Antimicrobial susceptibility tests (ASTs) are pivotal in combating multidrug resistant pathogens, yet they can be time-consuming, labor-intensive, and unstable. Using the AST of tigecycline for sepsis as the main model, here we establish an automated system of Clinical Antimicrobials Susceptibility Test Ramanometry (CAST-R), based on DO-probed Raman microspectroscopy. Featuring a liquid robot for sample pretreatment and a machine learning-based control scheme for data acquisition and quality control, the 3-h, automated CAST-R process accelerates AST by >10-fold, processes 96 paralleled antibiotic-exposure reactions, and produces high-quality Raman spectra. The Expedited Minimal Inhibitory Concentration via Metabolic Activity is proposed as a quantitative and broadly applicable parameter for metabolism-based AST, which shows 99% essential agreement and 93% categorical agreement with the broth microdilution method (BMD) when tested on 100 isolates. Further tests on 26 clinically positive blood samples for eight antimicrobials, including tigecycline, meropenem, ceftazidime, ampicillin/sulbactam, oxacillin, clindamycin, vancomycin, and levofloxacin reveal 93% categorical agreement with BMD-based results. The automation, speed, reliability, and general applicability of CAST-R suggest its potential utility for guiding the clinical administration of antimicrobials.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10989881PMC
http://dx.doi.org/10.1002/mlf2.12019DOI Listing

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