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bioRxiv · 10.1101/193797

Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae

Nguyen, M.·Brettin, T.·Long, S. W.·Olsen, R. J.·Musser, J. M.·Olson, R.·Shukla, M.·Stevens, R. L.·Xia, F.·Yoo, H.·Davis, J.
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Abstract

Antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to apidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way to reduce clinical test turnaround times compared to conventional culture-based methods, and in turn, improve patient outcomes. In this study, we use whole genome sequence data from 1668 clinical isolates of Klebsiella pneumoniae to develop a XGBoost-based machine learning model that accurately predicts minimum inhibitory concentrations (MICs) for 20 antibiotics. The overall accuracy of the model, within {+/-}1 two-fold dilution factor, is 92%. Individual accuracies are[≥]90% for 15/20 antibiotics. We show that the MICs predicted by the model correlate with known antimicrobial resistance genes. Importantly, the genome-wide approach described in this study offers a way to predict MICs for isolates without knowledge of the underlying gene content. This study shows that machine learning can be used to build a complete in silico MIC prediction panel for K. pneumoniae and provides a framework for building MIC prediction models for other pathogenic bacteria.

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Nguyen, M., Brettin, T., Long, S. W., Olsen, R. J., Musser, J. M., Olson, R., Shukla, M., Stevens, R. L., Xia, F., Yoo, H., Davis, J.. 2017-09-25. Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae. https://doi.org/10.1101/193797

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