(494f) A Mechanistic Model of Stress Hyperglycemia Evolution in the Intensive Care Unit | AIChE

(494f) A Mechanistic Model of Stress Hyperglycemia Evolution in the Intensive Care Unit

Authors 

Pritchard-Bell, A. - Presenter, University of Pittsburgh
Knab, T. - Presenter, University of Pittsburgh
Clermont, G. - Presenter, University of Pittsburgh
Parker, R. - Presenter, University of Pittsburgh

Many patients in the intensive care unit (ICU) experience
elevated blood glucose levels, so-called stress hyperglycemia. It has been
shown that controlling glucose levels using intensive insulin therapy (IIT) can
reduce mortality [1, 2]. However, a prospective multi-center study [3] of over
6,000 patients showed increased mortality for those receiving IIT. Retrospective
analysis [4] shows that IIT caused increased occurrence of hypoglycemia, which
consequently reduced survival within the IIT cohort. The conclusion drawn from
the aforementioned studies is the existence of a glucose range that, when
patients are controlled therein, yields lower mortality, approximately 80 to
120 mg/dL. A model-based decision support system (DSS) can provide
patient-specific treatment and improve blood glucose control to range without
increasing risk of hypoglycemia. The critical component of a patient-tailored
DSS is a mathematical model that can resolve the dynamic changes resulting in a
unique individuals metabolic state.

The underlying mechanism of stress hyperglycemia is a
complex network of biological signaling pathways that decrease sensitivity to
insulin [5] and increase endogenous glucose production (EGP). In this work,
mathematical modeling is used to identify and characterize the complex
biological pathways leading to stress hyperglycemia. The two modeled metabolic
regulatory processes involved in dynamic modification of metabolism in the ICU
are: (i) the hypoglycemia counterregulatory response and (ii) the acute
inflammatory response. The counterregulatory response occurs following a
hypoglycemic event, common in the ICU [4], and increases blood glucose via
increased EGP and insulin resistance leading to reduced insulin-mediated
glucose uptake (IMGU). Similarly, the acute inflammatory response includes
cytokines such as TNF-α and hormones such as cortisol, which also decrease
IMGU in humans [7, 8]. These two metabolic regulatory pathways are coupled with
an model of glucose and insulin homeostasis [6] from literature to resolve
patient specific variations in metabolic state.

Patient glucose measurements are fit by adjusting a single,
time-varying insulin sensitivity parameter. The time-varying parameter profile
used to match model glucose values with recorded glucose values for each
patient is subsequently used to predict explanatory biomarker concentrations
belonging to the two metabolic regulatory pathways previously described.
Specifically, TNF-α, cortisol, epinephrine, and glucagon dynamics are
simulated to match the insulin sensitivity profile for each patient.
Mathematical models of acute inflammation and counterregulation are used as
explanatory mechanisms driving insulin sensitivity, which enforce pathway
specific dynamics on overall glucose dynamics. Both pathways are evaluated to
determine whether they act individually or concurrently to produce observed
insulin resistance and stress hyperglycemia in ICU patients.

This model of glucose and insulin, combined with mechanistic
counterregulatory and inflammatory dynamics, serves as a simulation platform to
generate clinically-relevant critically ill patient metabolic profiles. A
library of time-varying metabolic parameter profiles is fit to a subset of the
215 trauma victims from two hospital centers in the Cologne area participating
in the German Trauma Registry effort, to provide a virtual patient cohort
matching the dynamics of clinical response. Such a virtual patient platform is
useful for developing DSS control strategies, as well as to better understand
possible patient differentiation metrics for separating treatment cohorts (e.g., counterregulation driven,
inflammation driven, or both) for which treatment strategies may differ as a
result of their metabolic upset.

References

1.
Van den Berghe G, Wouters P, Weekers F, Verwaest C, Bruyninckx F, et al. (2001)
Intensive insulin therapy in critically ill patients. New England journal of
medicine 345: 1359-1367.

2.
Krinsley JS (2004) Effect of an intensive glucose management protocol on the
mortality of critically ill adult patients. Mayo Clinic Proceedings. Elsevier,
volume 79, pp. 992-1000.

3.
Finfer S (2009) Intensive versus conventional glucose control in critically ill
patients. New England Journal of Medicine 360: 1283-1297.

4.
Finfer S (2012) Hypoglycemia and risk of death in critically ill patients. New
England Journal of Medicine 367: 1108-1118.

5.
Brealey D, Singer M (2009) Hyperglycemia in critical illness: A review. Journal
of Diabetes Science and Technology 3: 1250-1260.

6.
Lin J, Razak NN, Pretty CG, Le Compte A, Docherty P, et al. (2011) A physiological
intensive control insulin-nutrition-glucose (ICING) model validated in
critically ill patients. Computer Methods and Programs in Biomedicine 102:
192-205.

7.
Plomgaard P, Bouzakri K, Krogh-Madsen R, Mittendorfer B, Zierath JR, et al.
(2005) Tumor necrosis factor-α induces
skeletal muscle insulin resistance in healthy human subjects via inhibition of
akt substrate 160 phosphorylation. Diabetes 54: 2939-2945.

8.
Rizza RA, Mandarino LJ, Gerich JE (1982) Cortisol-induced insulin resistance in
man: Impaired suppression of glucose production and stimulation of glucose
utilization due to a postreceptor defect of insulin action. The Journal of
Clinical Endocrinology & Metabolism 54: 131-138.