactams, chloramphenicol, the fluoroquinolones, gentamicin, and the macrolides/lincosamides, and decreasing temporal resistance (decreasing MICs) to doxycycline was observed amongst isolates. Notably, ATF modeling showed changes in MIC distributions that were not identified using Cochran-Armitage tests on prevalence, MIC quantiles, and logrank tests. Increasing resistance amongst these S. pseudintermedius isolates highlights the need for rational, empirical prescribing practices and increased antimicrobial resistance (AMR) surveillance to maintain the efficacy of current therapeutic agents. AFT models with non-linear predictors may be a useful, breakpoint-independent, surveillance tool alongside other modeling methods and antibiograms.
