Predictive Models of Sepsis in Adult ICU Patients

Abstract

Sepsis is a dysregulated systemic response to infection that causes organ dysfunction. It is the leading cause of in-hospital mortality and is responsible for the longest, most expensive, hospital stays in the United States. Prognostic modeling of sepsis has demonstrated potential for improving treatment and outcomes for critical care patients. However, the majority of previous models have relied on an outdated definition of sepsis based on systemic inflammatory response syndrome (SIRS). Therefore, this study sought to build predictive models of sepsis using the most recent definition of sepsis, Sepsis-3. A total of three classification methods, including logistic regression (LR), support vector machines (SVM), and logistic model trees (LMT), were used to predict onset of sepsis in adult Intensive Care Unit (ICU) patients using vital signs and blood culture results. For patients who did not develop sepsis, predictor values were selected from a random 48-hour time window during the patient's ICU stay. For those who developed sepsis, a random time was selected for the patient within 48 to 6 hours prior to onset of sepsis, and the predictor values with the closest preceding time were extracted. The LMT produced superior classification performance compared with the LR and SVM. The wide time window used for data extraction improves the clinical utility of the models relative to those built in previous work. However, while the models showed similar sensitivity and specificity to pr…