Optimization of sepsis risk assessment for ward patients

Abstract

Sepsis is the systemic response to infection complicated by organ dysfunction. It is a leading cause of in-hospital mortality, with an observed mortality of 20-40%, and is associated with significantly high costs. Furthermore, patients who survive sepsis are more likely to have permanent organ damage, cognitive impairment and physical disability. Survival from sepsis is dependent on early and targeted treatment. Accepted methods for identifying and predicting sepsis have typically been based on intensive care unit (ICU) patients and founded upon a limited and outdated definition of sepsis based on systemic inflammatory response syndrome (SIRS) criteria - one which is not clinically validated and shown to miss at least 1 out of 8 cases. Recently updated consensus guidelines on sepsis have been published and offer the possibility of improved EWS and predictive models. Our goal was to identify non-ICU ward patients with sepsis earlier and more accurately using the newly established sepsis definition and improved predictive models. Using multivariate logistic regression and routinely available physiological and laboratory data from electronic health records (EHRs), we derived an EWS that identifies at-risk ward patients 12-24 hours prior to sepsis onset with an area under the receiver operating characteristic curve (AUC) result of 74%. This model, when applied on a separate ICU population, achieved an AUC curve result of 56%, indicating the model has worse performance in this setting.