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

Here, antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to rapidly 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.
 [1] ;  [2] ; ORCiD logo [3] ;  [3] ;  [3] ;  [2] ;  [2] ;  [2] ;  [2] ;  [2] ;  [2]
  1. Northern Illinois Univ., DeKalb, IL (United States); Univ. of Chicago, Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States)
  2. Univ. of Chicago, Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States)
  3. Houston Methodist Research Institute and Houston Methodist Hospital, Houston, TX (United States); Weill Cornell Medical College, New York, NY (United States)
Publication Date:
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Scientific Reports
Additional Journal Information:
Journal Volume: 8; Journal Issue: 1; Journal ID: ISSN 2045-2322
Nature Publishing Group
Research Org:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org:
National Institutes of Health (NIH); USDOE
Country of Publication:
United States
OSTI Identifier: