Purpose. Antimicrobial susceptibility is slow to determine, taking several days to fully impact treatment. This proof-of-concept study assessed the feasibility of using machine-learning techniques for analysis of data produced by the flow cytometer-assisted antimicrobial susceptibility test (FAST) method we developed.Methods. We used machine learning to assess the effect of antimicrobial agents on bacteria, comparing FAST results with broth microdilution (BMD) antimicrobial susceptibility tests (ASTs). We used Escherichia coli (1), Klebsiella pneumoniae (1) and Staphylococcus aureus (2) strains to develop the machine-learning algorithm, an expanded panel including these plus E. coli (2), K. pneumoniae (3), Proteus mirabilis (1), Pseudomonas aeruginosa (1), S. aureus (2) and Enterococcus faecalis (1), tested against FAST and BMD (Sensititre, Oxoid), then two representative isolates directly from blood cultures.Results. Our data machines defined an antibiotic-unexposed population (AUP) of bacteria, classified the FAST result by antimicrobial concentration range, and determined a concentration-dependent antimicrobial effect (CDE) to establish a predicted inhibitory concentration (PIC). Reference strains of E. coli, K. pneumoniae and S. aureus tested with different antimicrobial agents demonstrated concordance between BMD results and machine-learning analysis (CA, categoric agreement of 91 %; EA, essential agreement of 100 %). CA was achieved in 35 (83 %) and EA in 28 (67 %) by machine learning on first pass in a challenge panel of 27 Gram-negative and 15 Gram-positive ASTs. Same-day AST results were obtained from clinical E. coli (1) and S. aureus (1) isolates.Conclusions. The combination of machine learning with the FAST method generated same-day AST results and has the potential to aid early antimicrobial treatment decisions, stewardship and detection of resistance.
Keywords: Escherichia coli; Klebsiella pneumoniae; Staphylococcus aureus; antimicrobial susceptibility test; flow cytometer; machine learning.