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Clinical prediction tools for identifying antimicrobial-resistant organism (ARO) carriage on hospital admissions: a systematic review

  • D. Jeon
    Affiliations
    Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada
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  • S. Chavda
    Affiliations
    Department of Medicine, University of Calgary, Calgary, AB, Canada
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  • E. Rennert-May
    Affiliations
    Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada

    Department of Medicine, University of Calgary, Calgary, AB, Canada

    Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, AB, Canada

    O’Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada

    Snyder Institute for Chronic Diseases, University of Calgary, Calgary, AB, Canada
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  • J. Leal
    Correspondence
    Corresponding author. 3E17-Cal Wenzel Precision Health Bldg, 3280 Hospital Dr NW, Calgary, AB T2N 4Z6, Canada.
    Affiliations
    Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada

    Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, AB, Canada

    O’Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada

    Infection Prevention and Control, Alberta Health Services, Calgary, AB, Canada
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Published:January 16, 2023DOI:https://doi.org/10.1016/j.jhin.2023.01.003

      Summary

      Background

      Increasing prevalence of antimicrobial-resistant organisms (AROs) is a growing economic and healthcare challenge. Increasing utilization of electronic medical record (EMR) systems and improvements in computation and analytical techniques afford an opportunity to reduce the spread of AROs through the development of clinical prediction tools to identify ARO carriers on admission to hospital.

      Aim

      To identify existing clinical prediction tools for meticillin-resistant Staphylococcus aureus (MRSA) and carbapenemase-producing organisms (CPOs), their predictive performance, and risk factors utilized in these tools.

      Methods

      The CHARMS checklist was followed. Medline, EMBASE, Cochrane SR, CRD databases (DARE, NHS EED), CINAHL and Web of Science were searched from database inception to 26th July 2021. Full-text articles were assessed independently, and quality assessment was conducted using the Prediction Model Risk of Bias Assessment Tool.

      Findings

      In total, 3809 abstracts were identified and 22 studies were included. Among these studies, risk score models were the most common prediction tool (N=16). Previous admission, recent antibiotic exposure, age and sex were the most common risk factors for ARO carriage. Prediction tools were commonly evaluated on sensitivity and specificity with ranges of 15–100% and 46–98.6%, respectively, for MRSA, and 30–81.3% and 79.8–99.9%, respectively, for CPOs.

      Conclusion

      There is no gold standard ARO prediction tool. However, high-performance clinical prediction tools and identification of key risk factors for the early detection of AROs exist. Risk score models are easier to use and interpret; however, with recent improvements in machine learning techniques, highly robust models can be developed with data stored in an EMR.

      Keywords

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