» Download Article

Authors
Pielmeier, Ulrike; Andreassen, Steen; Steenfeldt Nielsen, Birgitte; Hann, Christopher E; Chase, J. Geoffrey; Haure, Pernille

Identifier
10.3182/20090812-3-DK-2006.00012

Index Terms
Endocrine and metabolic systems; Control of physiological and clinical variables,; Critical care and decision support systems

Abstract
Models of glucose metabolism can help to simulate and predict the blood glucose response in hyperglycaemic, critically ill patients. Model prediction performance depends on a sufficiently accurate estimation of the patient's time-varying insulin sensitivity. The work presents three least squares approaches, the integral method and a Bayesian method that have been compared by prediction accuracy on an absolute and on a relative scale. Clinical data yields 1491 blood glucose predictions based on 10 critically ill patients. The Bayesian approach proved to be best with small errors (9.7% absolute percent error, 14.7 root mean square of logarithmic error for prediction times <= 2h), and fewer and smaller outliers compared to the other methods. Computationally, the Bayesian method took 1.5 times longer per prediction compared to the fastest method. It can be concluded that a Bayesian parameter estimation gives safe and effective results for the insulin sensitivity estimation for this model.

References

{\rtf1\ansi\ansicpg1252\deff0\deflang1030{\fonttbl{\f0\ftech\fcharset0 CMR10;}{\f1\ftech\fcharset0 CMTI10;}{\f2\fswiss\fcharset0 Arial;}}
{\*\generator Msftedit 5.41.21.2508;}\viewkind4\uc1\pard\f0\fs20 Balkin, M., Mascioli, C., Smith, V., Alnachawati, H.,\par
Mehrishi, S., Saydain, G., Slone, H., Alessandrini, J.,\par
and Brown, L. (2007). Achieving durable glucose control\par
in the intensive care unit without hypoglycaemia : a new\par
practical IV insulin protocol. \f1 Diabetes Metab Res Rev\f0 ,\par
23, 49-55.\par
\par
Chase, J.G., LeCompte, A., Shaw, G., Blakemore, A.,\par
Wong, J., Lin, J., and Hann, C.E. (2008). A Benchmark\par
Data Set for Model-Based Glycemic Control in Critical\par
Care. \f1 J Diabetes Sci Technol\f0 , 2(4), 584-594.\par
\par
Cryer, P.E. (2006). Hypoglycaemia: the limiting factor in\par
the glycaemic management of the critically ill? \f1 Dia-\par
betologia\f0 , 49, 1722-1725.\par
\par
Devos, P., and Preiser, J.C. (2007). Current controversies\par
around tight glucose control in critically ill patients.\par
\f1 Curr Opin Clin Nutr Metab Care\f0 , 10, 206-209.\par
\par
Hann, C.E., Chase, J.G., Lin, J., Lotz, T., Doran, C.V.,\par
Shaw, G.M. (2005). Integral-based parameter identifi-\par
cation for long-term dynamic verification of a glucose-\par
insulin system model. \f1 Comp Meth Progr Biomed.\f0 , 77(3),\par
259-270.\par
\par
Hovorka, R., Chassin, L.J., Ellmerer, M., Plank, J., Wil-\par
inska, M.E. (2008). A simulation model of glucose\par
regulation in the critically ill. \f1 Physiol.Meas.\f0 , 29, 959-978.\par
\par
Krinsley, J.S. (2004). Effect of an intensive glucose man-\par
agement protocol on the mortality of critically ill adult\par
patients. \f1 Mayo Clin Proc\f0 , 79, 992-1000.\par
\par
McCowen, K.C., Malhotra, A., and Bistrian, B.R. (2001).\par
Stress-induced hyperglycemia. \f1 Crit Care Clin\f0 , 17(1),\par
107-124.\par
\par
Meijering, S., Corstjens, A.M., Tulleken, J.E., Meertens,\par
J.H.J.M., Zijlstra, J.G., and Ligtenberg, J.J.M. (2006).\par
Towards a feasible algorithm for tight glycaemic control\par
in critically ill patients: a systematic review of the\par
literature. \f1 Crit Care\f0 , 10, R19 (doi:10.1186/cc3981).\par
\par
Mizock, B.A. (1995). Alterations in carbohydrate\par
metabolism during stress: a review of the literature. \f1 Am\par
J Med\f0 , 98, 75-84.\par
\par
Pielmeier, U., Andreassen, S., Nielsen, B.S., Chase, J.G.,\par
Haure, P. (2009). A simulation model of insulin satu-\par
ration and glucose balance for glycemic control in ICU\par
patients. \f1 Comp Meth Progr Biomed.\f0 , in press.\par
\par
Plank, J., Blaha, J., Cordingley, J., Wilinska, M.E., Chas-\par
sin, et al. (2006). Multicentric, randomized, controlled\par
trial to evaluate blood glucose control by the model pre-\par
dictive control algorithm versus routine glucose manage-\par
ment protocols in intensive care unit patients. \f1 Diabetes\par
Care\f0 , 29(2), 271-276.\par
\par
Shulman, R., Finney, S.J., O'Sullivan, C., Glynne, P.A.,\par
and Greene, R. (2007). Tight glycaemic control:\par
a prospective observational study of a computerized\par
decision-supported intensive insulin therapy protocol.\par
\f1 Crit Care\f0 , 11, R75.\par
\par
Van den Berghe, G., Wouters, P., Weekers, F., Verwaest,\par
C., Bruyninckx, F., Schetz, M., Vlasselaers, D., Ferdi-\par
nande, P., Lauwers, P., Bouillon, R. (2001). Intensive\par
insulin treatment in critically ill patients. \f1 N Engl J Med\f0 ,\par
345, 1359-1367.\par
\par
Van den Berghe, G., Wilmer, A., Hermans, G., Meersse-\par
man, W., Wouters, P., Milants, I., Van Wijngaerden,\par
E., Bobbaers, H., Bouillon, R. (2006). Intensive insulin\par
therapy in the medical ICU. \f1 N Engl J Med\f0 , 354, 449-\par
461.\par
\par
Vogelzang, M., Zijlstra, F., and Nijsten, M.W. (2005).\par
Design and implementation of GRIP: a computerized\par
glucose control system at a surgical intensive care unit.\par
\f1 BMC Med. Inform. Decis. Mak.\f0 , 5, 38.\par
\par
Wong, X.W., Singh-Lewett, I., Hollingsworth, L.J., Shaw,\par
G.M., Hann, C.E., Lotz, T., Lin, J., Wong, O.S., and\par
Chase, J.G. (2006). A novel model-based insulin and\par
nutrition delivery controller for glycaemic regulation in\par
critically ill patients. \f1 Diab Technol Ther\f0 , 8, 174-190.\par
\par
\f2\par
}