Prediction of Impending Septic Shock in Children With Sepsis

Ran Liu1,2, Joseph L Greenstein1,2, James C Fackler3

  • 1Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD.

Insights

This study adapted a risk score to predict pediatric septic shock, providing an early warning and stratifying patients into risk groups. Early prediction of pediatric septic shock improves patient outcomes.

Area of Science:

  • Pediatric critical care medicine
  • Clinical informatics
  • Biostatistics

Background:

  • Sepsis and septic shock are major causes of in-hospital mortality in children.
  • Timely intervention is critical, but treatment delays are common.
  • Early prediction of septic shock progression in pediatric patients is needed to improve outcomes.

Purpose of the Study:

  • To adapt a previously developed time-evolving risk score for adult patients to predict septic shock in pediatric sepsis patients.
  • To determine if these risk scores can stratify patients into distinct temporal evolution groups.
  • To enhance the actionable window for interventions in pediatric sepsis.

Main Methods:

  • Retrospective cohort study of 6,161 pediatric patients admitted to a pediatric intensive care unit (PICU).
  • Development and application of time-evolving risk models to predict transition to septic shock.
  • Utilized spectral clustering to stratify patients based on risk score trajectories.

Main Results:

  • Achieved an area under the receiver operating curve of 0.90 for early prediction of septic shock.
  • Obtained a median early warning time of 8.9 hours.
  • Identified two distinct patient clusters with differing septic shock prevalence, mortality, and fluid resuscitation status.

Conclusions:

  • The methodology is applicable for early prediction and risk stratification of septic shock in pediatric sepsis.
  • Risk score evolution analysis confirmed an abrupt transition preceding septic shock onset in children.
  • Patients were successfully stratified into low- and high-risk categories based on risk score trajectories.
Abstract

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