Development and Validation of an Artificial Intelligence Predictive Model to Accelerate Antibiotic Therapy for Critical Ill Children with Sepsis in the Pediatric ED with Pediatric ICU Disposition

ImportancePediatric sepsis accounts for over 72,000 US hospitalizations annually with significant mortality and morbidity. Many pediatric hospitals struggle to promptly identify and treat sepsis. This study demonstrates the feasibility of a multi-tiered artificial intelligence (AI) to enhance sepsis clinical decision-making within a complex emergency department (ED) workflow. ObjectivesTo develop and validate a local AI model predicting critical sepsis among ED patients who received a fluid bolus and a disposition to the Pediatric Intensive Care Unit (PICU) but had not yet received antibiotics. DesignRetrospective observational cross-section study SettingUrban, quaternary-care, academic healthcare system PatientsPediatric ED patients InterventionsNone Measures and Main ResultsThe "Sepsis on ED to PICU Disposition" (SEPD) model aimed to predict critical sepsis within 72 hours of PICU disposition using a dataset totaling 5,534 patient encounters for model training and testing. During silent implementation, 1,058 encounters were used for validation. The SEPD model outperformed a vendor-developed sepsis model with an AUROC of 81.8%, compared to 57.5%. The model also demonstrated better precision-recall performance, showing more balanced identification of true positives. During silent implementation, the SEPD model maintained similar sensitivity (85.29%) and specificity (60.45%) to those observed during model testing. ConclusionThe SEPD model improved detection of critical sepsis among high-risk pediatric ED patients with a known PICU disposition, outperforming a vendor-developed sepsis model. Within a complex ED workflow, this model may facilitate timely sepsis identification and treatment in critically ill patients, who may have been missed during earlier stages of their ED course.