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 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.

Interface Design and Human Factors Considerations for Model-Based Tight Glycemic Control in Critical Care

“…In particular, a great deal more data entry could be included for other uses, such as tracking organ failure as a response to TGC, 9 monitoring the impact of drug therapy, 32 or using model-based metabolic markers in sepsis or other diagnostics. 33,34 Such possible additions were not in the focus of this case study, but their addition or use could be included using a similar design approach.…”

Glycemic control in the intensive care unit: A control systems perspective

“…Perhaps more importantly, such model-based SI [19,33,85,109,110,114,117,137,154,161,[165][166][167][168][169][170][171][172][173]] can be monitored and its level and/or variation assessed relative to condition [16,18,19,153,171,[174][175][176][177][178][179][180][181][182]. Thus, if accurate, this value offers not only the potential of good, personalised control, but also further insight into patient condition.…”

Physiological modeling, tight glycemic control, and the ICU clinician: what are models and how can they affect practice?

“…In the critical care arena, the use of in silico physiological models is only emerging. However, there are already model-based or model-derived applications for managing sedation [ 25 , 26 ], cardiovascular diagnosis and therapy [ 27 , 28 ], mechanical ventilation [ 29 , 30 ], and the diagnosis of sepsis [ 31 , 32 ]. Particular to TGC, there are already some attempts at modeling for both understanding and implementing TGC [ 23 , 33 - 42 ], with a review of many in [ 43 ].…”

“…In intensive care, patient-specific metabolic model parameters also have been used as sepsis biomarkers because they can accurately reflect the inflammatory status of the patient and severity of illness [ 31 , 32 ]. These studies showed that model-based insulin sensitivity alone could provide 70-80% sensitivity and specificity in assessing sepsis compared with a control cohort, yielding a negative predictive value (NPV) greater than 99%, thus clearly identifying periods where antibiotic therapy was not necessary.…”