Model-based targeted control with stochastic forecasting for regulation of glycemia in ELBW neonates

Abstract

Hyperglycemia occurs in 40-80% of very premature, low birthweight infants. The pathogenesis of hyperglycemia in critically ill adults and preterm infants may differ. The mechanisms responsible for hyperglycemia in preterm infants are related to immaturity of the glucose regulatory system, in addition to clinical stress. This condition has been linked to worsened outcomes, including increased incidence of sepsis, increased ventilator dependence, retinopathy of prematurity, hospital length of stay and mortality. Often, glucose restriction is used to control high blood glucose levels, but this can deprive the neonate of crucial energy required to promote growth. Continuous insulin infusion has thus been proposed as a solution to reduce plasma glucose concentrations and optimize nutrition in these small infants. However, great heterogeneity is the hallmark of neonatal glucose metabolism. Thus, the emerging use of insulin carries a significant risk of hypoglycemia due to patient response variations to insulin over time. An adaptive model of the fundamental glucose regulatory dynamics in neonates can track an infant’s sensitivity to exogenous insulin in realtime. Targeted control forecasts the range of likely future glucose levels to select the optimal insulin rate, adapting control to the infant’s current metabolic state.