Adaptive blood glucose control for intensive care applications

• Published in: Computer methods and programs in biomedicine Vol. 109; no. 2; pp. 144 - 156
• Main Authors: Ottavian, Matteo, Barolo, Massimiliano, Zisser, Howard, Dassau, Eyal, Seborg, Dale E.
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier Ireland Ltd 01.02.2013
• Subjects: Adaptive control; Blood Glucose - analysis; Blood Glucose - metabolism; Computer Simulation; Critical Care; Critical Illness - therapy; Humans; Hypoglycemic Agents - administration & dosage; ICU; Insulin - administration & dosage; Insulin Infusion Systems; Insulin Resistance; Insulin sensitivity; Internal Medicine; On-line tuning; Other; IIP; BG; Insulin sensitivity; ARX; RLS; ICU; PID; IAE; On-line tuning; IV; Adaptive control; IMC; blood glucose; autoregressive with exogenous input; integral absolute error; recursive least-squares; insulin infusion protocol; intravenous; proportional-integral-derivative; intensive care unit; internal model control
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2012.01.011

► A two-step glucose control strategy for intensive care unit (ICU) applications is proposed. ► During the initial stay (3h) in the ICU, blood glucose is controlled through a standard intensive care protocol, and data on blood glucose concentration and insulin infusion rate are used to recursively identify a model of the subject. ► A PID controller is tuned from the identified model, and it is switched on to automate blood glucose control. ► The controller settings are continuously adapted as new data become available. ► The control strategy performance is assessed on 200 simulated subjects, each one characterized by a unique insulin sensitivity profile. Control of blood glucose concentration for patients in intensive care units (ICUs) has been demonstrated to be beneficial in reducing mortality and the incidence of serious complications, for both diabetic and non-diabetic patients. However, the high degree of variability and uncertainty characterizing the physiological conditions of critically ill subjects makes automated glucose control quite difficult; consequently, traditional, nurse-implemented protocols are widely employed. These protocols are based on infrequent glucose measurements, look-up tables to determine the appropriate insulin infusion rates, and bedside insulin administration. In this paper, a novel automatic adaptive control strategy based on frequent glucose measurements and a self-tuning control technique is validated based on a simulation study for 200 virtual patients. The adaptive control strategy is shown to be highly effective in controlling blood glucose concentration despite the large degree of variability in the blood glucose response exhibited by the 200 simulated patients.