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Direct Antimicrobial Resistance Prediction from clinical MALDI-TOF mass spectra using Machine Learning

View ORCID ProfileCaroline Weis, View ORCID ProfileAline Cuénod, View ORCID ProfileBastian Rieck, Felipe Llinares-López, Olivier Dubuis, Susanne Graf, Claudia Lang, Michael Oberle, View ORCID ProfileMaximilian Brackmann, Kirstine K. Søgaard, View ORCID ProfileMichael Osthoff, View ORCID ProfileKarsten Borgwardt, View ORCID ProfileAdrian Egli
doi: https://doi.org/10.1101/2020.07.30.228411
Caroline Weis
1Department of Biosystems and Engineering, ETH Zürich, Basel, Switzerland
2SIB Swiss Institute of Bioinformatics, Switzerland
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  • ORCID record for Caroline Weis
  • For correspondence: caroline.weis@bsse.ethz.ch karsten.borgwardt@bsse.ethz.ch adrian.egli@usb.ch
Aline Cuénod
3Applied Microbiology Research, Department of Biomedicine, University of Basel, Basel, Switzerland
4Division of Clinical Bacteriology and Mycology, University Hospital Basel, Basel, Switzerland
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Bastian Rieck
1Department of Biosystems and Engineering, ETH Zürich, Basel, Switzerland
2SIB Swiss Institute of Bioinformatics, Switzerland
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Felipe Llinares-López
1Department of Biosystems and Engineering, ETH Zürich, Basel, Switzerland
2SIB Swiss Institute of Bioinformatics, Switzerland
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Olivier Dubuis
8Clinical Microbiology, Viollier AG, Allschwil, Switzerland
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Susanne Graf
7Department for Microbiology, Cantonal Hospital Baselland, Liestal, Switzerland
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Claudia Lang
8Clinical Microbiology, Viollier AG, Allschwil, Switzerland
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Michael Oberle
9Institute for Laboratory Medicine, Medical Microbiology, Cantonal Hospital of Aarau, Aarau, Switzerland
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Maximilian Brackmann
10Federal Office for Civil Protection, Spiez Laboratory, Proteomics, Bioinformatics and Toxins, Spiez, Switzerland
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Kirstine K. Søgaard
3Applied Microbiology Research, Department of Biomedicine, University of Basel, Basel, Switzerland
4Division of Clinical Bacteriology and Mycology, University Hospital Basel, Basel, Switzerland
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Michael Osthoff
5Division of Infectious Diseases and Hospital Epidemiology, University Hospital and University of Basel, Basel, Switzerland
6Department of Internal Medicine, University Hospital Basel and University of Basel, Basel, Switzerland
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Karsten Borgwardt
1Department of Biosystems and Engineering, ETH Zürich, Basel, Switzerland
2SIB Swiss Institute of Bioinformatics, Switzerland
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  • For correspondence: caroline.weis@bsse.ethz.ch karsten.borgwardt@bsse.ethz.ch adrian.egli@usb.ch
Adrian Egli
3Applied Microbiology Research, Department of Biomedicine, University of Basel, Basel, Switzerland
4Division of Clinical Bacteriology and Mycology, University Hospital Basel, Basel, Switzerland
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  • For correspondence: caroline.weis@bsse.ethz.ch karsten.borgwardt@bsse.ethz.ch adrian.egli@usb.ch
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Abstract

Early administration of effective antimicrobial treatments is critical for the outcome of infections. Antimicrobial resistance testing enables the selection of optimal antibiotic treatments, but current culture-based techniques take up to 72 hours. 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 0.8, 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 antibiotic stewardship.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/BorgwardtLab/maldi_amr

  • https://github.com/BorgwardtLab/maldi-learn

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted July 12, 2021.
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Direct Antimicrobial Resistance Prediction from clinical MALDI-TOF mass spectra using Machine Learning
Caroline Weis, Aline Cuénod, Bastian Rieck, Felipe Llinares-López, Olivier Dubuis, Susanne Graf, Claudia Lang, Michael Oberle, Maximilian Brackmann, Kirstine K. Søgaard, Michael Osthoff, Karsten Borgwardt, Adrian Egli
bioRxiv 2020.07.30.228411; doi: https://doi.org/10.1101/2020.07.30.228411
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Direct Antimicrobial Resistance Prediction from clinical MALDI-TOF mass spectra using Machine Learning
Caroline Weis, Aline Cuénod, Bastian Rieck, Felipe Llinares-López, Olivier Dubuis, Susanne Graf, Claudia Lang, Michael Oberle, Maximilian Brackmann, Kirstine K. Søgaard, Michael Osthoff, Karsten Borgwardt, Adrian Egli
bioRxiv 2020.07.30.228411; doi: https://doi.org/10.1101/2020.07.30.228411

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