Risk Factors for Pediatric Sepsis in the Emergency Department: A Machine Learning Pilot Study. Issue 2 (17th February 2023)

• Record Type: Journal Article
• Authors: Mercurio, Laura; Pou, Sovijja; Duffy, Susan; Eickhoff, Carsten
• Is Part Of: Pediatric emergency care. Volume 39:Issue 2(2023)
• Page Start: e48
• Page End: e56
• Publication Date: 2023-02-17
• Subjects: sepsis -- machine learning -- fever; Pediatric emergencies -- Periodicals; 618.92002505
• DOI: 10.1097/PEC.0000000000002893
• Languages: English
• ISSNs: 0749-5161

Abstract

Abstract : Objective: To identify underappreciated sepsis risk factors among children presenting to a pediatric emergency department (ED). Methods: A retrospective observational study (2017–2019) of children aged 18 years and younger presenting to a pediatric ED at a tertiary care children's hospital with fever, hypotension, or an infectious disease International Classification of Diseases (ICD)-10 diagnosis. Structured patient data including demographics, problem list, and vital signs were extracted for 35, 074 qualifying ED encounters. According to the Improving Pediatric Sepsis Outcomes Classification, confirmed by expert review, 191 patients met clinical sepsis criteria. Five machine learning models were trained to predict sepsis/nonsepsis outcomes. Top features enabling model performance (N = 20) were then extracted to identify patient risk factors. Results: Machine learning methods reached a performance of up to 93% sensitivity and 84% specificity in identifying patients who received a hospital diagnosis of sepsis. A random forest classifier performed the best, followed by a classification and regression tree. Maximum documented heart rate was the top feature in these models, with importance coefficients (ICs) of 0.09 and 0.21, which represent how much an individual feature contributes to the model. Maximum mean arterial pressure was the second most important feature (IC 0.05, 0.13). Immunization status (IC 0.02), age (IC 0.03), and patient zip code (IC 0.02) were also among the top features enabling models to predict sepsis from ED visit data. Stratified analysis revealed changes in the predictive importance of risk factors by race, ethnicity, oncologic history, and insurance status. Conclusions: Machine learning models trained to identify pediatric sepsis using ED clinical and sociodemographic variables confirmed well-established predictors, including heart rate and mean arterial pressure, and identified underappreciated relationships between sepsis and patient age, immunization status, and demographics.