Abstract
INTRODUCTION: Clinically useful predictions of end-organ function and failure in severe sepsis may be possible through analyzing the interactions among demographics, physiologic parameters, standard laboratory tests, and circulating markers of inflammation. The present study evaluated the ability of such a methodology, the Systemic Mediator Associated Response Test (SMART), to predict the clinical course of septic surgery patients from a database of medical and surgical patients with severe sepsis and/or septic shock. PATIENTS AND METHODS: Three hundred and three patients entered into the placebo arm of a multi-institutional sepsis study were randomly assigned to a model-building cohort (n = 200; 119 surgical) or to a predictive cohort (n = 103; 55 surgical). Using baseline and baseline plus serial measurements of physiologic data, standard laboratory tests, and plasma levels of IL-6, IL-8, and granulocyte colony-stimulating factor (GCSF), multivariate models were developed that predicted the presence or absence of pulmonary edema on chest radiography, and respiratory, renal, coagulation, hepatobiliary, or central nervous system dysfunction and shock in individual patients. Twenty-eight-day survival was predicted also in baseline plus serial data models. These models were validated prospectively by inserting baseline raw data from the 55 surgical patients in the predictive cohort into the models built on the comprehensive training cohort, and calculating the area under the curve (AUC) of predicted versus observed receiver operator characteristic (ROC) plots. RESULTS: SMART predictions of physiologic, respiratory, metabolic, hepatic, renal, and hematologic function indicators were validated prospectively, frequently at clinically useful levels of accuracy. ROC AUC values above 0.700 were achieved in 30 out of 49 (61%) of SMART baseline models in predicting shock and organ failure up to 7 days in advance, and in 30 out of 54 (56%) of baseline plus serial data models. CONCLUSION: SMART multivariate models accurately predict pathophysiology, shock, and organ failure in individual septic surgical patients. These prognostications may facilitate early treatment of end-organ dysfunction in surgical sepsis.