Accuracy of the Epic Sepsis Prediction Model in a Regional Health System
Tellen Bennett, Seth Russell, James King, Lisa Schilling, Chan Voong, Nancy Rogers, Bonnie Adrian, Nicholas Bruce, Debashis Ghosh
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Interest in an electronic health record-based computational model that can accurately predict a patient's risk of sepsis at a given point in time has grown rapidly in the last several years. Like other EHR vendors, the Epic Systems Corporation has developed a proprietary sepsis prediction model (ESPM). Epic developed the model using data from three health systems and penalized logistic regression. Demographic, comorbidity, vital sign, laboratory, medication, and procedural variables contribute to the model. The objective of this project was to compare the predictive performance of the ESPM with a regional health system's current Early Warning Score-based sepsis detection program.