Personalized antibiograms for machine learning driven antibiotic selection

Conor K Corbin; Lillian Sung; Arhana Chattopadhyay; Morteza Noshad; Amy Chang; Stanley Deresinksi; Michael Baiocchi; Jonathan H Chen

doi:10.1038/s43856-022-00094-8

Personalized antibiograms for machine learning driven antibiotic selection

Commun Med (Lond). 2022 Apr 8:2:38. doi: 10.1038/s43856-022-00094-8. eCollection 2022.

Authors

Conor K Corbin¹, Lillian Sung², Arhana Chattopadhyay¹, Morteza Noshad¹, Amy Chang³, Stanley Deresinksi³, Michael Baiocchi¹, Jonathan H Chen¹

Affiliations

¹ Center of Biomedical Informatics Research, Stanford University, Stanford, CA USA.
² Division of Haematology/Oncology, The Hospital for Sick Children, Toronto, ON M5G1X8 Canada.
³ Medicine and Infectious Diseases, Stanford Medicine, Stanford, CA USA.

Abstract

Background: The Centers for Disease Control and Prevention identify antibiotic prescribing stewardship as the most important action to combat increasing antibiotic resistance. Clinicians balance broad empiric antibiotic coverage vs. precision coverage targeting only the most likely pathogens. We investigate the utility of machine learning-based clinical decision support for antibiotic prescribing stewardship.

Methods: In this retrospective multi-site study, we developed machine learning models that predict antibiotic susceptibility patterns (personalized antibiograms) using electronic health record data of 8342 infections from Stanford emergency departments and 15,806 uncomplicated urinary tract infections from Massachusetts General Hospital and Brigham & Women's Hospital in Boston. We assessed the trade-off between broad-spectrum and precise antibiotic prescribing using linear programming.

Results: We find in Stanford data that personalized antibiograms reallocate clinician antibiotic selections with a coverage rate (fraction of infections covered by treatment) of 85.9%; similar to clinician performance (84.3% p = 0.11). In the Boston dataset, the personalized antibiograms coverage rate is 90.4%; a significant improvement over clinicians (88.1% p < 0.0001). Personalized antibiograms achieve similar coverage to the clinician benchmark with narrower antibiotics. With Stanford data, personalized antibiograms maintain clinician coverage rates while narrowing 69% of empiric vancomycin+piperacillin/tazobactam prescriptions to piperacillin/tazobactam. In the Boston dataset, personalized antibiograms maintain clinician coverage rates while narrowing 48% of ciprofloxacin to trimethoprim/sulfamethoxazole.

Conclusions: Precision empiric antibiotic prescribing with personalized antibiograms could improve patient safety and antibiotic stewardship by reducing unnecessary use of broad-spectrum antibiotics that breed a growing tide of resistant organisms.

Keywords: Antibiotics; Bacterial infection; Disease prevention; Epidemiology.

Grants and funding

R25 AI147369/AI/NIAID NIH HHS/United States