There is an urgent need to develop simple and fast antimicrobial susceptibility tests (ASTs) that allow informed prescribing of antibiotics. Here, we describe a label-free AST that can deliver results within an hour, using an actively dividing culture as starting material. The bacteria are incubated in the presence of an antibiotic for 30 min, and then approximately 105 cells are analysed one-by-one with microfluidic impedance cytometry for 2–3 min. The measured electrical characteristics reflect the phenotypic response of the bacteria to the mode of action of a particular antibiotic, in a 30-minute incubation window. The results are consistent with those obtained by classical broth microdilution assays for a range of antibiotics and bacterial species.
There is an urgent need to develop simple and fast antimicrobial susceptibility tests that allow informed prescribing of antibiotics. In this protocol we describe a method for a label-free AST that can deliver results within an hour, using an actively dividing culture as starting material.
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.
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