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

Marcus Nguyen, Thomas Brettin, S. Wesley Long, James M. Musser, Randall J. Olsen, Robert Olson, Maulik Shukla, Rick L. Stevens, Fangfang Xia, Hyunseung Yoo, James J. Davis

Research output: Contribution to journalArticle

41 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number421
JournalScientific Reports
Volume8
Issue number1
DOIs
StatePublished - Jan 11 2018

ASJC Scopus subject areas

  • General

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