DRIAMS: Database of Resistance Information on Antimicrobials and MALDI-TOF Mass Spectra

Early administration of effective antimicrobial treatments is critical for
the outcome of infections and the prevention of treatment resistance.
Antimicrobial resistance testing enables the selection of optimal
antibiotic treatments, but current culture-based techniques can take up to
72 hours to generate results. We have developed a novel machine learning
approach to predict antimicrobial resistance directly from MALDI-TOF mass
spectra profiles of clinical samples. We trained calibrated classifiers on
a newly-created publicly available database of mass spectra profiles from
clinically most relevant isolates with linked antimicrobial susceptibility
phenotypes. The dataset combines more than 300,000 mass spectra with more
than 750,000 antimicrobial resistance phenotypes from four medical
institutions. Validation against a panel of clinically important
pathogens, including Staphylococcus aureus, Escherichia coli, and
Klebsiella pneumoniae, resulting in AUROC values of 0.80, 0.74, and 0.74
respectively, demonstrated the potential of using machine learning to
substantially accelerate antimicrobial resistance determination and change
of clinical management. Furthermore, a retrospective clinical case study
found that implementation of this approach would have resulted in a
beneficial change in the clinical treatment in 88% (8/9) of cases.
MALDI-TOF mass spectra based machine learning may thus be an important new
tool for treatment optimization and antibiotic stewardship.