AI Models Breakthrough: Predicting Sepsis Risk in Children

Study validates AI models for preemptive sepsis care in pediatrics

Ann & Robert H. Lurie Children's Hospital of ChicagoOct 13 2025 Sepsis, or infection causing life-threatening organ dysfunction, is a leading cause of death in children worldwide. In efforts to prevent this rare but critical condition, researchers developed and validated AI models that accurately identify children at high risk for sepsis within 48 hours, so that early preemptive care can be provided. These predictive models used routine electronic health record (EHR) data from the first four hours the child spent in the Emergency Department (ED), before organ dysfunction was present. The multi-center study, led by Elizabeth Alpern, MD, MSCE, from Ann & Robert H. Lurie Children's Hospital of Chicago, is the first to use AI models to predict sepsis in children based on the new Phoenix Sepsis Criteria. Findings were published in JAMA Pediatrics. The predictive models we developed are a huge step toward precision medicine for sepsis in children. These models showed robust balance in identifying children in the ED who will later develop sepsis, without overidentifying those who are not at risk. This is very important because we want to avoid aggressive treatment for children who don't need it." Dr. Elizabeth Alpern, lead author and Division Head of Emergency Medicine at Lurie Children's, Professor of Pediatrics at Northwestern University Feinberg School of Medicine The study included five health systems contributing to the Pediatric Emergency Care Applied Research Network (PECARN), which provided Dr. Alpern and colleagues access to a large dataset and diverse population. Children with sepsis already at arrival or within the first hours of ED care were excluded, focusing the goal of the study on predicting sepsis, to allow for early initiation of therapies that have been proven as lifesaving. "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." This project work was supported by the National Institute of Child Health and Human Development (NICHD) grant R01HD087363. Dr. Alpern holds the George M. Eisenberg Professorship in Pediatrics at Northwestern University Feinberg School of Medicine. Source: Ann & Robert H. Lurie Children's Hospital of Chicago Journal reference: Alpern, E. R., et al. (2025). Derivation and Validation of Predictive Models for Early Pediatric Sepsis. JAMA Pediatrics. doi.org/10.1001/jamapediatrics.2025.3892

AI Models Predict Sepsis in Children, Allow Preemptive Care | Newswise

Newswise -- Sepsis, or infection causing life-threatening organ dysfunction, is a leading cause of death in children worldwide. In efforts to prevent this rare but critical condition, researchers developed and validated AI models that accurately identify children at high risk for sepsis within 48 hours, so that early preemptive care can be provided. These predictive models used routine electronic health record (EHR) data from the first four hours the child spent in the Emergency Department (ED), before organ dysfunction was present. The multi-center study, led by Elizabeth Alpern, MD, MSCE, from Ann & Robert H. Lurie Children's Hospital of Chicago, is the first to use AI models to predict sepsis in children based on the new Phoenix Sepsis Criteria. Findings were published in JAMA Pediatrics. "The predictive models we developed are a huge step toward precision medicine for sepsis in children," said 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. This is very important because we want to avoid aggressive treatment for children who don't need it." The study included five health systems contributing to the Pediatric Emergency Care Applied Research Network (PECARN), which provided Dr. Alpern and colleagues access to a large dataset and diverse population. Children with sepsis already at arrival or within the first hours of ED care were excluded, focusing the goal of the study on predicting sepsis, to allow for early initiation of therapies that have been proven as lifesaving. "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." This project work was supported by the National Institute of Child Health and Human Development (NICHD) grant R01HD087363. Dr. Alpern holds the George M. Eisenberg Professorship in Pediatrics at Northwestern University Feinberg School of Medicine. Ann & Robert H. Lurie Children's Hospital of Chicago is a nonprofit organization committed to providing access to exceptional care for every child. It is the only independent, research-driven children's hospital in Illinois and one of less than 35 nationally. This is where the top doctors go to train, practice pediatric medicine, teach, advocate, research and stay up to date on the latest treatments. Exclusively focused on children, all Lurie Children's resources are devoted to serving their needs. Research at Lurie Children's is conducted through Stanley Manne Children's Research Institute, which is focused on improving child health, transforming pediatric medicine and ensuring healthier futures through the relentless pursuit of knowledge. Lurie Children's is the pediatric training ground for Northwestern University Feinberg School of Medicine. It is ranked as one of the nation's top children's hospitals by U.S. News & World Report. Emergency medicine-focused research at Lurie Children's is conducted through the Grainger Research Program in Pediatric Emergency Medicine.

AI Might Help Predict Sepsis Risk Among Sick Kids

By Dennis Thompson HealthDay ReporterWEDNESDAY, Oct. 15, 2025 (HealthDay News) -- A newly trained AI might be able to help identify children who are at risk of sepsis within the next 48 hours, researchers say. The AI pointed out kids at risk for an infection leading to life-threatening organ dysfunction, after being trained on more than 1.6 million medical records, researchers reported Oct. 13 in JAMA Pediatrics. "The predictive models we developed are a huge step toward precision medicine for sepsis in children," said lead researcher Dr. Elizabeth Alpern, division head of emergency medicine at Ann & Robert H. Lurie Children's Hospital of Chicago. "These models showed robust balance in identifying children in the ED who will later develop sepsis, without overidentifying those who are not at risk," Alpern added in a news release. "This is very important because we want to avoid aggressive treatment for children who don't need it." More than 75,000 children are hospitalized with sepsis every year, with death rates as high as 20%, researchers said in background notes. Sepsis occurs when the immune system overreacts to an infection, causing an inflammatory cascade that can lead to organ failure, experts explained. Current checklists can help doctors predict which children with sepsis are more likely to die, but none has been developed that can show which kids are at higher risk for sepsis in the first place, researchers said. "There is a distinct and important role for both prediction of sepsis and for triggers that help identify patients who already have sepsis to allow for early treatment," researchers wrote. To try to fill this gap, researchers fed an AI tool data from children 2 months to 18 years old who went to the ERs of five major U.S. health systems for treatment between January 2016 and February 2020. The team specifically excluded children who went into the ER with sepsis or who were diagnosed with sepsis within a couple hours. The AI reviewed each patient based on their vitals, triage results and prior conditions that might increase the likelihood of sepsis. The researchers then tested the accuracy of the AI using another nearly 720,000 ER visits that occurred in 2021 and 2022. Results showed that the AI could accurately highlight patients who were more likely to develop sepsis. However, the AI had a lower positive predictive value - the probability that people with a positive test result actually have the disease. "Limited positive predictive values underscore the difficulty in predicting the rare outcome of pediatric sepsis in the ED," researchers noted. "We evaluated our models to ensure that there were no biases," Alpern said. "Future research will need to combine [electronic health record]-based AI models with clinician judgment to make even better predictions." More information The Cleveland Clinic has more about sepsis. SOURCES: Ann & Robert H. Lurie Children's Hospital of Chicago, news release, Oct. 13, 2025; JAMA Pediatrics, Oct. 13, 2025