Development of a model-based clinical sepsis biomarker for critically ill patients

• Published in: Computer methods and programs in biomedicine 2011-05, Vol.102 (2), p.149-155
• Main Authors: Lin, Jessica, Parente, Jacquelyn D, Chase, J. Geoffrey, Shaw, Geoffrey M, Blakemore, Amy J, LeCompte, Aaron J, Pretty, Christopher, Razak, Normy N, Lee, Dominic S, Hann, Christopher E, Wang, Sheng-Hui
• Format: Article
• Language: English
• Subjects: Biomarker; Biomarkers - blood; Blood Glucose - metabolism; Computer Simulation; Critical Illness; Diagnosis; Diagnosis, Computer-Assisted; Glucose control; Humans; Insulin Resistance; Insulin sensitivity; Internal Medicine; Models, Biological; Multivariate Analysis; Other; Predictive Value of Tests; Real-time clinical application; Receiver operator characteristic; ROC Curve; Sepsis; Sepsis - blood; Sepsis - diagnosis; Sepsis - physiopathology
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2010.04.002

Abstract 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 h. 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. Hourly model-based insulin sensitivity S I values were calculated from glycemic control data of 36 patients with sepsis. The hourly S I is compared to the hourly sepsis score (ss) for these patients (ss = 0–4 for increasing severity). A multivariate clinical biomarker was also developed to maximize the discrimination between different ss groups. Receiver operator characteristic (ROC) curves for severe sepsis (ss ≥ 2) are created for both S I and the multivariate clinical biomarker. Insulin sensitivity as a sepsis biomarker for diagnosis of severe sepsis achieves a 50% sensitivity, 76% specificity, 4.8% positive predictive value (PPV), and 98.3% negative predictive value (NPV) at an S I cut-off value of 0.00013 L/mU/min. Multivariate 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, the multivariate 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 shows potential avenues to improve the positive predictive value.