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A Quantitative Study of Decision Thresholds for Initiation of Antibiotics in Suspected Sepsis

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  • Stephanie Parks Taylor

    (Department of Internal Medicine, Wake Forest University School of Medicine, Atrium Health, Charlotte NC, USA
    Critical Illness Injury and Recovery Research Center, Wake Forest School of Medicine, Charlotte NC, USA
    Center for Outcomes Research and Evaluation, Atrium Health, Charlotte NC, USA)

  • Gary E. Weissman

    (Palliative and Advanced Illness Research (PAIR) Center University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
    Pulmonary, Allergy, and Critical Care Division University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
    Leonard Davis Institute of Health Economics, And Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA)

  • Marc Kowalkowski

    (Center for Outcomes Research and Evaluation, Atrium Health, Charlotte NC, USA)

  • Andrew J. Admon

    (Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
    Pulmonary Service, LTC Charles S. Kettles VA Medical Center, Ann Arbor, MI, USA
    Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA)

  • Sable Skewes

    (Department of Internal Medicine, Wake Forest University School of Medicine, Atrium Health, Charlotte NC, USA)

  • Yunfei Xia

    (Department of Mathematics and Statistics, University of North Carolina, Charlotte, NC, USA)

  • Shih-Hsuing Chou

    (Center for Outcomes Research and Evaluation, Atrium Health, Charlotte NC, USA)

Abstract

Background Clinicians’ decision thresholds for initiating antibiotics in patients with suspected sepsis have not been quantified. We aimed to define an average threshold of infection likelihood at which clinicians initiate antibiotics when treating a patient with suspected infection and to evaluate the influence of severity of illness and clinician-related factors on the threshold. Design This was a prospective survey of 153 clinicians responding to 8 clinical vignettes constructed from real-world data from 3 health care systems in the United States. We treated each hour in the vignette as a decision to treat or not treat with antibiotics and assigned an infection probability to each hour using a previously developed infection prediction model. We then estimated decision thresholds using regression models based on the timing of antibiotic initiation. We compared thresholds across categories of severity of illness and clinician-related factors. Results Overall, the treatment threshold occurred at a 69% probability of infection, but the threshold varied significantly across severity of illness categories—when patients had high severity of illness, the treatment threshold occurred at a 55% probability of infection; when patients had intermediate severity, the threshold for antibiotic initiation occurred at an infection probability of 69%, and the threshold was 84% when patients had low severity of illness ( P

Suggested Citation

  • Stephanie Parks Taylor & Gary E. Weissman & Marc Kowalkowski & Andrew J. Admon & Sable Skewes & Yunfei Xia & Shih-Hsuing Chou, 2023. "A Quantitative Study of Decision Thresholds for Initiation of Antibiotics in Suspected Sepsis," Medical Decision Making, , vol. 43(2), pages 175-182, February.
    • Handle: RePEc:sae:medema:v:43:y:2023:i:2:p:175-182
    DOI: 10.1177/0272989X221121279
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    References listed on IDEAS

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    1. Carlos M. Plasencia & Beth W. Alderman & Anna E. Barón & Robert T. Rolfs & Edward J. Boyko, 1992. "A Method to Describe Physician Decision Thresholds and Its Application in Examining the Diagnosis of Coronary Artery Disease Based on Exercise Treadmill Testing," Medical Decision Making, , vol. 12(3), pages 204-212, August.
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