Predictive models for severe sepsis in adult ICU patients

Abstract

Intensive Care Unit (ICU) patients have significant morbidity and mortality, often from complications that arise during the hospital stay. Severe sepsis is one of the leading causes of death among these patients. Predictive models have the potential to allow for earlier detection of severe sepsis and ultimately earlier intervention. However, current methods for identifying and predicting severe sepsis are biased and inadequate. The goal of this work is to identify a new framework for the prediction of severe sepsis and identify early predictors utilizing clinical laboratory values and vital signs collected in adult ICU patients. We explore models with logistic regression (LR), support vector machines (SVM), and logistic model trees (LMT) utilizing vital signs, laboratory values, or a combination of vital and laboratory values. When applied to a retrospective cohort of ICU patients, the SVM model using laboratory and vital signs as predictors identified 339 (65%) of the 3,446 patients as developing severe sepsis correctly. Based on this new framework and developed models, we provide a recommendation for the use in clinical decision support in ICU and non-ICU environments.