PT  - JOURNAL ARTICLE
AU  - Weis, Caroline
AU  - Cuénod, Aline
AU  - Rieck, Bastian
AU  - Llinares-López, Felipe
AU  - Dubuis, Olivier
AU  - Graf, Susanne
AU  - Lang, Claudia
AU  - Oberle, Michael
AU  - Soegaard, Kirstine K.
AU  - Osthoff, Michael
AU  - Borgwardt, Karsten
AU  - Egli, Adrian
TI  - Direct Antimicrobial Resistance Prediction from MALDI-TOF mass spectra profile in clinical isolates through Machine Learning
AID  - 10.1101/2020.07.30.228411
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.07.30.228411
4099  - http://biorxiv.org/content/early/2020/07/30/2020.07.30.228411.short
4100  - http://biorxiv.org/content/early/2020/07/30/2020.07.30.228411.full
AB  - Early administration of effective antimicrobial treatments improves the outcome of infections. Culture-based antimicrobial resistance testing allows for tailored treatments, but takes up to 96h. We present a revolutionary approach to predict resistance with unmatched speed within 24h, using calibrated logistic regression and LightGBM-classifiers trained on species-specific MALDI-TOF mass spectrometry measurements. For this analysis, we created an unprecedented large, publicly-available dataset combining mass spectra and resistance information. Our models provide highly valuable treatment guidance 12–72h earlier than classical approaches. Rejection of uncertain predictions enables quality control and clinically-applicable sensitivities and specificities for the priority pathogens Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae.Competing Interest StatementThe authors have declared no competing interest.