A Two-Biomarker Model Predicts Mortality in the Critically Ill with Sepsis.

RATIONALE

Improving the prospective identification of patients with systemic inflammatory response syndrome (SIRS) and sepsis at low risk for organ dysfunction and death is a major clinical challenge.

OBJECTIVES

To develop and validate a multibiomarker-based prediction model for 28-day mortality in critically ill patients with SIRS and sepsis.

METHODS

A derivation cohort (n = 888) and internal test cohort (n = 278) were taken from a prospective study of critically ill intensive care unit (ICU) patients meeting two of four SIRS criteria at an academic medical center for whom plasma was obtained within 24 hours. The validation cohort (n = 759) was taken from a prospective cohort enrolled at another academic medical center ICU for whom plasma was obtained within 48 hours. We measured concentrations of angiopoietin-1, angiopoietin-2, IL-6, IL-8, soluble tumor necrosis factor receptor-1, soluble vascular cell adhesion molecule-1, granulocyte colony-stimulating factor, and soluble Fas.

MEASUREMENTS AND MAIN RESULTS

We identified a two-biomarker model in the derivation cohort that predicted mortality (area under the receiver operator characteristic curve [AUC], 0.79; 95% confidence interval [CI], 0.74-0.83). It performed well in the internal test cohort (AUC, 0.75; 95% CI, 0.65-0.85) and the external validation cohort (AUC, 0.77; 95% CI, 0.72-0.83). We determined a model score threshold demonstrating high negative predictive value (0.95) for death. In addition to a low risk of death, patients below this threshold had shorter ICU length of stay, lower incidence of acute kidney injury, acute respiratory distress syndrome, and need for vasopressors.

CONCLUSIONS

We have developed a simple, robust biomarker-based model that identifies patients with SIRS/sepsis at low risk for death and organ dysfunction.