Sepsis occurs frequently in the intensive care unit (ICU) and is a leading cause of admission, mortality, and cost. Treatment guidelines recommend early intervention, however positive blood culture results may take up to 48 hours. Insulin sensitivity (S I ) is known to decrease with worsening condition and could thus be used to aid diagnosis. Some glycemic control protocols are able to accurately identify insulin sensitivity in real-time.Receiver operator characteristic (ROC) curves and cut-off S I values for sepsis diagnosis were calculated for real-time model-based insulin sensitivity from glycemic control data of 36 patients with sepsis. Patients were identified as having sepsis based on a clinically validated sepsis score (ss) of 2 or higher (ss = 0-4 for increasing severity). A clinical biomarker was calculated from patient clinical data to maximize the discrimination between cohorts.Insulin sensitivity as a sepsis biomarker for diagnosis of severe sepsis achieves a 50% sensitivity, 76% specificity, 4.8% PPV, and 98.3% NPV at a S I cut-off value of 0.00013 L*mU min -1 . A clinical biomarker combining S I , temperature, heart rate, respiratory rate, blood pressure, and their respective hourly rates of change achieves 73% sensitivity, 80% specificity, 8.4% PPV, and 99.2% NPV. Thus, a clinical biomarker provides an effective real-time negative predictive diagnostic for severe sepsis. Examination of both inter-and intra-patient statistical distribution of this biomarker and sepsis score show potential avenues to improve the positive predictive value.