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Clinical Decision Support for Septic Shock in the Emergency Department: A Cluster Randomized Trial

Halden F Scott1,2, Carter J Sevick3, Kathryn L Colborn3,4

  • 1Section of Pediatric Emergency Medicine, Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado.

Pediatrics
|June 9, 2025
PubMed

Insights

Clinical decision support (CDS) using machine learning did not improve early septic shock treatment in children. While feasible and accepted by providers, the system did not reduce the progression to hypotensive septic shock.

Area of Science:

  • Pediatric Emergency Medicine
  • Clinical Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Septic shock diagnosis delays in children contribute to preventable mortality.
  • Limited evidence exists for effective early recognition strategies in pediatric sepsis.
  • Machine learning-based clinical decision support (CDS) was hypothesized to improve early recognition and treatment.

Purpose of the Study:

  • To evaluate the effectiveness of machine learning-based CDS in increasing the proportion of children receiving timely septic shock treatment.
  • To assess the impact of CDS on key clinical outcomes, including time to antibiotics and development of hypotensive septic shock.
  • To determine the feasibility and provider acceptance of implementing predictive CDS in pediatric emergency departments.

Main Methods:

  • A prospective, stepped-wedge, cluster randomized trial was conducted across 4 pediatric emergency departments.
  • The CDS utilized machine learning models to identify high-risk pediatric patients for sepsis based on electronic health record data.
  • Effectiveness was measured by the proportion of patients receiving antibiotics and fluid bolus within 1 hour of sepsis suspicion.

Main Results:

  • No significant difference was observed in the primary outcome (antibiotic and bolus within 1 hour) between the intervention and control groups (39.0% vs. 38.9%).
  • The CDS did not significantly impact secondary outcomes, including the incidence of hypotensive septic shock or antibiotic timeliness.
  • Providers found the CDS valuable and unobtrusive, indicating good adoption and maintenance post-trial.

Conclusions:

  • Implementing predictive CDS for pediatric sepsis is feasible and acceptable to healthcare providers.
  • The studied CDS, despite its acceptance, did not improve the rate of early treatment for suspected sepsis or prevent progression to hypotensive shock.
  • Further research may be needed to refine predictive models or implementation strategies for improving pediatric septic shock outcomes.
Abstract

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