Modelling Insulin Clearance and Degradation in Very Premature Infants. Issue 20 (2015)

• Record Type: Journal Article
• Authors: Dickson, J.; Pretty, C.; Fisk, L.; Alsweiler, J.; Chase, J.G
• Is Part Of: IFAC-PapersOnLine. Volume 48:Issue 20(2015)
• Page Start: 13
• Page End: 18
• Publication Date: 2015
• Subjects: Glycaemic control -- physiological modeling -- insulin clearance -- premature infants -- critical care; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2015.10.107
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Inability to maintain blood glucose concentrations (BG) to a normal range is a common complication of prematurity and stress in neonatal intensive care units (NICUs). STAR (Stochastic TARgeted) glycaemic control uses physiological models to capture and predict patient glycaemic behaviour, and thus dose insulin safely and effectively for tight glycaemic control. STAR in the NICU has in past been based on a simple NICU model. In this study, clinical data is used to identify and validate insulin kinetic parameters of the more physiologically descriptive NICING (Neonatal Intensive Care Insulin-Nutrition-Glucose) model. C-peptide, plasma insulin and BG from a cohort of 41 extremely pre-term (median age 27.2 [26.2 - 28.7] weeks) and very low birth-weight infants (median birth weight 839 [735 - 1000] g) was used alongside C-peptide kinetic models to identify model parameters associated with insulin kinetics in the NICING model. These kinetic parameters in the NICING model were validated by fitting the model to a cohort of 160 glucose, insulin, and nutrition data records from extremely premature infants from two different NICUs. The model fits data and predicts changes in BG in a manner similar to the previous NICU model, indicating that it will perform safely and effectively in the clinical setting. However, the NICING model is more physiologically descriptive, capturing more insulin kinetics and allowing consistency with existing adult ICU glycaemic control protocols and other diabetes models.