An Open Demonstrator for an Interoperable Clinical Decision Support System for the Detection of Systemic Inflammation and Sepsis in Pediatric Intensive Care

Background: Sepsis is a life-threatening condition triggered by infection and associated with dysregulated immune response of the patient followed often by multiorgan dysfunction or failure. In the clinical evolution of sepsis towards organ dysfunction, early initiation of a suited therapy significantly increases patient outcomes and reduces mortality rates. Since electronic health records provide data in a machine-readable format, this process could be supported by computerized systems. Methods: We developed an interoperable, time-sensitive CDSS that able to detect systemic inflammation and the different classifications of sepsis (bacterial/viral, suspected/proven, on admission/PICU acquired) in pediatric patients based on the analysis of routine clinical data. This application is provided as part of this publication as an open demonstrator (web application), and the usability and accuracy of the CDSS is shown by a retrospective creation of sepsis outcome labels for a routine data set of 4,655 pediatric patients. As a reference standard, the patients were manually assessed by blinded clinical experts. Results: In comparison with the reference standard, the CDSS achieved sensitivity of 96.9% (95% CI: 80.9-99.6%) and specificity of 99.1% (95% CI: 95.1-99.8%). In the context of a sepsis outcome labeling for 4,655 patients, the CDSS detected 4,342 episodes of inflammation of which 1,723 were classified as sepsis. Conclusions: We demonstrated that our routine-data based CDSS is able to perform a complex sepsis detection process with high diagnostic accuracy. Such CDSS with the ability to differentiate between SIRS, sepsis on admission, suspected and proven sepsis can prospectively support clinical management, monitoring and quality management.