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Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning

Abstract

Early use 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 matrix-assisted laser desorption/ionization–time of flight (MALDI-TOF) mass spectra profiles of clinical isolates. We trained calibrated classifiers on a newly created publicly available database of mass spectra profiles from the clinically most relevant isolates with linked antimicrobial susceptibility phenotypes. This dataset combines more than 300,000 mass spectra with more than 750,000 antimicrobial resistance phenotypes from four medical institutions. Validation on a panel of clinically important pathogens, including Staphylococcus aureus, Escherichia coli and Klebsiella pneumoniae, resulting in areas under the receiver operating characteristic curve 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 of 63 patients found that implementing this approach would have changed the clinical treatment in nine cases, which would have been beneficial in eight cases (89%). MALDI-TOF mass spectra-based machine learning may thus be an important new tool for treatment optimization and antibiotic stewardship.

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Fig. 1: MALDI-TOF mass spectrometry (MS)-based antimicrobial resistance (AMR) prediction workflow.
Fig. 2: Receiver operating characteristic and precision–recall curves of best-performance AMR prediction models on DRIAMS-A.
Fig. 3: Validation of predictive performance of each scenario trained and tested on DRIAMS-A–D (AUROC).
Fig. 4: Stability of results with different dataset perturbations.
Fig. 5: Quantification of feature impact on prediction through analysis of Shapley additive explanations (SHAP) values of the 30 most impactful features.
Fig. 6: Retrospective clinical case study including 63 cases of invasive bacterial infection.

Data availability

The full datasets generated during and analyzed during the current study are available in the Dryad repository, https://doi.org/10.5061/dryad.bzkh1899q.

Code availability

All R and Python scripts can be found in https://github.com/BorgwardtLab/maldi_amr under a BSD 3-Clause License.

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Acknowledgements

The authors thank O. Grüninger, J. Reist, Y. Mahiddine, D. Lang, C. Straub and M. Schneider for excellent technical assistance in accessing the mass spectra and antimicrobial resistance data at the University Hospital Basel; and B. Rodríguez-Sánchez (Hospital General Universitario Gregorio Marañón, Madrid, Spain), H. Seth-Smith (University Hospital Basel), C. Kaufmann (University Hospital Basel), and V. Pflüger (MabritecAG, Riehen, Switzerland) for valuable advice, technical consultation and feedback throughout the project. The authors also thank X. He (ETH Zurich) for fruitful discussions, and D. Chen (ETH Zurich) and Jacques Schrenzel (Geneva University Hospitals) for assistance with the manuscript and for providing valuable feedback. This study was supported by the Alfried Krupp Prize for Young University Teachers of the Alfried Krupp von Bohlen und Halbach-Stiftung (K.B.), by the two Cantons of Basel through a D-BSSE-Uni-Basel Personalised Medicine grant from the ETH Zurich (PMB-03-17, K.B. and A.E.) and a Doc.Mobility fellowship (A.C.) by the Swiss National Science Foundation (P1BSP3-184342). This study received ethics approval through the local ethics review board (EKNZ number: IEC 2019-00729, 2019-01860 and 2019-00748).

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Authors and Affiliations

Authors

Contributions

C.W., B.R. and K.B. designed the machine learning experiments; C.W. and B.R. implemented all experiments of the machine learning analysis; A.E., A.C. and K.K.S. organized data collection; A.C., S.G., O.D., C.L. and M.O. extracted clinical data; A.C. and M.O. performed the retrospective clinical case study; A.C. and C.W. implemented the preprocessing of the datasets DRIAMS-A and DRIAMS-B; C.W. implemented the preprocessing of the datasets DRIAMS-C and DRIAMS-D; M.B. contributed to mass spectrometry data interpretation; M.O. and A.E. provided feedback on the clinical implications of resistance predictions; C.W., B.R. and A.C. designed all display items; C.W., B.R., A.C., K.B. and A.E. wrote the manuscript with the assistance and feedback of all of the co-authors. K.B. and A.E. conceived and supervised the study.

Corresponding authors

Correspondence to Caroline Weis, Karsten Borgwardt or Adrian Egli.

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The authors declare no competing interests.

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Nature Medicine thanks Roman Yelensky and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Alison Farrell was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Comparison turnaround times between MALDI-TOF MS and resistance.

Time from the entry of a patient sample at the diagnostic laboratory at the DRIAMS-A collection site to species identification by MALDI-TOF MS and phenotypic resistance testing for three clinically relevant species: E. coli (n = 54), K. pneumoniae (n = 66), and S. aureus (n = 57). Boxplot shows median and interquartile time ranges in hours, whiskers indicate adjacent values.

Extended Data Fig. 2 Improved antimicrobial resistance prediction based on MALDI-TOF mass spectra combining all species compared to species information alone.

AUROC values of logistic regression classifiers trained on data combining all samples with labels available for each antimicrobial prediction task in DRIAMS-A. The blue bars depict predictive performance using spectral data as features. The red bars show the predictive performance when using species label information only. The fractions of resistant/intermediate samples in the training data are indicated in brackets after the antibiotic name. Reported metrics and error bars are the mean and standard deviation of 10 repetitions with different random train–test-splits. The asterisks indicate a statistically significant difference between the reported metrics between all species and species information alone of a two-sided Welch’s t-test (not assuming equal population variance) and a significance level of <0.05.

Extended Data Fig. 3 Flowchart inclusion of cases into the retrospective clinical study.

We reviewed 416 clinical cases which had a severe bacterial infection with K. pneumoniae, E. coli or S. aureus between April and August 2018. Cases were excluded if (i) cases were treated external to the DRIAMS-A collection site, (ii) no consultation note by a infectious diseases specialist was available within 5 days (for cases with a positive blood culture) or 1 day (for cases with a positive deep tissue sample), (iii) the general research consent was rejected, (iv) the species identified by the Bruker database did not match the species in the laboratory report, (v) the antibiotic resistance profile was already present at the at the time of the infectious diseases consultation and (vi) if the consultation note was written without the knowledge of the species identity. 63 clinical cases were included.

Extended Data Fig. 4 Barplot of feature importances of LightGBM and MLP model.

Importance values larger than two times absolute standard deviation are colored in either blue (LightGBM) or orange (MLP). The sign of each feature importance value of the MLP model indicates the association with the positive (positive sign) or negative class. The LightGBM values indicate the contribution to the prediction without direction of association. All three models indicate that a large number of features are relevant for an accurate antimicrobial resistance prediction. The scenario abbreviations follow Fig. 2a.

Extended Data Fig. 5 Temporal validation including sample size of training window.

The timepoints correspond to points in Fig. 4 and arrow directions indicate time progression. With time progression both the trends in sample size per 8-month time window and the predictive performance increase. The scenario abbreviations follow Fig. 2a.

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Weis, C., Cuénod, A., Rieck, B. et al. Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning. Nat Med 28, 164–174 (2022). https://doi.org/10.1038/s41591-021-01619-9

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