Written by Joshua Belfer
Using EHR data from the first 4 hours of ED care, machine learning models accurately predicted which children would develop sepsis within 48 hours—before organ dysfunction was present.
Predicting sepsis before the crash
Early sepsis recognition remains one of the greatest challenges for pediatric emergency care. In this PECARN-led multicenter study utilizing EHR data from over 2.3 million pediatric ED visits, machine learning models that predict future sepsis were developed and validated.
Using routinely collected ED data (vital signs, ESI, age, markers of medical complexity), two different models (logistic regression model and gradient tree boosting model) performed exceptionally well. The area under the receiver operating characteristic (AUROC) was 0.92 (95%CI 0.92–0.93) for the logistic regression model and 0.94 (95%CI 0.93–0.94) for the gradient tree boosting model. Additionally, the models had similarly high AUROCs when looking at septic shock. At a prespecified sensitivity of 90%, the gradient boosting model achieved a positive likelihood ratio of at least 4.7 for sepsis and 4.2 for septic shock.
Model performance was consistent across most demographic groups. However, the gradient tree boosting model, while the best-performing, is also complex, which could limit scalability.
How will this change my practice?
If recognition of sepsis is the cornerstone of the pediatric ER, then prediction of this life-threatening condition would be a paradigm shift. By predicting future Phoenix Sepsis Criteria-defined sepsis, the models created in this paper point toward a future where risk stratification mirrors our current sepsis definitions. This sets the stage for a world in which AI models can assist in recognizing and treating impending sepsis before patients even show signs of organ dysfunction.
AI models analyze and find patterns in large datasets better than humans. The EHR is ripe for this type of study. This may help us not only with sepsis but other conditions that are time-sensitive, diagnostically noisy, and rely on patterns across multiple (sometimes weak) signals. Appendicitis? Intussusception? DKA? I look forward to future work that pairs this kind of model with clinical gestalt to improve outcomes.
Source
Derivation and Validation of Predictive Models for Early Pediatric Sepsis. JAMA Pediatr. 2025 Dec 1;179(12):1318-1325. doi: 10.1001/jamapediatrics.2025.3892. PMID: 41082207; PMCID: PMC12519407.
