Implement and PREDICT Shock: An Implementation Trial of Predictive Modeling to Enhance Diagnosis and Improve Critical Treatment in Pediatric Septic Shock

Septic shock is a leading global cause of pediatric death. In the US, the in-hospital mortality rate for children with sepsis is 5-20%. Septic shock is a state of critical infection that requires advanced and resource-intensive resuscitation, and morbidity-free survival depends on timely diagnosis. Critical care delivered in a delayed fashion, after a child is in hypotensive shock, is less effective; for each hour of unrecognized shock the odds of death more than double. Advances have been made in timely sepsis treatment, but improving diagnosis of septic shock in children remains elusive. Improved early diagnosis would accelerate treatment and improve outcomes.

Tools that have been deployed to improve diagnosis in pediatric sepsis either diagnose it after organ dysfunction criteria have been met, or depend heavily on subspecialty physician judgment and have not been tested outside of tertiary pediatric hospitals. Thus, the evidence-based 2020 Surviving Sepsis Children's Guidelines for pediatric sepsis stated that "high-quality trials on pediatric sepsis recognition are lacking, and data are not sufficient to suggest any particular screening tool," and identified pediatric sepsis recognition trials as an important research need. Despite this, the guidelines weakly recommended screening patients "who present as acutely unwell" for septic shock, citing very low quality evidence. The guidelines also stated that there is no evidence for the effectiveness of any existing pediatric sepsis screening tools.

This study addresses the gap in knowledge about the effectiveness of pediatric sepsis prediction tools. The study team has developed and retrospectively validated early diagnostic models that leverage clinical data in the Electronic Health Record (EHR) to predict septic shock in children the emergency setting [1, 2]. In order to address concerns about alert fatigue and antibiotic overuse, these predictive models were designed to identify patients at high risk for shock among patients in whom clinicians initially had some suspicion for sepsis.

In this pilot implementation trial, Clinical Decision Support based on septic shock prediction models will be introduced to 4 pediatric Emergency Departments (EDs) within the Children's Hospital Colorado care system in a stepped wedge design, in addition to routine clinical care. Routine clinical care at all study sites includes existing sepsis pathways, order sets, and quality metrics that are aligned with the national Improving Pediatric Sepsis Outcomes Collaborative and the Pediatric Surviving Sepsis guidelines. The primary outcome will be the proportion of sepsis patients who receive guideline-concordant septic shock care after implementation of the CDS, and the secondary outcome will be time-to-antibiotic after sepsis recognition.