Objectives: To create and evaluate a continuous automated alert system embedded in the electronic health record for the detection of severe sepsis among pediatric inpatient and emergency department patients.
Design: Retrospective cohort study. The main outcome was the algorithm's appropriate detection of severe sepsis. Episodes of severe sepsis were identified by chart review of encounters with clinical interventions consistent with sepsis treatment, use of a diagnosis code for sepsis, or deaths. The algorithm was initially tested based upon criteria of the International Pediatric Sepsis Consensus Conference; we present iterative changes which were made to increase the positive predictive value and generate an improved algorithm for clinical use.
Setting: A quaternary care, freestanding children's hospital with 404 inpatient beds, 70 ICU beds, and approximately 60,000 emergency department visits per year PATIENTS:: All patients less than 18 years presenting to the emergency department or admitted to an inpatient floor or ICU (excluding neonatal intensive care) between August 1, 2016, and December 28, 2016.
Intervention: Creation of a pediatric sepsis screening algorithm.
Measurements and main results: There were 288 (1.0%) episodes of severe sepsis among 29,010 encounters. The final version of the algorithm alerted in 9.0% (CI, 8.7-9.3%) of the encounters with sensitivity 72% (CI, 67-77%) for an episode of severe sepsis; specificity 91.8% (CI, 91.5-92.1%); positive predictive value 8.1% (CI, 7.0-9.2%); negative predictive value 99.7% (CI, 99.6-99.8%). Positive predictive value was highest in the ICUs (10.4%) and emergency department (9.6%).
Conclusions: A continuous, automated electronic health record-based sepsis screening algorithm identified severe sepsis among children in the inpatient and emergency department settings and can be deployed to support early detection, although performance varied significantly by hospital location.