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Machine learning for the prediction of antimicrobial stewardship intervention in hospitalized patients receiving broad-spectrum agents

Published online by Cambridge University Press:  18 June 2020

Rachel J. Bystritsky Open the ORCID record for Rachel J. Bystritsky [Opens in a new window] ,
Alex Beltran ,
Albert T. Young ,
Andrew Wong ,
Xiao Hu  and
Sarah B. Doernberg
Rachel J. Bystritsky*
Affiliation:
Department of Medicine, Infectious Diseases, University of California–San Francisco, San Francisco, California
Alex Beltran
Affiliation:
Department of Bioengineering, University of California–San Francisco, San Francisco, California
Albert T. Young
Affiliation:
School of Medicine, University of California–San Francisco, San Francisco, California
Andrew Wong
Affiliation:
School of Medicine, University of California–San Francisco, San Francisco, California
Xiao Hu
Affiliation:
Department of Bioengineering, University of California–San Francisco, San Francisco, California
Sarah B. Doernberg
Affiliation:
Department of Medicine, Infectious Diseases, University of California–San Francisco, San Francisco, California
*
Author for correspondence: Rachel J. Bystritsky, E-mail: Rachel.Bystritsky@ucsf.edu
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Abstract

Objective:

A significant proportion of inpatient antimicrobial prescriptions are inappropriate. Post-prescription review with feedback has been shown to be an effective means of reducing inappropriate antimicrobial use. However, implementation is resource intensive. Our aim was to evaluate the performance of traditional statistical models and machine-learning models designed to predict which patients receiving broad-spectrum antibiotics require a stewardship intervention.

Methods:

We performed a single-center retrospective cohort study of inpatients who received an antimicrobial tracked by the antimicrobial stewardship program. Data were extracted from the electronic medical record and were used to develop logistic regression and boosted-tree models to predict whether antibiotic therapy required stewardship intervention on any given day as compared to the criterion standard of note left by the antimicrobial stewardship team in the patient’s chart. We measured the performance of these models using area under the receiver operating characteristic curves (AUROC), and we evaluated it using a hold-out validation cohort.

Results:

Both the logistic regression and boosted-tree models demonstrated fair discriminatory power with AUROCs of 0.73 (95% confidence interval [CI], 0.69–0.77) and 0.75 (95% CI, 0.72–0.79), respectively (P = .07). Both models demonstrated good calibration. The number of patients that would need to be reviewed to identify 1 patient who required stewardship intervention was high for both models (41.7–45.5 for models tuned to a sensitivity of 85%).

Conclusions:

Complex models can be developed to predict which patients require a stewardship intervention. However, further work is required to develop models with adequate discriminatory power to be applicable to real-world antimicrobial stewardship practice.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 41 , Issue 9 , September 2020 , pp. 1022 - 1027
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Copyright
© 2020 by The Society for Healthcare Epidemiology of America. All rights reserved.

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