Accuracy of the Epic Sepsis Prediction Model in a Regional Health System
This work addresses the need for accurate sepsis prediction in healthcare systems, but it is incremental as it compares a proprietary model to an existing method without introducing new techniques.
The study evaluated the Epic Sepsis Prediction Model (ESPM) against a regional health system's existing Early Warning Score-based program for sepsis detection, finding that the ESPM demonstrated moderate predictive performance with an area under the receiver operating characteristic curve of 0.76 and sensitivity of 0.67 at a specificity of 0.70.
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.