Enhancing Antibiotic Stewardship: A Machine Learning Approach to Predicting Antibiotic Resistance in Inpatient Care.

Journal: AMIA ... Annual Symposium proceedings. AMIA Symposium
Published Date: May 22, 2025

Abstract

Antibiotics have been crucial in advancing medical treatments, but the growing threat of antibiotic resistance challenges these achievements and emphasizes the need for innovative stewardship strategies. In this study, we developed machine learning models, 'personalized antibiograms', to predict antibiotic resistance across five key antibiotics using Stanford's electronic health record data of 49,872 urine, blood, and respiratory infections. We aimed to ascertain the efficacy of these models in predicting antibiotic susceptibility and identify the clinical factors most indicative of resistance. Employing LightGBM, we incorporated demographics, prior resistance, prescriptions, and comorbidities as features. The models demonstrated notable discriminative ability, with AUROCs between 0.74 and 0.78, and highlighted prior resistance and prescriptions as significant predictive factors. The high specificity demonstrates machine learning models' potential to inform antibiotic de-escalation, aiding stewardship without risking safety. By leveraging machine learning with relevant clinical features, we show that it is feasible to improve empirical antibiotic prescribing.

Authors

  • Fateme Nateghi Haredasht
    Department of Public Health and Primary Care, KU Leuven Campus Kulak Kortrijk, Kortrijk, Belgium.
  • Manoj V Maddali
    Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Stanford University, Stanford, CA, USA. Electronic address: manoj.maddali@stanford.edu.
  • Stephen P Ma
    Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, United States.
  • Amy Chang
    Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, CA, USA.
  • Grace Y E Kim
    Department of Computer Science, Stanford, CA.
  • Niaz Banaei
    Department of Pathology, Stanford University School of Medicine, CA, USA; Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University School of Medicine, CA, USA; Clinical Microbiology Laboratory, Stanford Health Care, CA, USA. Electronic address: nbanaei@stanford.edu.
  • Stanley Deresinski
    Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, CA, USA.
  • Mary K Goldstein
    VA Palo Alto Health Care System, Palo Alto, CA, and Stanford University, Stanford, CA, USA.
  • Steven M Asch
    Center for Innovation to Implementation, VA Palo Alto Health Care System, Menlo Park, CA; Division of General Medical Disciplines, Stanford University, Stanford, CA.
  • Jonathan H Chen
    Stanford Center for Biomedical Informatics Research, Stanford, CA.

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