title: Development of a model-based clinical sepsis biomarker for critically ill patients.
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.
authors.affiliation: University of Otago Christchurch, Department of Medicine, Christchurch, New Zealand. jessica.lin@otago.ac.nz
authors.forename: Jessica
authors.initials: J
authors.lastname: Lin
authors.forename: Jacquelyn D
authors.initials: JD
authors.lastname: Parente
authors.forename: J Geoffrey
authors.initials: JG
authors.lastname: Chase
authors.forename: Geoffrey M
authors.initials: GM
authors.lastname: Shaw
authors.forename: Amy J
authors.initials: AJ
authors.lastname: Blakemore
authors.forename: Aaron J
authors.initials: AJ
authors.lastname: Lecompte
authors.forename: Christopher
authors.initials: C
authors.lastname: Pretty
authors.forename: Normy N
authors.initials: NN
authors.lastname: Razak
authors.forename: Dominic S
authors.initials: DS
authors.lastname: Lee
authors.forename: Christopher E
authors.initials: CE
authors.lastname: Hann
authors.forename: Sheng-Hui
authors.initials: SH
authors.lastname: Wang
mesh_terms: D015415:Biomarkers / Q000097:blood; D001786:Blood Glucose / Q000378:metabolism; D003198:Computer Simulation; D016638:Critical Illness; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D007333:Insulin Resistance; D008954:Models, Biological; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D012372:ROC Curve; D018805:Sepsis / Q000097:blood / Q000175:diagnosis* / Q000503:physiopathology
chemical_list: D015415:Biomarkers; D001786:Blood Glucose