Work

Personalized Antibiograms: Machine Learning for Precision Selection of Empiric Antibiotics.

Year: 2020

Type: article

Abstract: Up to 50% of antibiotic use in hospital settings is suboptimal. We build machine learning models trained on electronic health record data to minimize wasteful use of antibiotics. Our classifiers flag no growth blood and urine microbial cultures with high precision. Further, we build models that predict the likelihood of bacterial susceptibility to sets of antibiotics. These models contain decision thresholds that separate subgroups of patients whose susceptibility rates to narrow-spectrum antibiotics equal overall susceptibility rates to broader-spectrum drugs. Retroactively analyzing these thresholds on our one year test set, we find that 14% of patients infected with Escherichia coli and empirically treated with piperacillin/tazobactam could have been treated with ceftriaxone with coverage equal to the overall susceptibility rate ofpiperacillin/tazobactam. Similarly, 13% of the same cohort could have been treated with cefazolin - a first generation cephalosporin.

Source: PubMed Central

Authors: Conor K Corbin, Richard J Medford, Kojo Osei, Jonathan H Chen

Institutions: Stanford University, The University of Texas Southwestern Medical Center

Language: English

Topic: Antibiotic Use and Resistance

Subfield: Infectious Diseases

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute of Environmental Health Sciences, National Center for Research Resources

Awards: Spectrum Stanford Center for clinical and Translational Research and Education, Stanford Center for Clinical and Translational Education and Research (SCCTER)UL1, Data-Mining Clinical Decision Support from Electronic Health Records