Development of blood glucose control for extremely premature infants

Abstract

Abstract:Extremely premature neonates often experience hyperglycaemia, which has been linked to increased mortality and worsened outcomes. Insulin therapy can assist in controlling blood glucose levels and promoting needed growth. This study presents the development of a model-based stochastic targeted controller designed to adapt insulin infusion rates to match the unique and changing metabolic state and control parameters of the neonate. Long-term usage of targeted BG control requires successfully forecasting variations in neonatal metabolic state, accounting for differences in clinical practices between units, and demonstrating robustness to errors that can occur in everyday clinical usage. Simulation studies were used to evaluate controller ability to target several common BG ranges and evaluate controller sensitivity to missed BG measurements and delays in control interventions on a virtual patient cohort of 25 infants developed from retrospective data. Initial clinical pilot trials indicated model performance matched expected performance from simulations. Stochastic targeted glucose control developed using validated patient-specific virtual trials can yield effective protocols for this cohort. Long-term trials show fundamental success, however clinical interface design appears as a critical factor to ensuring good compliance and thus good control.3

A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) model validated in critically ill patients

“…However, "equilibrium blood glucose level" G E is no longer present, and BG(t) is the absolute BG level per more recent works [55,30,48].…”

“…This model only requires measurements in blood glucose levels (BG), therefore it can be used by the bedside for clinical real-time identification and control. This structure has been widely used in clinical TGC studies and other analyses [30,40,37].…”

“…Thus, when there is a significant shift in this balance in a patient, for any number of reasons [36,44,47], G E often needs to be adjusted to capture the patient's (then) current clinical glucose-insulin dynamics. Hence, the term is non-physiological, unidentifiable and was ignored in later model evolutions [30,48,49].…”

“…Interestingly, some TGC studies that reported a mortality reduction also had reduced and relatively low hypoglycaemic rates [13,14], whereas almost all other reports had increased and often excessive hypoglycaemia [15,17]. Finally, model-based and model-derived TGC methods have shown the ability to provide very tight control with little or no hypoglycaemia [13,27,28,29,30].…”

Brain mass estimation by head circumference and body mass methods in neonatal glycaemic modelling and control

“…Next, changes in glycaemic control were analyzed using clinically-validated virtual patients [11,28]. In particular, both models for estimating brain mass were compared by fitting data to create virtual patients.…”