Sepsis, a life-threatening condition where infection leads to organ dysfunction, is a leading cause of childhood mortality worldwide. Because symptoms often escalate rapidly, early identification and treatment are crucial. In this new study, researchers developed and validated AI models that analyze electronic health record (EHR) data collected within the first four hours of a child’s emergency department (ED) visit, before any signs of organ dysfunction are apparent.
The study drew on data from five major health systems participating in the Pediatric Emergency Care Applied Research Network (PECARN), giving researchers access to a large and diverse pediatric population. Importantly, the study excluded children who already showed signs of sepsis upon arrival, focusing solely on predicting future cases. This approach is designed to enable early, targeted therapies that have been proven to save lives.
“The predictive models we developed are a huge step toward precision medicine for sepsis in children,” Dr. Alpern, lead author and Division Head of Emergency Medicine at Lurie Children’s, as well as Professor of Pediatrics at Northwestern University Feinberg School of Medicine. “These models showed robust balance in identifying children in the ED who will later develop sepsis, without overidentifying those who are not at risk.”
Researchers emphasized the importance of avoiding unnecessary aggressive treatment in low-risk children, while enabling faster care for those at true risk. “We evaluated our models to ensure that there were no biases,” said Dr. Alpern. “Future research will need to combine EHR-based AI models with clinician judgment to make even better predictions.”
Reference: Alpern ER, Scott HF, Balamuth F, et al. Derivation and Validation of Predictive Models for Early Pediatric Sepsis. JAMA Pediatr. Published online October 13, 2025. doi:10.1001/jamapediatrics.2025.3892
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