Ceftolozane/tazbactam (C/T) is a potent anti-pseudomonal agent that has clinical utility against infections caused by non-carbapenemase producing carbapenem-resistant P. aeruginosa (non-CP-CR-PA). Accurate, precise and reliable antimicrobial susceptibility testing (AST) is crucial to guide clinical decisions. However, studies assessing the performance of different AST methods against non-CP-CR-PA- (the main clinical niche for C/T), are lacking. Here, we evaluated performance of gradient strips (Etest and MIC test strip (MTS), and disk diffusion (DD) using CLSI breakpoints. Additionally, we assessed the performance of DD using EUCAST breakpoints. For all susceptibility tests, we used a collection of 97 non-CP-CR-PA clinical isolates recovered from 11 Chilean hospitals. Both gradient strips and DD had acceptable performance when using CLSI breakpoints, yielding a categorical agreement (CA) of >90% and 92%, respectively. In contrast, DD using EUCAST breakpoints performed sub-optimally (CA 81%). MTS yielded a higher essential agreement (EA, >90%) than Etest (84%). Importantly, the performance of all methods varied significantly when the isolates were stratified by their degree of susceptibility to other anti-pseudomonal β-lactams. All methods had 100% CA when testing isolates that were pan-susceptible to all β-lactams (Pan-β-S). However, the CA markedly decreased when testing isolates resistant to all β-lactams (Pan-β-R). Indeed, the CA was 81% for Etest (6 errors), 78% for MTS (7 errors) and 78% and 56% for DD when using CLSI (7 errors) or EUCAST breakpoints (14 errors), respectively. Our results suggest that all manual AST methods have strikingly decreased performance in the context of Pan-β-R P. aeruginosa with potentially major clinical implications.

Methicillin-resistant Staphylococcus aureus (MRSA) is a major public health pathogen that disseminates through the emergence of successful dominant clones in specific geographic regions. Knowledge of the dissemination and molecular epidemiology of MRSA in Latin America is scarce and is largely based on small studies or more limited typing techniques that lack the resolution to represent an accurate description of the genomic landscape.